From 10f974ee8108fae7a92de5b7abaae3299c1a8a77 Mon Sep 17 00:00:00 2001
From: karin0018 <karin0018@users.noreply.github.com>
Date: Fri, 6 Aug 2021 21:43:29 +0800
Subject: [PATCH 001/149] add examples

---
 examples/Tokenizer/tokenizer.ipynb | 169 +++++++
 examples/i2v/i2v.py                |  13 +
 examples/sif/sif_additon.ipynb     | 733 +++++++++++++++++++++++++++++
 examples/t2v/t2v.py                |  34 ++
 examples/utils/data.ipynb          | 192 ++++++++
 5 files changed, 1141 insertions(+)
 create mode 100644 examples/Tokenizer/tokenizer.ipynb
 create mode 100644 examples/i2v/i2v.py
 create mode 100644 examples/sif/sif_additon.ipynb
 create mode 100644 examples/t2v/t2v.py
 create mode 100644 examples/utils/data.ipynb

diff --git a/examples/Tokenizer/tokenizer.ipynb b/examples/Tokenizer/tokenizer.ipynb
new file mode 100644
index 00000000..87c9f7f8
--- /dev/null
+++ b/examples/Tokenizer/tokenizer.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "from EduNLP.Tokenizer import TextTokenizer\n",
+    "\n",
+    "# 法一：直接调用类\n",
+    "items = [\"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\"]\n",
+    "tokenizer = TextTokenizer()\n",
+    "tokens = tokenizer(items)\n",
+    "next(tokens)  # doctest: +NORMALIZE_WHITESPACE"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['已知',\n",
+       " '集合',\n",
+       " 'A',\n",
+       " '=',\n",
+       " '\\\\left',\n",
+       " '\\\\{',\n",
+       " 'x',\n",
+       " '\\\\mid',\n",
+       " 'x',\n",
+       " '^',\n",
+       " '{',\n",
+       " '2',\n",
+       " '}',\n",
+       " '-',\n",
+       " '3',\n",
+       " 'x',\n",
+       " '-',\n",
+       " '4',\n",
+       " '<',\n",
+       " '0',\n",
+       " '\\\\right',\n",
+       " '\\\\}',\n",
+       " ',',\n",
+       " '\\\\quad',\n",
+       " 'B',\n",
+       " '=',\n",
+       " '\\\\{',\n",
+       " '-',\n",
+       " '4',\n",
+       " ',',\n",
+       " '1',\n",
+       " ',',\n",
+       " '3',\n",
+       " ',',\n",
+       " '5',\n",
+       " '\\\\}',\n",
+       " ',',\n",
+       " '\\\\quad',\n",
+       " 'A',\n",
+       " '\\\\cap',\n",
+       " 'B',\n",
+       " '=']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "from EduNLP.Tokenizer import get_tokenizer\n",
+    "\n",
+    "# 法二：导入内部函数 get_tokenizer\n",
+    "tokenizer_2 = get_tokenizer(\"text\")\n",
+    "tokens_2 = tokenizer_2(items)\n",
+    "next(tokens_2) "
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['已知',\n",
+       " '集合',\n",
+       " 'A',\n",
+       " '=',\n",
+       " '\\\\left',\n",
+       " '\\\\{',\n",
+       " 'x',\n",
+       " '\\\\mid',\n",
+       " 'x',\n",
+       " '^',\n",
+       " '{',\n",
+       " '2',\n",
+       " '}',\n",
+       " '-',\n",
+       " '3',\n",
+       " 'x',\n",
+       " '-',\n",
+       " '4',\n",
+       " '<',\n",
+       " '0',\n",
+       " '\\\\right',\n",
+       " '\\\\}',\n",
+       " ',',\n",
+       " '\\\\quad',\n",
+       " 'B',\n",
+       " '=',\n",
+       " '\\\\{',\n",
+       " '-',\n",
+       " '4',\n",
+       " ',',\n",
+       " '1',\n",
+       " ',',\n",
+       " '3',\n",
+       " ',',\n",
+       " '5',\n",
+       " '\\\\}',\n",
+       " ',',\n",
+       " '\\\\quad',\n",
+       " 'A',\n",
+       " '\\\\cap',\n",
+       " 'B',\n",
+       " '=']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/i2v/i2v.py b/examples/i2v/i2v.py
new file mode 100644
index 00000000..33f62d64
--- /dev/null
+++ b/examples/i2v/i2v.py
@@ -0,0 +1,13 @@
+from EduNLP import get_pretrained_i2v
+from EduNLP.Vector.t2v import PRETRAINED_MODELS
+from EduNLP.I2V.i2v import MODELS
+from EduNLP.I2V import D2V
+from pathlib import PurePath
+
+
+item = {
+  "stem": r"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$",
+  "options": ["$p_1=p_2$", "$p_1=p_3$", "$p_2=p_3$", "$p_1=p_2+p_3$"]
+}
+i2v = get_pretrained_i2v("d2v_eng_256")
+print(i2v(item))
diff --git a/examples/sif/sif_additon.ipynb b/examples/sif/sif_additon.ipynb
new file mode 100644
index 00000000..db6de0b6
--- /dev/null
+++ b/examples/sif/sif_additon.ipynb
@@ -0,0 +1,733 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "from EduNLP.SIF import is_sif, to_sif,sif4sci"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## is_sif"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    " text = '若$x,y$满足约束条件' \\\n",
+    "     '$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，' \\\n",
+    "    '则$z=x+7 y$的最大值$\\\\SIFUnderline$'\n",
+    "    \n",
+    "is_sif(text)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "is_sif(text)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## to_sif"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "to_sif(text)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## sif4sci\n",
+    " to_symbolize:\n",
+    " - \"t\": text\n",
+    " - \"f\": formula\n",
+    " - \"g\": figure\n",
+    " - \"m\": question mark"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "source": [
+    " test_item = r\"如图所示，则$\\bigtriangleup ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$\"\n",
+    " t1 = sif4sci(test_item)\n",
+    " t1"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图所示', '\\\\bigtriangleup', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "source": [
+    "t1.describe()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'t': 2, 'f': 2, 'g': 1, 'm': 1}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "with t1.filter('fgm'):\n",
+    "    print(t1)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['如图所示', '面积']\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "with t1.filter(keep='t'):\n",
+    "    print(t1)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['如图所示', '面积']\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "with t1.filter():\n",
+    "    print(t1)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['如图所示', '\\\\bigtriangleup', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "t1.text_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图所示', '面积']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "t1.formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\bigtriangleup', 'ABC']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "t1.figure_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[\\FigureID{1}]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "t1.ques_mark_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\SIFBlank']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\"}})"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图所示', <Formula: \\bigtriangleup ABC>, '面积', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "sif4sci(test_item, symbol=\"tfgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图所示', '\\\\bigtriangleup', 'A', 'B', 'C', '面积', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    " test_item_1 = {\n",
+    "     \"stem\": r\"若$x=2$, $y=\\sqrt{x}$，则下列说法正确的是$\\SIFChoice$\",\n",
+    "     \"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]\n",
+    "  }"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    " tls = [\n",
+    "         sif4sci(e, symbol=\"gm\",\n",
+    "         tokenization_params={\n",
+    "            \"formula_params\": {\n",
+    "                 \"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True, \"var_numbering\": True,\n",
+    "                 \"link_variable\": False}\n",
+    "         })\n",
+    "         for e in ([test_item_1[\"stem\"]] + test_item_1[\"options\"])\n",
+    "     ]"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "tls"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['mathord_0', '=', 'textord', 'mathord_1', '=', 'mathord_0', '{ }', '\\\\sqrt', '说法', '正确', '[MARK]'],\n",
+       " ['mathord_0', '<', 'mathord_1'],\n",
+       " ['mathord_0', '=', 'mathord_1'],\n",
+       " ['mathord_0', '<', 'mathord_1']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "tls[1:]"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['mathord_0', '<', 'mathord_1'],\n",
+       " ['mathord_0', '=', 'mathord_1'],\n",
+       " ['mathord_0', '<', 'mathord_1']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "source": [
+    "from EduNLP.utils import dict2str4sif\n",
+    "\n",
+    "test_item_1_str = dict2str4sif(test_item_1, tag_mode=\"head\", add_list_no_tag=False)\n",
+    "test_item_1_str  # doctest: +ELLIPSIS"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'$\\\\SIFTag{stem}$若$x=2$, $y=\\\\sqrt{x}$，则下列说法正确的是$\\\\SIFChoice$$\\\\SIFTag{options}$$x < y$$\\\\SIFSep$$y = x$$\\\\SIFSep$$y < x$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "source": [
+    "tl1 = sif4sci(\n",
+    "    test_item_1_str, \n",
+    "    symbol=\"gm\", \n",
+    "    tokenization_params={\n",
+    "        \"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True}\n",
+    "    })\n",
+    "    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "source": [
+    "tl1.get_segments()[0]"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\SIFTag{stem}']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "source": [
+    "tl1.get_segments()[1:3]"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['[TEXT_BEGIN]', '[TEXT_END]'],\n",
+       " ['[FORMULA_BEGIN]', 'mathord', '=', 'textord', '[FORMULA_END]']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "source": [
+    "tl1.get_segments(add_seg_type=False)[0:3]"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['\\\\SIFTag{stem}'],\n",
+       " ['mathord', '=', 'textord'],\n",
+       " ['mathord', '=', 'mathord', '{ }', '\\\\sqrt']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "source": [
+    "test_item_2 = {\"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]}"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "source": [
+    "test_item_2_str = dict2str4sif(test_item_2, tag_mode=\"head\", add_list_no_tag=False)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "source": [
+    "test_item_2_str"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'$\\\\SIFTag{options}$$x < y$$\\\\SIFSep$$y = x$$\\\\SIFSep$$y < x$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "source": [
+    "tl2 = sif4sci(test_item_2_str, symbol=\"gms\",\n",
+    "     tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})\n",
+    "tl2 "
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\SIFTag{options}', 'x', '<', 'y', '[SEP]', 'y', '=', 'x', '[SEP]', 'y', '<', 'x']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "source": [
+    "tl2.get_segments(add_seg_type=False)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['\\\\SIFTag{options}'],\n",
+       " ['x', '<', 'y'],\n",
+       " ['[SEP]'],\n",
+       " ['y', '=', 'x'],\n",
+       " ['[SEP]'],\n",
+       " ['y', '<', 'x']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "source": [
+    "tl2.get_segments(add_seg_type=False, drop=\"s\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['\\\\SIFTag{options}'], ['x', '<', 'y'], ['y', '=', 'x'], ['y', '<', 'x']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "source": [
+    "tl3 = sif4sci(test_item_1[\"stem\"], symbol=\"gs\")\n",
+    "tl3.text_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['说法', '正确']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "source": [
+    "tl3.formula_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['x', '=', '2'], ['y', '=', '\\\\sqrt', '{', 'x', '}']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "source": [
+    "tl3.figure_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "source": [
+    "tl3.ques_mark_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['\\\\SIFChoice']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/t2v/t2v.py b/examples/t2v/t2v.py
new file mode 100644
index 00000000..279bbcc9
--- /dev/null
+++ b/examples/t2v/t2v.py
@@ -0,0 +1,34 @@
+from EduNLP.Vector import get_pretrained_t2v
+import json
+from tqdm import tqdm
+from EduNLP.SIF.segment import seg
+from EduNLP.SIF.tokenization import tokenize
+from EduNLP.Pretrain import GensimWordTokenizer
+
+def load_items():
+    test_items = [
+        {'ques_content':'有公式$\\FormFigureID{wrong1?}$和公式$\\FormFigureBase64{wrong2?}$，如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'},
+        {'ques_content':'如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'},
+        {'ques_content':'<div>Below is a discussion on a website.<br><table border=\1'},
+    ]
+    for line in test_items:
+        yield line
+    # """or use your jsonfile like this"""
+    # work_file_path = "../../../data/OpenLUNA.json"
+    # with open(work_file_path, 'r', encoding="utf-8") as f:
+    #     for line in f:
+    #         yield json.loads(line)
+
+
+token_items = []
+for item in tqdm(load_items(), "sifing"):
+    # transform content into special marks('g','m','a','s'), except text('t') and formula('f').
+    # 'general' means symbolize the Formula in figure format and use 'linear' method for formula segmentation
+    tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+    token_item = tokenizer(item["ques_content"])
+    if token_item:
+        token_items.append(token_item.tokens)
+
+# make a model -> t2v
+t2v = get_pretrained_t2v("d2v_eng_256")
+print(t2v(token_items))
\ No newline at end of file
diff --git a/examples/utils/data.ipynb b/examples/utils/data.ipynb
new file mode 100644
index 00000000..536e4aeb
--- /dev/null
+++ b/examples/utils/data.ipynb
@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "from EduNLP.utils import dict2str4sif"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "item = {\n",
+    "    \"stem\": r\"若复数$z=1+2 i+i^{3}$，则$|z|=$\",\n",
+    "    \"options\": ['0', '1', r'$\\sqrt{2}$', '2'],\n",
+    "    }\n",
+    "item"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'stem': '若复数$z=1+2 i+i^{3}$，则$|z|=$',\n",
+       " 'options': ['0', '1', '$\\\\sqrt{2}$', '2']}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# 给题目各个部分加标签\n",
+    "dict2str4sif(item) # doctest: +ELLIPSIS"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options_end}$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options_end}$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "dict2str4sif(item, tag_mode=\"head\") # doctest: +ELLIPSIS"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'$\\\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{options}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "dict2str4sif(item, tag_mode=\"tail\") # doctest: +ELLIPSIS"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options}$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$0$\\\\SIFSep$1$\\\\SIFSep$$\\\\sqrt{2}$$\\\\SIFSep$2$\\\\SIFTag{options_end}$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "dict2str4sif(item, key_as_tag=False)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\\\SIFSep$1$\\\\SIFSep$$\\\\sqrt{2}$$\\\\SIFSep$2'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file

From 265a338024e499e61fe296432813e46b2a27f129 Mon Sep 17 00:00:00 2001
From: karin0018 <karin0018@users.noreply.github.com>
Date: Wed, 11 Aug 2021 14:37:03 +0800
Subject: [PATCH 002/149] [examples] update formula,i2v,t2v,Tokenizer

---
 examples/Tokenizer/tokenizer.ipynb            | 268 ++++++----
 examples/formula/formula.ipynb                | 472 ++++++++++++++----
 examples/formula/tree.ipynb                   |  54 +-
 examples/i2v/get_pretrained_i2v.ipynb         | 212 ++++++++
 examples/i2v/i2v.ipynb                        | 201 ++++++++
 examples/i2v/i2v.py                           |  13 -
 examples/sif/sif_additon.ipynb                |   2 +-
 examples/t2v/get_pretrained_t2v.ipynb         | 273 ++++++++++
 examples/t2v/t2v.ipynb                        | 261 ++++++++++
 examples/t2v/t2v.py                           |  34 --
 .../test_gensim_luna_stem_tf_d2v_256.bin      | Bin 0 -> 5376077 bytes
 11 files changed, 1528 insertions(+), 262 deletions(-)
 create mode 100644 examples/i2v/get_pretrained_i2v.ipynb
 create mode 100644 examples/i2v/i2v.ipynb
 delete mode 100644 examples/i2v/i2v.py
 create mode 100644 examples/t2v/get_pretrained_t2v.ipynb
 create mode 100644 examples/t2v/t2v.ipynb
 delete mode 100644 examples/t2v/t2v.py
 create mode 100644 examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin

diff --git a/examples/Tokenizer/tokenizer.ipynb b/examples/Tokenizer/tokenizer.ipynb
index 87c9f7f8..d21d7408 100644
--- a/examples/Tokenizer/tokenizer.ipynb
+++ b/examples/Tokenizer/tokenizer.ipynb
@@ -1,142 +1,204 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Tokenizer\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目文本转换成令牌序列。按照试题文本类型分为以下四部分：   \n",
+    "\n",
+    "1. 分句（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
+    "  \n",
+    "\n",
+    "\n",
+    "2. 标记解析（text-tokenization）：一个句子（不含公式）是由若干“标记”按顺序构成的，将一个句子切分为若干标记的过程称为“标记解析”。每个标记为一个“令牌”（token）。    \n",
+    "\n",
+    "3. 公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \n",
+    "  \n",
+    "4. 综合解析（item-tokenization）：将带公式的句子切分为若干标记的过程。每个标记为一个“令牌”（token）。    \n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 标记解析\n",
+    "\n",
+    "先使用 `jieba` 分词工具得到分词序列，再过滤掉停用词 -  [stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 36,
    "source": [
-    "from EduNLP.Tokenizer import TextTokenizer\n",
+    "# 导入模块\n",
+    "from EduNLP.SIF.tokenization.text import tokenize \n",
     "\n",
-    "# 法一：直接调用类\n",
-    "items = [\"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\"]\n",
-    "tokenizer = TextTokenizer()\n",
-    "tokens = tokenizer(items)\n",
-    "next(tokens)  # doctest: +NORMALIZE_WHITESPACE"
+    "# 输入\n",
+    "text = \"三角函数是基本初等函数之一\"\n",
+    "# 输出\n",
+    "print(tokenize(item))"
    ],
    "outputs": [
     {
      "output_type": "stream",
-     "name": "stderr",
+     "name": "stdout",
      "text": [
-      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
+      "['三角函数', '初等', '函数']\n"
      ]
-    },
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 公式解析\n",
+    "切分出 latex 公式的每个标记符号。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "source": [
+    "# 导入模块\n",
+    "from EduNLP.SIF.tokenization.formula import tokenize\n",
+    "\n",
+    "# 输入\n",
+    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\"\n",
+    "\n",
+    "# 输出\n",
+    "\n",
+    "# 输出形式选择普通序列（linear）\n",
+    "print('linear: ',tokenize(formula,method=\"linear\"))\n",
+    "\n",
+    "# 输出形式选择抽象语法分析树（ast）\n",
+    "print('ast : ',tokenize(formula,method=\"ast\",return_type = \"list\", ord2token=False))\n",
+    "\n",
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token \n",
+    "print('ast & ord2token: ',tokenize(formula,method=\"ast\",return_type = \"list\", ord2token=True))\n",
+    "\n",
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\n",
+    "print('ast & ord2token & var_numbering: ',tokenize(formula,method=\"ast\",return_type = \"list\", ord2token=True, var_numbering=True))\n",
+    "\n"
+   ],
+   "outputs": [
     {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['已知',\n",
-       " '集合',\n",
-       " 'A',\n",
-       " '=',\n",
-       " '\\\\left',\n",
-       " '\\\\{',\n",
-       " 'x',\n",
-       " '\\\\mid',\n",
-       " 'x',\n",
-       " '^',\n",
-       " '{',\n",
-       " '2',\n",
-       " '}',\n",
-       " '-',\n",
-       " '3',\n",
-       " 'x',\n",
-       " '-',\n",
-       " '4',\n",
-       " '<',\n",
-       " '0',\n",
-       " '\\\\right',\n",
-       " '\\\\}',\n",
-       " ',',\n",
-       " '\\\\quad',\n",
-       " 'B',\n",
-       " '=',\n",
-       " '\\\\{',\n",
-       " '-',\n",
-       " '4',\n",
-       " ',',\n",
-       " '1',\n",
-       " ',',\n",
-       " '3',\n",
-       " ',',\n",
-       " '5',\n",
-       " '\\\\}',\n",
-       " ',',\n",
-       " '\\\\quad',\n",
-       " 'A',\n",
-       " '\\\\cap',\n",
-       " 'B',\n",
-       " '=']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 1
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "linear:  ['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']\n",
+      "ast :  ['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']\n",
+      "ast & ord2token:  ['mathord', '{ }', 'mathord', '+', 'mathord', '{ }', '\\\\frac', '+', 'textord', '=', 'mathord']\n",
+      "ast & ord2token & var_numbering:  ['mathord_con', '{ }', 'mathord_0', '+', 'mathord_1', '{ }', '\\\\frac', '+', 'textord', '=', 'mathord_0']\n"
+     ]
     }
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 综合解析\n",
+    "\n",
+    "标记解析 + 公式解析。特殊符号将转换成常量，例如：\n",
+    "```python\n",
+    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\n",
+    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\n",
+    "```\n"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 39,
    "source": [
+    "# 导入模块\n",
     "from EduNLP.Tokenizer import get_tokenizer\n",
     "\n",
-    "# 法二：导入内部函数 get_tokenizer\n",
-    "tokenizer_2 = get_tokenizer(\"text\")\n",
-    "tokens_2 = tokenizer_2(items)\n",
-    "next(tokens_2) "
+    "# 输入\n",
+    "item = {\n",
+    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
+    "}\n",
+    "\n",
+    "# 输出\n",
+    "tokenizer = get_tokenizer(\"text\")\n",
+    "tokens = tokenizer(item)\n",
+    "next(tokens) "
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "['已知',\n",
-       " '集合',\n",
-       " 'A',\n",
-       " '=',\n",
-       " '\\\\left',\n",
-       " '\\\\{',\n",
-       " 'x',\n",
-       " '\\\\mid',\n",
-       " 'x',\n",
-       " '^',\n",
-       " '{',\n",
-       " '2',\n",
-       " '}',\n",
-       " '-',\n",
-       " '3',\n",
-       " 'x',\n",
-       " '-',\n",
-       " '4',\n",
-       " '<',\n",
-       " '0',\n",
-       " '\\\\right',\n",
-       " '\\\\}',\n",
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " 'ABC',\n",
+       " '斜边',\n",
+       " 'BC',\n",
+       " '直角',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\x08',\n",
+       " 'igtriangleupABC',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " 'I',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " 'II',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " 'III',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " 'I',\n",
        " ',',\n",
-       " '\\\\quad',\n",
-       " 'B',\n",
-       " '=',\n",
-       " '\\\\{',\n",
-       " '-',\n",
-       " '4',\n",
+       " 'II',\n",
        " ',',\n",
+       " 'III',\n",
+       " '概率',\n",
+       " '记',\n",
+       " 'p',\n",
+       " '_',\n",
        " '1',\n",
        " ',',\n",
-       " '3',\n",
-       " ',',\n",
-       " '5',\n",
-       " '\\\\}',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
        " ',',\n",
-       " '\\\\quad',\n",
-       " 'A',\n",
-       " '\\\\cap',\n",
-       " 'B',\n",
-       " '=']"
+       " 'p',\n",
+       " '_',\n",
+       " '3',\n",
+       " '[MARK]',\n",
+       " '[FIGURE]']"
       ]
      },
      "metadata": {},
-     "execution_count": 2
+     "execution_count": 39
     }
    ],
    "metadata": {}
diff --git a/examples/formula/formula.ipynb b/examples/formula/formula.ipynb
index 2ee49390..794ed7da 100644
--- a/examples/formula/formula.ipynb
+++ b/examples/formula/formula.ipynb
@@ -1,69 +1,177 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Formula\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "Formula 首先在分词功能中（SIF/segment ==> SIF/tokenization）中对原始文本的公式做切分处理，之后在 Formula/ast 中提供 [公式解析树] 功能，可以将数学公式的抽象语法分析树用文本或图片的形式表示出来。  \n",
+    "\n",
+    "本模块另提供公式变量标准化的功能，如判断几个子公式内的‘x’为同一变量。"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from EduNLP.Formula import Formula\n",
+    "from EduNLP.Formula import FormulaGroup\n",
+    "from EduNLP.Formula.viz import ForestPlotter"
+   ],
    "outputs": [
     {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n",
+      "/home/lvrui/.local/lib/python3.8/site-packages/EduNLP/Formula/viz/__init__.py:5: UserWarning: Do not use this package\n",
+      "  warnings.warn(\"Do not use this package\")\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 公式语法结构分析\n",
+    "\n",
+    "### 初始化实例\n",
+    "\n",
+    "- item 类型：`str or List[Dict]` \n",
+    "- item 内容：latex 公式 或 公式经解析后产生的抽象语法分析树(abstracted syntax tree)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "f = Formula(\"x^2 + x+1 = y\")\n",
+    "f "
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "[{'structure': {'bro': [None, 3],\n                'child': [1, 2],\n                'father': None,\n                'forest': None},\n  'val': {'id': 0, 'role': None, 'text': '^', 'type': 'supsub'}},\n {'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None},\n  'val': {'id': 1, 'role': 'base', 'text': 'x', 'type': 'mathord'}},\n {'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None},\n  'val': {'id': 2, 'role': 'sup', 'text': '2', 'type': 'textord'}},\n {'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 3, 'role': None, 'text': '+', 'type': 'bin'}},\n {'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 4, 'role': None, 'text': '1', 'type': 'textord'}},\n {'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 5, 'role': None, 'text': '=', 'type': 'rel'}},\n {'structure': {'bro': [5, None],\n                'child': None,\n                'father': None,\n                'forest': None},\n  'val': {'id': 6, 'role': None, 'text': 'y', 'type': 'mathord'}}]"
+      "text/plain": [
+       "<Formula: x^2 + x+1 = y>"
+      ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 2
     }
    ],
+   "metadata": {
+    "collapsed": true
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from EduNLP.Formula import Formula\n",
-    "from EduNLP.Formula import FormulaGroup\n",
-    "from EduNLP.Formula.viz import ForestPlotter\n",
-    "\n",
-    "Formula(\"x^2 + 1 = y\")"
-   ]
+    "查看公式切分后的结点元素:"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
+   "source": [
+    "f.elements"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "NodeView((0, 1, 2, 3, 4, 5, 6))"
+      "text/plain": [
+       "[{'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       " {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
+       " {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
+       " {'id': 3, 'type': 'bin', 'text': '+', 'role': None},\n",
+       " {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},\n",
+       " {'id': 5, 'type': 'bin', 'text': '+', 'role': None},\n",
+       " {'id': 6, 'type': 'textord', 'text': '1', 'role': None},\n",
+       " {'id': 7, 'type': 'rel', 'text': '=', 'role': None},\n",
+       " {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]"
+      ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "f = Formula(\"x^2 + 1 = y\", variable_standardization=True)\n",
-    "f.ast.nodes"
+    "查看公式的抽象语法分析树："
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
+   "source": [
+    "f.ast "
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "OutEdgeView([(1, 0), (2, 0)])"
+      "text/plain": [
+       "[{'val': {'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       "  'structure': {'bro': [None, 3],\n",
+       "   'child': [1, 2],\n",
+       "   'father': None,\n",
+       "   'forest': None}},\n",
+       " {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
+       "  'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},\n",
+       " {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
+       "  'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},\n",
+       " {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},\n",
+       "  'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},\n",
+       " {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},\n",
+       "  'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},\n",
+       " {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},\n",
+       "  'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},\n",
+       " {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},\n",
+       "  'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},\n",
+       " {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},\n",
+       "  'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},\n",
+       " {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},\n",
+       "  'structure': {'bro': [7, None],\n",
+       "   'child': None,\n",
+       "   'father': None,\n",
+       "   'forest': None}}]"
+      ]
      },
-     "execution_count": 11,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "source": [
-    "f.ast.edges"
+    "print('nodes: ',f.ast_graph.nodes)\n",
+    "print('edges: ' ,f.ast_graph.edges)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
+      "edges:  [(0, 1), (0, 2)]\n"
+     ]
+    }
    ],
    "metadata": {
     "collapsed": false,
@@ -74,25 +182,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
+   "source": [
+    "ForestPlotter().export(\n",
+    "    f.ast_graph, root_list=[node[\"val\"][\"id\"] for node in f.ast if node[\"structure\"][\"father\"] is None],\n",
+    ")\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWAElEQVR4nO3dW3BT94HH8e9fV0u2ZRswGEPC/ZaEhBAIuW2apM10uk3KtLPdzs4+93ln9mH7ug/70vdt+7Az2Z3pdHZ220mbaTZt2iYQciOhBBICOKHcwTeChWTdjnR0zj7IdoFgY8vS/xzC7/NEIkv/3zmSf4j//38k4/s+IiJiRyToACIidxOVroiIRSpdERGLVLoiIhapdEVELFLpiohYpNIVEbFIpSsiYpFKV0TEIpWuiIhFKl0REYtUuiIiFql0RUQsUumKiFik0hURsUilKyJikUpXRMQila6IiEUqXRERi1S6IiIWqXRFRCxS6YqIWKTSFRGxSKUrImKRSldExCKVroiIRSpdERGLVLoiIhapdEVELIrZHCwST476bnWFzTFnY2KJMa/mDLTr8W0f662OJ0zne9rNOYPIGPZzNdtrM+jXVJjO0fXa/bvcasb3fXuDGeOv+dGr1saby/kfv4Dv+6Zdj2/7WG91PGE639NuzhlExrCfq9lem0G/psJ0jq7X7t/lVgv19EL57Eczf8598PINt/l1l+z+/+Tau/9tO1bLzHl8bpX8oVfI7nspNJkAnNG/cPX1n9iMdNtM4y//G4Xj+2xGAm6fK3/4t+Q/fBnfq4cik1erkD/0CuO//Fc8pxSKTL7vk93/X0z88WfUy3lrmYIU6tKtjp/DzY2TPfBznOGTeLXKzBNYHT9Lav0uYpnld+yTNdfxmViC+LJ7qZcnQ5PJr7vUrpwj1rsyNJkAIh3d+DXHaqbb5fIqBaojn4OJhiZTJN5BZvdeEoNbiCTTochkjMGvlfHrLpGEvUxBCnXpAjiXT9L14PMklq0hEu8gtW5n0JFaaq7jS617mOTgFvy6G4pM1fEz1AsTOJeOUy/lQpEJYNnf/hNepYAXQPHOlsv3PWI9A8SXrqY6djoUmQDc3BixHvvTsrNl8qoVkqvvJ735CWpfXLCeKwihL93kqq2UPnuX2tWLjb8hzxwGILF8HeUzh3Hz40RTmYBTNm+243MLE+Te/9/GL2zE7rul2TIlV26m5/G/J7n6fqLpnlBkqpfz5A7+knphgkg8aTXTXLmiqQwYqJz/mHjfYCgyAZROHSS98VGreebKZCJRKhc/pXzuCLGe5dZzBUELaW0S9KJHEBnmIwyLM2E/V1pIWxgtpImIyKwCKd1brfR+lcZrZYbr73f94lG7x52LmxujOPQOlQuf4IycmvXnpv8VtZAMQZ2nVo6/kNubOUfzHXMuN/8L18ZzNJd2vqbCxurFEdPc3CilUwdxs8N073yRyY9/jzM8RHJgMx1rH6L8lw/xvTrpzY/jZodJb34CgNzBXxFNZ8BESG3YTeHYG0TTGbxKEfw6sd4B6qUcHWsfpvTZeyRXbsTEUziXPiVbyZN5ZC/Rrr4gDrnpY3azw+Q//DXx/jVUx89RL0zgVcv4TomeJ37QtnGv/uGnRFMZTDyJm7/Ckud+yOTR1/DKk3Ss3YFz8RjJex+kevYIxRP7yezaS+Hj1yEaI5bpb2xJ8lxMLIHnFEJ/nhY7fu3KWcrnjuJmh/GqZRIrNuBcPE60q4/U+l1UR0/h5saZPPIaAB3rHsa5cIxY70o8p0g9PzbvjIvNevUPPyW1YTfOpRNgImQeecHauEG8psImkHe6sZ4B0pseI9Y3yORHr5LasJt470q6HnyeyoVjVMfOEO1aSj3/xcwTBhDrHcBzysDU9I3vAZDatAe/7hJfck/j9ql9kb7XuD3Rv47ObU/jjM7+N2i7NXvM0XQPmUe/S/W6v/07t/4NRONtHTeW6af7kReJpDIklq/Hr1cBcPNXiGX6Sd6znWiqm/TGPUS7luBcPknn9m+AaTw3nduexq+7dO98AWPm/zIL6jwtdvx4/zpSa3fgjHxONN2DV8qRXH0ftYnLxHqWkxjYRL0wQcfaHcT6VuKVcqQ27KZeuEpm13eIdHQvKOdissZ6lpMc2Ei9kCXa2Ys7edXOuAG9psImkOTRrj6KJ94itX4XzuWTxHsHqGVHyB/6DR33PEBixXp81yHWt5LiZ+/O3M+vlvDKeeL9ayh88kdqE5cAKJ08AL5PvVIgkujAzY3jla5ROXcEgOqVsxRPHCAxsDGIwwWaP+Z6KUf+w1/fmN3Mf82g2XGn95eaSBSMwasU8etuYz9lR/fUtrH81M4KQ3LVNorH/gTT/2w1hmjXUoon9uN789/yFtR5Wuz4kXiS8ulDJAc24TlFYktW4QwPkehfS+3aKPX8OCaeoHLuKG52hEi6B0yE+LJ7KRzf19R+7KafWyJE0r1Eu3oxxhDrWmpn3IBeU2ET6O6F8rmjeE6Rzi1PkvvgZXr2fG/Bj1k49idSG3YveAtTULsXWnHMt3K7Ffl2jbvYnLbP060y3CpHkOdrobsXbL2mwnSO5soZdoHM6U5Lrd0x8+dmn7Cu7d9oURo7WnHMd9K4zQo6b9DjL4ReU3eWUO5euPl2Nz9O+eyRto1nU7Or3LUvLrYjzm3Hne/trfZVOk/FE/vJHvh5uyKF7rmbz5hh+p20LdDdC37NoV6YINq9FDc3TrSrj1jPCqqjp6heOY+bvUx68xPEMsspnz1C5eKnVMfO0HHvdspnj9C59UmckVOUTr0/s3JaPHmAxPL1uLlxevZ8r/Hkei75w7/FGEP3zoWt1AZ97NNKpw/Rs+yeUOW69vYvGqvzmx4j1rUk8DwQzvPUed8zuG0smWYyFYfeoT61gJbe+hSx7oXN67Y6k+/Vmfzo/6Yue6/Rcc8DLc0TJoHuXnCGh4ikuvGcEunNj1P6/P3GRPzAJhL9a274ZQIaH7TiufhuY9VzenfC9Sun8d4B/Op1n6Dke/hene4d32psLQtYM8dePPEWzqXj5A+9suCV5nbmmlmdb3HhNpsnrOep3b4KmUwkCvg4l46TXH2/tZxBCHT3QnJwC155kviSQUpD79C1/Xmcy0N4lUmq42duWPmMpntwr41gEmnc3Dh+rULp8/cArls5vR+v5lAvXiO+dBWTR39HffILTCTK5NHfYSx+stJsmjn2zvu+RnL1/WR27235O5Jmc/m+d8PqfNB5IJznCaB85jDOpePUJi6HJlPn1qfI7N7btnPVTKbk6sbvr2li18mdJJDpha77n/3S/+u498GZPydXbQUgsXz9DbfXS7lb/rNjeirh5vuEUTPHDu1fqFhoLmMi9D75D6HJMy1s5wkgtf4RUusfCVWmdmsmk3PhGJ33fa394QIW6O6FhYgk07N+BqhWTkXufJlHvxt0BCtCcVnHQq4DLxz7E7kPXsYZOUXlwiftjtYSYTw+ZbpzM4U1VxgzhVHg73Sz+14iMbARNzuC51bpfuibQGNRZHrVM5JI07HmIUyscUmn5xRmLiHMvfc/M/fL7nuJWM9yMo/9HfkPfz1zXXm0M5jPWwjr8SnTnZsprLnCmCmsAn+nG0ln6Nz2NABd27+OMzwEcMOqZ2rDrpknChrXYJdOHpj57+n7JVZuJLV+F252pOnrylstjMenTHduprDmCmOmsAq8dK+PUDj2BsnBxgT7Dauepw/NbBMDvvQ33l/vF2l8Fo4xTV9X3nphPD5lunMzhTVXGDOFk745ok2C/pT/IDLMRxi+jSDs50rfHLEwd9pnL4Tgna6IyN3D6jvdSDw56rtV+19Fegsmlhjzas5Aux7f9rHe6njCdL6n3ZwziIxhP1ezvTaDfk2F6Rxdr92/y61mtXQXyhgTBa4A9/m+/6XLnowx3wb+2ff9r1sPJyLShLBPL+wARm9VuFPeBvYYYzrsRRIRaV7YS/c54M3ZbvR9Pw98CjxuLZGIyCKEvXSfZY7SnfLm1M+JiIReaEvXGBMHngLeus2P7qPxjlhEJPRCW7rAbuC07/u3uxTlXWCHMabLQiYRkUUJc+nOZ2oB3/dLwGHgybYnEhFZpDCX7nM0pg7mQ1MMInJHCGXpTm0B2wMcuN3PTnkTla6I3AFCWbrAY8CnU1vC5uMDYKsxprd9kUREFi+spbuQqQV833eAg8DTbUskItICYS7d2y6i3URTDCISeqErXWNMJ43Lf9+9zY/eTBdJiEjoha50aVwQ8dHUVrCFOAysNcb0tyGTiEhLhLF0FzSfO833fZfGB+A80+pAIiKtEsbSfRZ4o8n7aopBREItVKU7teVrG40tYM3QYpqIhFqoSpfGlq+DU1vAmvEJ0G+MGWxhJhGRlglb6c7r8xZm4/u+B+xHUwwiElJhK91m9ufeTFMMIhJaoSndqa1ea2ls/VoMffiNiIRWaEqXxlavt6e2fi3GSSBljFm3+EgiIq0VptJd1HzuNL/x9cbaOiYioRSm0m3qoohZaIpBREIpFKU7tcWrH/i4RQ/5JvCcMca06PFERFoiFKVLYypg/9SWr1Y4A9SAzS16PBGRlghL6bZiq9iM6+Z1NcUgIqESptJt1XzuNM3rikjoBF66U1u7UjS2erXSPuAZY0zgxygiMi0MhfQs8ObUlEDL+L5/EcgCD7TycUVEFiMMpduOqYVpmmIQkVAJtHSntnS15KKIWegiCREJlaDf6W4GXBpbvNphP/C0MSbWpscXEVmQoEv3OWBfq+dzp/m+PwZcBh5ux+OLiCxUGEq3XVML07RfV0RCI7DSndrK9QztW0SbpnldEQmNIN/pPgBcm9ra1U5vAU8aYxJtHkdE5LaCLF0bUwv4vp8FPgcebfdYIiK3E0jpGmOitHer2M3eBJ6dGldEJDBBvdN9j0bpbmr3QMaYFNAH/AD4WbvHExGZS1ClmwfSNMqw3ao09gPfD5yzMJ6IyKyCKt0hGh9w8y/tHsj3/TqwF5gAPm33eCIiczFtui5BRERuIeiLI0RE7ioqXRERi+b8IJhIPDnqu9UVtsLMl4klxgDCkM3EEmNezRkIOoeI3BnmnNM1xvhrfvSqxTjzc/7HLwAQhmznf/wCvu/rW4dFZF40vSAiYlFbSrd89qOZP+c+ePlLt2f3vURx6J12DD2ruTLVSzlyB3/JxBv/YTWTiNx92lK61fFzuLlxsgd+jjN8Eq9WuaH0und+ux3DNp0pmu6h57HvE0l2Ws8lIneXtk0vOJdP0vXg8ySWrSES7yC1bme7hmpJpvLZIyRXtv2qZBG5y7WtdJOrtlL67F1qVy823lWeOTxzW3HoHcqn/4xXq7Rr+AVlqhez5D/4FbWJYXSxiIi0k3YvLJJ2L4jIQmj3goiIRfMq3VvtQFgsNzdGcegdKhc+wRk5NevPTb8Tv1WGZnPd7n6z3T5XFhGR+ZjXV5O7uVFKpw7iZofp3vkikx//Hmd4iOTAZjrWPkT5Lx/ie3XSmx/HzQ6T3vwEAFf/8FOiqQwmnsTNX2HJcz9k8uhreOVJOtbuwLl4jOS9D1I9e4Tiif1kdu2l8PHrEI0Ry/TjOSXwXEwsgecUWparduUs5XNHcbPDeNUyiRUbcC4eJ9rVR2r9Lqqjp3Bz40weeQ2AjnUP41w4Rqx3JZ5TpJ4fa9X5F5G7zLze6cZ6BkhveoxY3yCTH71KasNu4r0r6XrweSoXjlEdO0O0ayn1/BczxQYQy/TT/ciLRFIZEsvX49erALj5K8Qy/STv2U401U164x6iXUtwLp+kc/s3wDSmSDu3PY1fd+ne+QKN77FsTa54/zpSa3fgjHxONN2DV8qRXH0ftYnLxHqWkxjYRL0wQcfaHcT6VuKVcqQ27KZeuEpm13eIdHQv6qSLyN1rXqUb7eqjeOItUut34Vw+Sbx3gFp2hPyh39BxzwMkVqzHdx1ifSspfvbuX+849e04JhIFY/AqRfy6i193iXR041w6Tr2Uh0gUMCRXbaN47E8wvbhnDNGupRRP7Mf33JblisSTlE8fIjmwCc8pEluyCmd4iET/WmrXRqnnxzHxBJVzR3GzI0TSPWAixJfdS+H4PurlyebPuIjc1Ra0e6F87iieU6Rzy5PkPniZnj3fs5HxS27evRBkLu1eEJGFWNDuhdTaHXRueRLgS8XW7OJUK8yWq5lM+T+/wtXXf0K9eK2lGUVEYJ4LafMxvajl1xzqhQmi3Utxc+NEu/qI9aygOnqK6pXzuNnLM/Or197+RWPxatNjxLqWtCrKojJldu2leOItvEqBaGdvyzOJyN2tZft0pxe1nOEhIqluPKdEevPjlD5/vzG/OrCJRP+aGxa0Zhav2lC4zWZy81eoF7PEl65uSyYRubu1rHSnF7WSg1vwypPElwxSGnqHru3P41wewqtMUh0/M7Og5fveDYtX7bDQTABXX/938H3c/JW2ZBKRu5suA14kLaSJyELoMmAREYsWVbo3v0uea7eA7UtnF5JNRMSWpncvZPe9RGJgI252BM+t0v3QNwEonnhrZqdAJJGmY81DmFgcPJf84d9ijCExuJXK+aPE+1ZRL1wFEyG1fheTR1/DROL0/s0/LuqgFpqt8Okb+DWHxMrNuNdG8WsV4svWUPj490Q7l9Dz+PcxscSiMomIwCLe6UbSGTq3PQ1A1/av4wwPAdywUyC1YVejcAHfq9O941uNq9KqZSIdGapfnCfWN4jnlBr3S3Y1ftatLe6gFpgtvmQ1nlMimspMZfUASA5uITGwkVp2eFF5RESmLWJ64a93LRx7g+TgVoAbdwqcPoTvNj5vwUSiTB79HSaZxs1faRRevY7nlPDrNZKrtjX2xnb1zZShrWxepQAmgucUcIaHcC6fAKBy8QSVi8eI9w0uMo+ISIN2L8zCzY3hjJyic+tTt82i3QsiMl8tuyLtqybWs4JYz4qgY4jIV8yc73Qj8eSo71ZD1zwmlhgDCEM2E0uMeTVnIOgcInJnmLN0RUSktXRxhIiIRSpdERGLVLoiIhapdEVELFLpiohYpNIVEbFIpSsiYpFKV0TEIpWuiIhFKl0REYtUuiIiFql0RUQsUumKiFik0hURsUilKyJikUpXRMQila6IiEUqXRERi1S6IiIWqXRFRCxS6YqIWKTSFRGxSKUrImKRSldExCKVroiIRSpdERGLVLoiIhapdEVELFLpiohYpNIVEbFIpSsiYpFKV0TEIpWuiIhFKl0REYtUuiIiFql0RUQsUumKiFik0hURsUilKyJikUpXRMQila6IiEUqXRERi1S6IiIWqXRFRCxS6YqIWKTSFRGxSKUrImKRSldExCKVroiIRSpdERGLVLoiIhapdEVELFLpiohYpNIVEbFIpSsiYpFKV0TEIpWuiIhFKl0REYtUuiIiFql0RUQsUumKiFik0hURsUilKyJikUpXRMQila6IiEUqXRERi1S6IiIWqXRFRCxS6YqIWKTSFRGxSKUrImKRSldExCKVroiIRSpdERGLVLoiIhapdEVELFLpiohYpNIVEbFIpSsiYpFKV0TEIpWuiIhFKl0REYtUuiIiFql0RUQsUumKiFik0hURsUilKyJikUpXRMQila6IiEUqXRERi1S6IiIWqXRFRCxS6YqIWKTSFRGxSKUrImKRSldExCKVroiIRSpdERGLVLoiIhapdEVELFLpiohY9P8QdEv7a4+pVQAAAABJRU5ErkJggg=="
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
-   "source": [
-    "ForestPlotter().export(\n",
-    "    f.ast, root_list=[node[\"val\"][\"id\"] for node in f.element if node[\"structure\"][\"father\"] is None],\n",
-    ")\n",
-    "plt.show()"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -100,22 +210,42 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 变量标准化\n",
+    "\n",
+    "下面这个例子中，`var` 为变量编号。同一变量拥有相同的变量编号。  \n",
+    "如：`x` 变量的编号为 `0`， `y` 变量的编号为 `1`。"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
+   "source": [
+    "f.variable_standardization().elements"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "OutEdgeView([(1, 0), (2, 0)])"
+      "text/plain": [
+       "[{'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       " {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},\n",
+       " {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
+       " {'id': 3, 'type': 'bin', 'text': '+', 'role': None},\n",
+       " {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},\n",
+       " {'id': 5, 'type': 'bin', 'text': '+', 'role': None},\n",
+       " {'id': 6, 'type': 'textord', 'text': '1', 'role': None},\n",
+       " {'id': 7, 'type': 'rel', 'text': '=', 'role': None},\n",
+       " {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]"
+      ]
      },
-     "execution_count": 13,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 20
     }
    ],
-   "source": [
-    "f.ast.edges"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -123,22 +253,38 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 方程组结构解析\n",
+    "\n",
+    "调用 `FormulaGroup` 类解析公式方程组，相关的属性和函数方法同上。"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
+   "source": [
+    "fs = FormulaGroup([\n",
+    "    \"x^2 = y\",\n",
+    "    \"x^3 = y^2\",\n",
+    "    \"x + y = \\pi\"\n",
+    "])\n",
+    "fs"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "[{'structure': {'bro': [None, 3],\n                'child': [1, 2],\n                'father': None,\n                'forest': None},\n  'val': {'id': 0, 'role': None, 'text': '^', 'type': 'supsub'}},\n {'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None},\n  'val': {'id': 1, 'role': 'base', 'text': 'x', 'type': 'mathord', 'var': 0}},\n {'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None},\n  'val': {'id': 2, 'role': 'sup', 'text': '2', 'type': 'textord'}},\n {'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 3, 'role': None, 'text': '+', 'type': 'bin'}},\n {'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 4, 'role': None, 'text': '1', 'type': 'textord'}},\n {'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 5, 'role': None, 'text': '=', 'type': 'rel'}},\n {'structure': {'bro': [5, None],\n                'child': None,\n                'father': None,\n                'forest': None},\n  'val': {'id': 6, 'role': None, 'text': 'y', 'type': 'mathord', 'var': 1}}]"
+      "text/plain": [
+       "<FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \\pi>>"
+      ]
      },
-     "execution_count": 14,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 21
     }
    ],
-   "source": [
-    "f.variable_standardization()"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -148,47 +294,199 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
+   "source": [
+    "fs.elements"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "[[{'structure': {'bro': [None, 3],\n                'child': [1, 2],\n                'father': None,\n                'forest': None},\n  'val': {'id': 0, 'role': None, 'text': '^', 'type': 'supsub'}},\n {'structure': {'bro': [None, 2],\n                'child': None,\n                'father': 0,\n                'forest': [6, 12]},\n  'val': {'id': 1, 'role': 'base', 'text': 'x', 'type': 'mathord'}},\n {'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None},\n  'val': {'id': 2, 'role': 'sup', 'text': '2', 'type': 'textord'}},\n {'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 3, 'role': None, 'text': '=', 'type': 'rel'}},\n {'structure': {'bro': [3, None],\n                'child': None,\n                'father': None,\n                'forest': [10, 14]},\n  'val': {'id': 4, 'role': None, 'text': 'y', 'type': 'mathord'}}],\n [{'structure': {'bro': [None, 8],\n                'child': [6, 7],\n                'father': None,\n                'forest': None},\n  'val': {'id': 5, 'role': None, 'text': '^', 'type': 'supsub'}},\n {'structure': {'bro': [None, 7],\n                'child': None,\n                'father': 5,\n                'forest': [1, 12]},\n  'val': {'id': 6, 'role': 'base', 'text': 'x', 'type': 'mathord'}},\n {'structure': {'bro': [6, None], 'child': None, 'father': 5, 'forest': None},\n  'val': {'id': 7, 'role': 'sup', 'text': '3', 'type': 'textord'}},\n {'structure': {'bro': [5, 9], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 8, 'role': None, 'text': '=', 'type': 'rel'}},\n {'structure': {'bro': [8, None],\n                'child': [10, 11],\n                'father': None,\n                'forest': None},\n  'val': {'id': 9, 'role': None, 'text': '^', 'type': 'supsub'}},\n {'structure': {'bro': [None, 11],\n                'child': None,\n                'father': 9,\n                'forest': [4, 14]},\n  'val': {'id': 10, 'role': 'base', 'text': 'y', 'type': 'mathord'}},\n {'structure': {'bro': [10, None], 'child': None, 'father': 9, 'forest': None},\n  'val': {'id': 11, 'role': 'sup', 'text': '2', 'type': 'textord'}}],\n [{'structure': {'bro': [None, 13],\n                'child': None,\n                'father': None,\n                'forest': [1, 6]},\n  'val': {'id': 12, 'role': None, 'text': 'x', 'type': 'mathord'}},\n {'structure': {'bro': [12, 14], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 13, 'role': None, 'text': '+', 'type': 'bin'}},\n {'structure': {'bro': [13, 15],\n                'child': None,\n                'father': None,\n                'forest': [4, 10]},\n  'val': {'id': 14, 'role': None, 'text': 'y', 'type': 'mathord'}},\n {'structure': {'bro': [14, 16], 'child': None, 'father': None, 'forest': None},\n  'val': {'id': 15, 'role': None, 'text': '=', 'type': 'rel'}},\n {'structure': {'bro': [15, None],\n                'child': None,\n                'father': None,\n                'forest': None},\n  'val': {'id': 16, 'role': None, 'text': '\\\\pi', 'type': 'mathord'}}]]"
+      "text/plain": [
+       "[{'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       " {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
+       " {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
+       " {'id': 3, 'type': 'rel', 'text': '=', 'role': None},\n",
+       " {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},\n",
+       " {'id': 5, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       " {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
+       " {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},\n",
+       " {'id': 8, 'type': 'rel', 'text': '=', 'role': None},\n",
+       " {'id': 9, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       " {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},\n",
+       " {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
+       " {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},\n",
+       " {'id': 13, 'type': 'bin', 'text': '+', 'role': None},\n",
+       " {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},\n",
+       " {'id': 15, 'type': 'rel', 'text': '=', 'role': None},\n",
+       " {'id': 16, 'type': 'mathord', 'text': '\\\\pi', 'role': None}]"
+      ]
      },
-     "execution_count": 15,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 22
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "source": [
-    "fs = FormulaGroup([\n",
-    "    \"x^2 = y\",\n",
-    "    \"x^3 = y^2\",\n",
-    "    \"x + y = \\pi\"\n",
-    "])\n",
-    "fs"
+    "fs.ast"
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[{'val': {'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       "  'structure': {'bro': [None, 3],\n",
+       "   'child': [1, 2],\n",
+       "   'father': None,\n",
+       "   'forest': None}},\n",
+       " {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
+       "  'structure': {'bro': [None, 2],\n",
+       "   'child': None,\n",
+       "   'father': 0,\n",
+       "   'forest': [6, 12]}},\n",
+       " {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
+       "  'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},\n",
+       " {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},\n",
+       "  'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},\n",
+       " {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},\n",
+       "  'structure': {'bro': [3, None],\n",
+       "   'child': None,\n",
+       "   'father': None,\n",
+       "   'forest': [10, 14]}},\n",
+       " {'val': {'id': 5, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       "  'structure': {'bro': [None, 8],\n",
+       "   'child': [6, 7],\n",
+       "   'father': None,\n",
+       "   'forest': None}},\n",
+       " {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
+       "  'structure': {'bro': [None, 7],\n",
+       "   'child': None,\n",
+       "   'father': 5,\n",
+       "   'forest': [1, 12]}},\n",
+       " {'val': {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},\n",
+       "  'structure': {'bro': [6, None], 'child': None, 'father': 5, 'forest': None}},\n",
+       " {'val': {'id': 8, 'type': 'rel', 'text': '=', 'role': None},\n",
+       "  'structure': {'bro': [5, 9], 'child': None, 'father': None, 'forest': None}},\n",
+       " {'val': {'id': 9, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
+       "  'structure': {'bro': [8, None],\n",
+       "   'child': [10, 11],\n",
+       "   'father': None,\n",
+       "   'forest': None}},\n",
+       " {'val': {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},\n",
+       "  'structure': {'bro': [None, 11],\n",
+       "   'child': None,\n",
+       "   'father': 9,\n",
+       "   'forest': [4, 14]}},\n",
+       " {'val': {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
+       "  'structure': {'bro': [10, None],\n",
+       "   'child': None,\n",
+       "   'father': 9,\n",
+       "   'forest': None}},\n",
+       " {'val': {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},\n",
+       "  'structure': {'bro': [None, 13],\n",
+       "   'child': None,\n",
+       "   'father': None,\n",
+       "   'forest': [1, 6]}},\n",
+       " {'val': {'id': 13, 'type': 'bin', 'text': '+', 'role': None},\n",
+       "  'structure': {'bro': [12, 14],\n",
+       "   'child': None,\n",
+       "   'father': None,\n",
+       "   'forest': None}},\n",
+       " {'val': {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},\n",
+       "  'structure': {'bro': [13, 15],\n",
+       "   'child': None,\n",
+       "   'father': None,\n",
+       "   'forest': [4, 10]}},\n",
+       " {'val': {'id': 15, 'type': 'rel', 'text': '=', 'role': None},\n",
+       "  'structure': {'bro': [14, 16],\n",
+       "   'child': None,\n",
+       "   'father': None,\n",
+       "   'forest': None}},\n",
+       " {'val': {'id': 16, 'type': 'mathord', 'text': '\\\\pi', 'role': None},\n",
+       "  'structure': {'bro': [15, None],\n",
+       "   'child': None,\n",
+       "   'father': None,\n",
+       "   'forest': None}}]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
     }
-   }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 25,
+   "source": [
+    "ForestPlotter().export(\n",
+    "    fs.ast_graph, root_list=[node[\"val\"][\"id\"] for node in fs.ast if node[\"structure\"][\"father\"] is None],\n",
+    ")"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "[[{'structure': {'bro': [None, 3],\n                 'child': [1, 2],\n                 'father': None,\n                 'forest': None},\n   'val': {'id': 0, 'role': None, 'text': '^', 'type': 'supsub'}},\n  {'structure': {'bro': [None, 2],\n                 'child': None,\n                 'father': 0,\n                 'forest': [6, 12]},\n   'val': {'id': 1, 'role': 'base', 'text': 'x', 'type': 'mathord', 'var': 0}},\n  {'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None},\n   'val': {'id': 2, 'role': 'sup', 'text': '2', 'type': 'textord'}},\n  {'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None},\n   'val': {'id': 3, 'role': None, 'text': '=', 'type': 'rel'}},\n  {'structure': {'bro': [3, None],\n                 'child': None,\n                 'father': None,\n                 'forest': [10, 14]},\n   'val': {'id': 4, 'role': None, 'text': 'y', 'type': 'mathord', 'var': 1}}],\n [{'structure': {'bro': [None, 8],\n                 'child': [6, 7],\n                 'father': None,\n                 'forest': None},\n   'val': {'id': 5, 'role': None, 'text': '^', 'type': 'supsub'}},\n  {'structure': {'bro': [None, 7],\n                 'child': None,\n                 'father': 5,\n                 'forest': [1, 12]},\n   'val': {'id': 6, 'role': 'base', 'text': 'x', 'type': 'mathord', 'var': 0}},\n  {'structure': {'bro': [6, None], 'child': None, 'father': 5, 'forest': None},\n   'val': {'id': 7, 'role': 'sup', 'text': '3', 'type': 'textord'}},\n  {'structure': {'bro': [5, 9], 'child': None, 'father': None, 'forest': None},\n   'val': {'id': 8, 'role': None, 'text': '=', 'type': 'rel'}},\n  {'structure': {'bro': [8, None],\n                 'child': [10, 11],\n                 'father': None,\n                 'forest': None},\n   'val': {'id': 9, 'role': None, 'text': '^', 'type': 'supsub'}},\n  {'structure': {'bro': [None, 11],\n                 'child': None,\n                 'father': 9,\n                 'forest': [4, 14]},\n   'val': {'id': 10, 'role': 'base', 'text': 'y', 'type': 'mathord', 'var': 1}},\n  {'structure': {'bro': [10, None], 'child': None, 'father': 9, 'forest': None},\n   'val': {'id': 11, 'role': 'sup', 'text': '2', 'type': 'textord'}}],\n [{'structure': {'bro': [None, 13],\n                 'child': None,\n                 'father': None,\n                 'forest': [1, 6]},\n   'val': {'id': 12, 'role': None, 'text': 'x', 'type': 'mathord', 'var': 0}},\n  {'structure': {'bro': [12, 14], 'child': None, 'father': None, 'forest': None},\n   'val': {'id': 13, 'role': None, 'text': '+', 'type': 'bin'}},\n  {'structure': {'bro': [13, 15],\n                 'child': None,\n                 'father': None,\n                 'forest': [4, 10]},\n   'val': {'id': 14, 'role': None, 'text': 'y', 'type': 'mathord', 'var': 1}},\n  {'structure': {'bro': [14, 16], 'child': None, 'father': None, 'forest': None},\n   'val': {'id': 15, 'role': None, 'text': '=', 'type': 'rel'}},\n  {'structure': {'bro': [15, None],\n                 'child': None,\n                 'father': None,\n                 'forest': None},\n   'val': {'id': 16, 'role': None, 'text': '\\\\pi', 'type': 'mathord'}}]]"
+      "text/plain": [
+       "[Text(22.32, 181.2, 'id: 0\\ntype: supsub\\ntext: \\\\supsub\\nrole: None'),\n",
+       " Text(11.16, 108.72, 'id: 1\\ntype: mathord\\ntext: x\\nrole: base'),\n",
+       " Text(33.480000000000004, 108.72, 'id: 2\\ntype: textord\\ntext: 2\\nrole: sup'),\n",
+       " Text(55.8, 181.2, 'id: 3\\ntype: rel\\ntext: =\\nrole: None'),\n",
+       " Text(78.12, 181.2, 'id: 4\\ntype: mathord\\ntext: y\\nrole: None'),\n",
+       " Text(111.6, 181.2, 'id: 5\\ntype: supsub\\ntext: \\\\supsub\\nrole: None'),\n",
+       " Text(100.44, 108.72, 'id: 6\\ntype: mathord\\ntext: x\\nrole: base'),\n",
+       " Text(122.76, 108.72, 'id: 7\\ntype: textord\\ntext: 3\\nrole: sup'),\n",
+       " Text(145.08, 181.2, 'id: 8\\ntype: rel\\ntext: =\\nrole: None'),\n",
+       " Text(178.56, 181.2, 'id: 9\\ntype: supsub\\ntext: \\\\supsub\\nrole: None'),\n",
+       " Text(167.4, 108.72, 'id: 10\\ntype: mathord\\ntext: y\\nrole: base'),\n",
+       " Text(189.72, 108.72, 'id: 11\\ntype: textord\\ntext: 2\\nrole: sup'),\n",
+       " Text(212.04, 181.2, 'id: 12\\ntype: mathord\\ntext: x\\nrole: None'),\n",
+       " Text(234.36, 181.2, 'id: 13\\ntype: bin\\ntext: +\\nrole: None'),\n",
+       " Text(256.68, 181.2, 'id: 14\\ntype: mathord\\ntext: y\\nrole: None'),\n",
+       " Text(279.0, 181.2, 'id: 15\\ntype: rel\\ntext: =\\nrole: None'),\n",
+       " Text(301.32, 181.2, 'id: 16\\ntype: mathord\\ntext: \\\\pi\\nrole: None')]"
+      ]
      },
-     "execution_count": 16,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 25
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "source": [
-    "fs.variable_standardization()"
+    "for ft in fs.variable_standardization():\n",
+    "    print(ft.elements)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[{'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]\n",
+      "[{'id': 5, 'type': 'supsub', 'text': '\\\\supsub', 'role': None}, {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'}, {'id': 8, 'type': 'rel', 'text': '=', 'role': None}, {'id': 9, 'type': 'supsub', 'text': '\\\\supsub', 'role': None}, {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base', 'var': 1}, {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'}]\n",
+      "[{'id': 12, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, {'id': 13, 'type': 'bin', 'text': '+', 'role': None}, {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, {'id': 15, 'type': 'rel', 'text': '=', 'role': None}, {'id': 16, 'type': 'mathord', 'text': '\\\\pi', 'role': None}]\n"
+     ]
+    }
    ],
    "metadata": {
     "collapsed": false,
@@ -200,23 +498,25 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
   },
   "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
+   "pygments_lexer": "ipython3",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "file_extension": ".py"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 2
 }
\ No newline at end of file
diff --git a/examples/formula/tree.ipynb b/examples/formula/tree.ipynb
index b5f0fd10..ce8bb972 100644
--- a/examples/formula/tree.ipynb
+++ b/examples/formula/tree.ipynb
@@ -2,20 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'value': 1, 'id': 0}"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 1,
    "source": [
     "import networkx as nx\n",
     "\n",
@@ -26,28 +13,45 @@
     "g.add_edge(0, 1)\n",
     "g.add_edge(0, 2)\n",
     "g.nodes[0]"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'value': 1, 'id': 0}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "metadata": {
+    "collapsed": true
+   }
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
   },
   "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
+   "pygments_lexer": "ipython3",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "file_extension": ".py"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 2
 }
\ No newline at end of file
diff --git a/examples/i2v/get_pretrained_i2v.ipynb b/examples/i2v/get_pretrained_i2v.ipynb
new file mode 100644
index 00000000..6c1ee0ff
--- /dev/null
+++ b/examples/i2v/get_pretrained_i2v.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# get_pretrained_i2v\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "使用 EduNLP 项目组给定的预训练模型将给定的题目文本转成向量。\n",
+    "\n",
+    "- 优点：简单方便。\n",
+    "- 缺点：只能使用项目中给定的模型，局限性较大。\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入功能块"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "from EduNLP import get_pretrained_i2v"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 输入\n",
+    "\n",
+    "类型：str  \n",
+    "内容：题目文本 （text）"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "item = {\n",
+    "\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
+    "}\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 模型选择与使用"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "根据题目所属学科选择预训练模型：  \n",
+    "| 预训练模型名称 | 模型训练数据的所属学科 |\n",
+    "| -------------- | ---------------------- |\n",
+    "| d2v_all_256    | 全学科                 |\n",
+    "| d2v_sci_256    | 理科                   |\n",
+    "| d2v_eng_256    | 英语                   |\n",
+    "| d2v_lit_256    | 文科                   |\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "i2v = get_pretrained_i2v(\"d2v_sci_256\")\n",
+    "print(i2v(item))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "EduNLP, INFO Use pretrained t2v model d2v_sci_256\n",
+      "downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as /home/lvrui/.EduNLP/model/general_science_256.zip\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Downloading /home/lvrui/.EduNLP/model/general_science_256.zip 100.00%: 1.90GB | 1.90GB\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "downloader, INFO /home/lvrui/.EduNLP/model/general_science_256.zip is unzip to /home/lvrui/.EduNLP/model/general_science_256\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "([array([-2.38646612e-01,  6.78282678e-02, -2.70181447e-01,  1.64666966e-01,\n",
+      "        3.01246569e-02, -1.82321504e-01,  9.00692567e-02,  1.39259361e-02,\n",
+      "        8.87747705e-02,  3.15691501e-01,  9.60902050e-02, -2.27211684e-01,\n",
+      "       -2.58575261e-01,  6.71877190e-02,  9.72832963e-02, -1.98090047e-01,\n",
+      "        8.28761086e-02, -2.25134045e-01, -1.76834762e-01, -3.76207419e-02,\n",
+      "        6.22433126e-01, -2.67150372e-01,  8.10120702e-02, -1.59442604e-01,\n",
+      "       -1.80032104e-01, -1.31213591e-01,  2.74172515e-01,  1.82820112e-01,\n",
+      "        8.61180499e-02,  2.31776193e-01,  1.79873109e-01,  1.53902888e-01,\n",
+      "       -1.38063684e-01, -1.95138857e-01, -1.09946504e-01,  1.13749191e-01,\n",
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+      "       -1.33229955e-03, -2.00035229e-01, -8.04887619e-03, -2.81720281e-01,\n",
+      "       -1.64224923e-01, -4.84411605e-02, -2.24565342e-01, -1.70706406e-01,\n",
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+      "        5.86069599e-02,  9.45951939e-02,  1.88357439e-02,  1.42038196e-01,\n",
+      "        4.01655644e-01,  6.68841675e-02, -5.52068591e-01, -1.85107216e-01,\n",
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+      "       -1.70589730e-01,  2.10852250e-01,  4.33011979e-01, -3.39060128e-01,\n",
+      "        7.44158542e-03, -1.47019088e-01, -4.82755564e-02,  8.07425156e-02,\n",
+      "        1.82245448e-01, -6.77390322e-02, -5.62187284e-02,  1.52256146e-01,\n",
+      "        2.22834423e-01,  3.34665984e-01, -2.60272548e-02,  1.03390038e-01,\n",
+      "       -3.96476947e-02,  1.40223369e-01, -3.94263297e-01, -4.81414124e-02,\n",
+      "       -9.04471427e-02, -1.01487234e-01,  3.86349201e-01, -4.08536009e-02,\n",
+      "        4.17221561e-02, -3.44486445e-01,  2.28582658e-02,  8.65597930e-03,\n",
+      "        9.48065370e-02,  1.13664225e-01,  7.57174492e-02, -6.70918226e-02,\n",
+      "       -2.76283026e-01, -2.82264426e-02,  1.18102543e-01, -5.32101169e-02,\n",
+      "       -2.39996426e-02,  2.78141703e-02,  2.07452640e-01, -9.74735692e-02,\n",
+      "        8.04340020e-02,  4.25180979e-02, -1.34585306e-01,  2.18971297e-01,\n",
+      "        1.17185367e-02, -1.18722573e-01,  2.41390932e-02,  1.29328389e-02,\n",
+      "       -7.56185055e-02, -1.14210650e-01,  7.66615868e-02, -2.96691954e-02,\n",
+      "        1.80785861e-02, -2.76242346e-01, -2.63213754e-01, -2.42437288e-01,\n",
+      "        1.77747458e-01,  4.11453784e-01, -6.25603944e-02,  2.83305943e-01,\n",
+      "        9.83239859e-02, -2.67412305e-01,  1.50755286e-01,  2.54022274e-02,\n",
+      "        1.23227865e-01,  7.44485529e-03,  8.24907571e-02, -2.45544506e-04,\n",
+      "       -1.21799987e-02,  4.10213545e-02, -2.60983467e-01, -9.83531028e-02,\n",
+      "       -1.91514686e-01, -2.42492985e-02, -2.21523643e-01, -4.43225615e-02,\n",
+      "        9.25253630e-02, -5.05034365e-02,  2.15248734e-01,  2.56709158e-01,\n",
+      "        6.49087429e-02, -8.12986307e-03,  1.28661385e-02, -2.32116710e-02,\n",
+      "        1.69111729e-01,  2.24174489e-03,  2.37702616e-02, -1.39817741e-04],\n",
+      "      dtype=float32)], None)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/i2v/i2v.ipynb b/examples/i2v/i2v.ipynb
new file mode 100644
index 00000000..f6c901f1
--- /dev/null
+++ b/examples/i2v/i2v.ipynb
@@ -0,0 +1,201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# I2V\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。\n",
+    "\n",
+    "- 优点：可以使用自己的模型，另可调整训练参数，灵活性强。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入类"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "from EduNLP.I2V import D2V"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 输入\n",
+    "\n",
+    "类型：str  \n",
+    "内容：题目文本 （text）"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "source": [
+    "item = {\n",
+    "\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
+    "}"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "model_path = \"../test_model/test_gensim_luna_stem_tf_d2v_256.bin\"\n",
+    "i2v = D2V(\"text\",\"d2v\",filepath=model_path, pretrained_t2v = False)\n",
+    "i2v "
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<EduNLP.I2V.i2v.D2V at 0x7fe2517f97c0>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "source": [
+    "i2v(item)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "([array([ 4.76559885e-02, -1.60574958e-01,  1.94614579e-03,  2.40295693e-01,\n",
+       "          2.24517003e-01, -3.24351490e-02,  4.35789041e-02, -1.65670961e-02,\n",
+       "         -7.77302235e-02,  4.23757173e-02,  4.62658405e-02,  7.54115507e-02,\n",
+       "         -4.54682261e-02, -1.82153687e-01,  5.55203669e-02,  4.23391759e-02,\n",
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+       "         -7.20706284e-02, -6.22547232e-02,  1.06899485e-01,  4.60059335e-03,\n",
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+       "         -9.55015421e-02, -7.73611516e-02,  4.43056040e-02, -1.65684260e-02,\n",
+       "          1.65379923e-02, -1.26138464e-01,  8.31304193e-02,  2.06687212e-01,\n",
+       "         -1.69529378e-01,  3.43789416e-03,  1.19198427e-01, -1.38129979e-01,\n",
+       "         -1.87937781e-01, -8.27087983e-02, -1.76488962e-02,  8.51018950e-02,\n",
+       "          8.15693215e-02,  2.30262652e-02,  1.05074964e-01,  3.13350782e-02,\n",
+       "          1.53877333e-01,  1.01772640e-02,  9.17675197e-02, -1.32400826e-01,\n",
+       "          5.29836975e-02,  2.52282787e-02, -6.19753152e-02, -5.56256585e-02,\n",
+       "          3.87686864e-02,  4.30755690e-02,  7.57815093e-02,  2.63280701e-02,\n",
+       "          4.59217802e-02, -1.17288530e-01,  1.76368475e-01,  9.27482091e-04,\n",
+       "          2.64808517e-02,  9.73805785e-03,  1.90501258e-01,  1.02596413e-02,\n",
+       "         -5.55249080e-02, -1.17555618e-01, -9.98716354e-02,  1.28057361e-01,\n",
+       "         -4.52451073e-02,  7.51599446e-02, -3.01250312e-02,  6.24186322e-02,\n",
+       "          5.77449016e-02,  2.07213312e-02, -2.53734970e-03, -1.69801563e-01,\n",
+       "         -2.28750743e-02, -2.55512260e-02,  1.70693725e-01,  2.35232189e-01,\n",
+       "         -2.71384805e-01, -1.84327438e-01,  4.16823551e-02,  8.70332569e-02,\n",
+       "          1.82847306e-01,  2.76729286e-01, -4.31840494e-02, -1.38212308e-01,\n",
+       "         -3.26297544e-02, -4.25132550e-02, -1.62892416e-01,  1.91870285e-03,\n",
+       "          1.52552709e-01, -1.01523520e-02, -9.16219354e-02, -5.46490997e-02,\n",
+       "          6.06994517e-02, -6.42470419e-02,  7.96310753e-02, -5.70830703e-02,\n",
+       "         -8.82780831e-03, -3.94574478e-02,  9.63162258e-02,  1.54309124e-01,\n",
+       "          1.81100428e-01,  8.63620341e-02,  1.56518817e-02, -4.08006124e-02,\n",
+       "          5.20652272e-02,  8.38029310e-02, -1.55516326e-01,  3.57730500e-03,\n",
+       "         -1.50946556e-02,  2.84812655e-02,  1.37905419e-01,  8.77659023e-02,\n",
+       "          8.23542774e-02, -1.04377635e-01,  4.80731949e-03,  1.18891411e-02,\n",
+       "          9.32120830e-02,  7.88019150e-02, -1.44494563e-01, -7.53350407e-02,\n",
+       "         -1.13602541e-01,  5.43805361e-02,  1.64935380e-01, -2.00515296e-02,\n",
+       "          1.92917317e-01, -4.35359031e-02,  8.92477036e-02, -4.37481068e-02,\n",
+       "          4.01461311e-02, -2.59898454e-01, -1.11872263e-01, -1.25746787e-01,\n",
+       "         -2.34577611e-01, -6.69524372e-02,  5.55978045e-02, -1.91931397e-01,\n",
+       "          5.87355606e-02,  1.01886272e-01, -2.64038593e-01, -2.05450356e-02,\n",
+       "         -1.97510555e-01,  9.13371146e-02,  1.49546817e-01, -3.91026959e-02,\n",
+       "          5.94646595e-02,  1.29657034e-02, -3.72891256e-04,  5.56622408e-02,\n",
+       "          1.61776438e-01,  2.29037628e-02, -1.94774106e-01, -5.02247922e-02,\n",
+       "         -5.45939505e-02,  5.31783216e-02,  1.26433298e-01, -1.23263724e-01,\n",
+       "          8.53074417e-02, -1.41412809e-01, -7.71067888e-02,  1.21865064e-01,\n",
+       "          4.73318882e-02,  7.20091909e-02, -9.83269960e-02,  1.99413914e-02,\n",
+       "         -1.88907124e-02, -2.14710683e-02, -4.93260436e-02,  1.64937660e-01,\n",
+       "         -1.07827298e-01, -7.75848776e-02, -6.23578345e-03, -1.05760902e-01,\n",
+       "         -4.14819457e-02,  5.95730543e-02,  4.11023498e-02, -2.18305327e-02,\n",
+       "         -2.30057724e-02, -3.34391668e-02,  1.30382255e-01,  5.10290638e-02,\n",
+       "         -1.21569566e-01, -1.23630039e-01, -1.83883369e-01,  1.10945016e-01,\n",
+       "         -1.05633408e-01, -8.24846700e-02, -3.76710802e-01, -4.50239740e-02],\n",
+       "        dtype=float32)],\n",
+       " None)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/i2v/i2v.py b/examples/i2v/i2v.py
deleted file mode 100644
index 33f62d64..00000000
--- a/examples/i2v/i2v.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from EduNLP import get_pretrained_i2v
-from EduNLP.Vector.t2v import PRETRAINED_MODELS
-from EduNLP.I2V.i2v import MODELS
-from EduNLP.I2V import D2V
-from pathlib import PurePath
-
-
-item = {
-  "stem": r"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$",
-  "options": ["$p_1=p_2$", "$p_1=p_3$", "$p_2=p_3$", "$p_1=p_2+p_3$"]
-}
-i2v = get_pretrained_i2v("d2v_eng_256")
-print(i2v(item))
diff --git a/examples/sif/sif_additon.ipynb b/examples/sif/sif_additon.ipynb
index db6de0b6..bdefc769 100644
--- a/examples/sif/sif_additon.ipynb
+++ b/examples/sif/sif_additon.ipynb
@@ -418,7 +418,7 @@
     "from EduNLP.utils import dict2str4sif\n",
     "\n",
     "test_item_1_str = dict2str4sif(test_item_1, tag_mode=\"head\", add_list_no_tag=False)\n",
-    "test_item_1_str  # doctest: +ELLIPSIS"
+    "test_item_1_str "
    ],
    "outputs": [
     {
diff --git a/examples/t2v/get_pretrained_t2v.ipynb b/examples/t2v/get_pretrained_t2v.ipynb
new file mode 100644
index 00000000..7a9b9acf
--- /dev/null
+++ b/examples/t2v/get_pretrained_t2v.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# get_pretrained_t2v\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "使用 EduNLP 项目组给定的预训练模型将一组题目的切分序列表征为向量。\n",
+    "\n",
+    "- 优点：简单方便。\n",
+    "- 缺点：只能使用项目中给定的模型，局限性较大。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入功能块"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "from tqdm import tqdm\n",
+    "from EduNLP.SIF.segment import seg\n",
+    "from EduNLP.SIF.tokenization import tokenize\n",
+    "from EduNLP.Pretrain import GensimWordTokenizer\n",
+    "from EduNLP.Vector import get_pretrained_t2v"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 输入\n",
+    "\n",
+    "类型：list  \n",
+    "内容：一个题组中每个题目切分序列的组合。\n",
+    "> 这里需要调用 `GensimWordTokenizer` 将题目文本（`str` 类型）转换成 tokens。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "def load_items():\n",
+    "    test_items = [\n",
+    "        {'ques_content':'有公式$\\\\FormFigureID{wrong1?}$和公式$\\\\FormFigureBase64{wrong2?}$，如图$\\\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$'},\n",
+    "        {'ques_content':'如图$\\\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$'},\n",
+    "        {'ques_content':'<div>Below is a discussion on a website.<br><table border=\\1'},\n",
+    "    ]\n",
+    "    for line in test_items:\n",
+    "        yield line\n",
+    "\n",
+    "\n",
+    "token_items = []\n",
+    "for item in tqdm(load_items(), \"sifing\"):\n",
+    "    # transform content into special marks('g','m','a','s'), except text('t') and formula('f').\n",
+    "    # 'general' means symbolize the Formula in figure format and use 'linear' method for formula segmentation\n",
+    "    tokenizer = GensimWordTokenizer(symbol=\"gmas\", general=True)\n",
+    "    token_item = tokenizer(item[\"ques_content\"])\n",
+    "    if token_item:\n",
+    "        token_items.append(token_item.tokens)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "sifing: 3it [00:01,  1.70it/s]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 模型的选择与使用\n",
+    "\n",
+    "根据题目所属学科选择预训练模型：  \n",
+    "| 预训练模型名称 | 模型训练数据的所属学科 |\n",
+    "| -------------- | ---------------------- |\n",
+    "| d2v_all_256    | 全学科                 |\n",
+    "| d2v_sci_256    | 理科                   |\n",
+    "| d2v_eng_256    | 英语                   |\n",
+    "| d2v_lit_256    | 文科                   |"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# make a model -> t2v\n",
+    "t2v = get_pretrained_t2v(\"d2v_sci_256\")\n",
+    "print(t2v(token_items))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as /home/lvrui/.EduNLP/model/general_science_256.zip\n",
+      "downloader, INFO file existed, skipped\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[array([ 0.06439726, -0.00687894,  0.00189043, -0.09257554,  0.05736929,\n",
+      "       -0.11668563, -0.10532057, -0.05405452, -0.0999706 ,  0.06865267,\n",
+      "       -0.02944315,  0.01962002, -0.05162324,  0.20003118,  0.0697258 ,\n",
+      "       -0.07881501,  0.02241233, -0.09130172,  0.00737528,  0.12381789,\n",
+      "        0.17466484,  0.06978347, -0.04626915,  0.0546972 ,  0.04325071,\n",
+      "        0.08251449,  0.02235107,  0.03021882,  0.06331849,  0.047343  ,\n",
+      "       -0.0523185 ,  0.03466854,  0.04471075, -0.157977  , -0.17309383,\n",
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+      "       -0.03859513,  0.04407888, -0.13459732, -0.00885631, -0.03051728,\n",
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+      "        0.02258897, -0.04278664, -0.06129966, -0.0530447 ,  0.07391489,\n",
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+      "        0.00630049,  0.00872505,  0.1209898 ,  0.06722015, -0.08482002,\n",
+      "       -0.04103451, -0.06156358, -0.06794793, -0.08269838,  0.00797044,\n",
+      "        0.0398024 , -0.05226437, -0.08767968,  0.05370751, -0.0163406 ,\n",
+      "        0.06354823,  0.01833309, -0.04794859,  0.00293007,  0.02795881,\n",
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+      "       -0.00129473,  0.0166647 , -0.12217847, -0.03043189,  0.06749637,\n",
+      "        0.02518799,  0.0023186 , -0.09546225, -0.02834061,  0.09958285,\n",
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+      "        0.04012492,  0.1049351 ,  0.0812963 , -0.03247725,  0.02230814,\n",
+      "       -0.09350789, -0.07374805, -0.00360975, -0.04406869,  0.06652459,\n",
+      "       -0.01352143,  0.03196577, -0.02445628, -0.00369316,  0.08269379,\n",
+      "        0.01450656,  0.00063832, -0.03835842,  0.04345626,  0.01187227,\n",
+      "       -0.00093305,  0.07115235, -0.0076325 ,  0.08701758,  0.04713482,\n",
+      "        0.01264468,  0.06189806, -0.08814917, -0.05333196, -0.00481453,\n",
+      "       -0.0817222 , -0.0235838 ,  0.06427557, -0.1294789 ,  0.0511005 ,\n",
+      "       -0.10060691, -0.09959193,  0.06534179,  0.06727127,  0.02504219,\n",
+      "        0.10331362,  0.08312651,  0.01973537, -0.05313597,  0.00906576,\n",
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+      "        0.07376881,  0.02747924,  0.04651742,  0.0238242 , -0.06653331,\n",
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+      "        0.0520957 ,  0.10467171,  0.00502419, -0.06434394,  0.0264335 ,\n",
+      "        0.10456662, -0.05373713, -0.08392601, -0.00148737, -0.06927754,\n",
+      "       -0.0069511 ,  0.09514498, -0.18718064,  0.01543353,  0.02678617,\n",
+      "       -0.0604992 ,  0.04154759,  0.04833129, -0.03514674, -0.06739928,\n",
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+      "       -0.01374356, -0.10934446, -0.03206901, -0.12148175,  0.04096264,\n",
+      "       -0.01937677], dtype=float32), array([ 1.22610450e-01,  7.39429221e-02, -2.68309414e-02, -1.84546083e-01,\n",
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+      "        5.27941734e-02,  1.18618578e-01, -2.80179754e-02,  2.87339203e-02,\n",
+      "       -1.55507982e-01, -9.19391289e-02,  3.76508455e-03, -5.51198199e-02,\n",
+      "        1.52254015e-01, -1.63303297e-02,  1.53180743e-02, -4.80292551e-02,\n",
+      "        3.64428870e-02,  4.72433344e-02, -1.54690584e-02,  1.38526782e-01,\n",
+      "       -2.07128674e-02, -6.04419131e-03,  8.58368129e-02,  2.59652175e-02,\n",
+      "        3.62342522e-02,  1.93398967e-02,  1.85270697e-01,  8.60779434e-02,\n",
+      "        5.13356440e-02,  9.97062027e-02, -1.60114333e-01, -7.86597505e-02,\n",
+      "       -1.56474188e-02, -4.79406789e-02, -9.83885676e-02,  1.37612447e-01,\n",
+      "       -1.12792589e-01,  8.41441303e-02, -2.76834853e-02, -7.47333467e-02,\n",
+      "        5.91405258e-02,  8.87417868e-02,  1.24135986e-01,  1.16535001e-01,\n",
+      "        7.25473464e-02, -5.59153333e-02, -8.62004235e-02, -2.05151532e-02,\n",
+      "       -5.09192795e-02, -1.02337520e-03, -6.75873319e-03,  9.50852856e-02,\n",
+      "        1.14124052e-01,  1.21425636e-01,  1.50401089e-02,  2.82184426e-02,\n",
+      "        8.28925986e-03, -7.12435395e-02, -3.39830741e-02,  3.50209065e-02,\n",
+      "       -2.47368943e-02, -1.32899368e-02,  5.53644374e-02,  2.38398798e-02,\n",
+      "       -2.79304106e-02, -4.90840599e-02, -6.04364052e-02, -1.19904675e-01,\n",
+      "        2.09521875e-02,  1.43528074e-01, -2.40091328e-02, -8.17930847e-02,\n",
+      "        2.31443103e-02,  9.93111953e-02, -2.10919082e-02, -8.92970189e-02,\n",
+      "        6.02104515e-02, -9.31999236e-02,  5.16967252e-02,  6.72613308e-02,\n",
+      "       -2.68879086e-01,  8.30310310e-05,  1.95715223e-02, -9.48403254e-02,\n",
+      "        3.57986689e-02,  7.16455355e-02,  4.67495993e-02, -7.20672682e-02,\n",
+      "        5.46650030e-03,  3.63146327e-02,  2.60427035e-02,  4.11444530e-02,\n",
+      "       -2.85640303e-02,  2.30800770e-02, -4.83432189e-02,  1.26891539e-01,\n",
+      "       -1.80475771e-01, -1.37855738e-01,  1.61161378e-01, -2.38378569e-02,\n",
+      "        7.05108345e-02, -6.50920421e-02,  6.49009272e-02,  2.87603238e-03,\n",
+      "        2.45792121e-02,  5.64777888e-02,  1.05535127e-01, -8.80619362e-02,\n",
+      "       -2.43405942e-02,  5.27076647e-02,  1.44338518e-01,  1.88424165e-04,\n",
+      "       -5.45456521e-02,  2.62364335e-02, -7.24560320e-02,  2.49359254e-02,\n",
+      "       -1.95902586e-02, -1.22845350e-02,  8.02877247e-02, -1.18080072e-01,\n",
+      "       -4.97216135e-02, -1.31015852e-01,  9.49402303e-02, -1.05913930e-01],\n",
+      "      dtype=float32)]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/t2v/t2v.ipynb b/examples/t2v/t2v.ipynb
new file mode 100644
index 00000000..908ff182
--- /dev/null
+++ b/examples/t2v/t2v.ipynb
@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# T2V\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "使用自己提供的任一预训练模型（给出模型存放路径即可）将一组题目的切分序列表征为向量。\n",
+    "\n",
+    "- 优点：模型及其参数可自主调整，灵活性强。\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入功能块"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "from tqdm import tqdm\n",
+    "from EduNLP.SIF.segment import seg\n",
+    "from EduNLP.SIF.tokenization import tokenize\n",
+    "from EduNLP.Pretrain import GensimWordTokenizer\n",
+    "from EduNLP.Vector import T2V"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 输入\n",
+    "\n",
+    "类型：list  \n",
+    "内容：一个题组中每个题目切分序列的组合。\n",
+    "> 这里需要调用 `GensimWordTokenizer` 将题目文本（`str` 类型）转换成 tokens。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "print(type(token_items))\n",
+    "print(type(token_items[0]))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'list'>\n",
+      "<class 'list'>\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "token_items[0]"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['公式',\n",
+       " '[FORMULA]',\n",
+       " '公式',\n",
+       " '[FORMULA]',\n",
+       " '如图',\n",
+       " '[FIGURE]',\n",
+       " 'x',\n",
+       " ',',\n",
+       " 'y',\n",
+       " '约束条件',\n",
+       " '[SEP]',\n",
+       " 'z',\n",
+       " '=',\n",
+       " 'x',\n",
+       " '+',\n",
+       " '7',\n",
+       " 'y',\n",
+       " '最大值',\n",
+       " '[MARK]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "path = \"../test_model/test_gensim_luna_stem_tf_d2v_256.bin\"\n",
+    "t2v = T2V('d2v',filepath=path)\n",
+    "t2v(token_items)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[array([ 0.0256574 ,  0.06061139, -0.00121044, -0.0167674 , -0.0111706 ,\n",
+       "         0.05325712, -0.02097339, -0.01613594,  0.02904145,  0.0185046 ,\n",
+       "         0.03473525,  0.00628165,  0.03696947,  0.00666153, -0.02352318,\n",
+       "        -0.00458236,  0.02308686, -0.02153478,  0.01579256, -0.01575841,\n",
+       "        -0.02654778,  0.01376328,  0.02539059, -0.01098955,  0.02203193,\n",
+       "        -0.01503642,  0.01310026, -0.03569775, -0.00450978,  0.02522727,\n",
+       "        -0.01547103, -0.00907244, -0.00072009, -0.0021727 ,  0.02894731,\n",
+       "         0.01382611,  0.01647377,  0.00452782, -0.02488854,  0.02741116,\n",
+       "         0.0489724 , -0.04156181, -0.00855933,  0.01783935,  0.00704233,\n",
+       "         0.01296936, -0.06078439, -0.04922014, -0.0206639 ,  0.00820663,\n",
+       "         0.02565274,  0.0164784 ,  0.00996537, -0.02215545,  0.06741589,\n",
+       "         0.01634789, -0.0094168 ,  0.00183323,  0.00853508, -0.0547929 ,\n",
+       "         0.00405556,  0.01386227, -0.04204945,  0.02175955, -0.01960315,\n",
+       "        -0.05279269, -0.01511251, -0.02905018, -0.00405249,  0.03328003,\n",
+       "        -0.00487469, -0.00338632,  0.01793213,  0.00942458, -0.02468935,\n",
+       "         0.03548338, -0.00907473,  0.00927462, -0.02545504,  0.02286367,\n",
+       "        -0.01822809,  0.03625014, -0.00976438, -0.00188348,  0.06408882,\n",
+       "        -0.04314236, -0.00193059,  0.02433112, -0.0091018 ,  0.0276503 ,\n",
+       "        -0.0036342 , -0.02485391,  0.02309245,  0.01880057, -0.00893952,\n",
+       "        -0.03391525,  0.02678591, -0.00618519, -0.03601262,  0.0327184 ,\n",
+       "         0.09240578,  0.03631649, -0.00700663, -0.01786321, -0.02987848,\n",
+       "         0.00315695, -0.02082208, -0.00494443, -0.02717963, -0.00938541,\n",
+       "        -0.0329605 ,  0.0069218 ,  0.01227082,  0.00856757, -0.0008222 ,\n",
+       "        -0.0067637 , -0.01577486,  0.0628339 , -0.02329138, -0.00475964,\n",
+       "         0.02197625,  0.03022351,  0.00256966, -0.00247619, -0.01218352,\n",
+       "         0.01257284,  0.0051926 , -0.05297434, -0.0057066 ,  0.01031242,\n",
+       "         0.02414824, -0.0115857 ,  0.01625632, -0.03126714, -0.02389767,\n",
+       "        -0.01417263,  0.02280749, -0.01431546, -0.00771551,  0.0264634 ,\n",
+       "         0.00115387, -0.01903204, -0.00100629,  0.00608774,  0.03787961,\n",
+       "         0.05098663,  0.03064756, -0.00654223, -0.01838502, -0.01889201,\n",
+       "         0.04686983, -0.02295219, -0.00901293,  0.00916024, -0.00013042,\n",
+       "         0.01236307, -0.00918534,  0.01792936,  0.00862702, -0.00018518,\n",
+       "        -0.00566689,  0.00499178,  0.0246148 , -0.0170825 ,  0.01850726,\n",
+       "         0.00031357,  0.02411471,  0.01080729, -0.01361136, -0.06226439,\n",
+       "         0.01830878,  0.01209503, -0.00980596, -0.01865078,  0.03692432,\n",
+       "        -0.04503555,  0.0037965 , -0.04214804, -0.05657932, -0.01566005,\n",
+       "         0.00271924, -0.00026349, -0.00783886,  0.01218421, -0.03205092,\n",
+       "        -0.02793218, -0.00298462,  0.00380523,  0.04471321, -0.02079478,\n",
+       "         0.0100926 ,  0.00450996, -0.03412817,  0.03027697,  0.00872989,\n",
+       "         0.01512562,  0.01527565,  0.03683509,  0.05608684,  0.01055199,\n",
+       "         0.01637757, -0.01995301, -0.01610573,  0.04207385,  0.00058077,\n",
+       "         0.03846577,  0.04952911, -0.02142448,  0.0049874 , -0.00308159,\n",
+       "        -0.02233348,  0.02013967, -0.01194606, -0.02481469,  0.01824989,\n",
+       "        -0.00939436, -0.00374474,  0.02278485,  0.04107878,  0.01870474,\n",
+       "        -0.00310527, -0.00257802, -0.03689042, -0.0200304 , -0.04838364,\n",
+       "         0.0035307 ,  0.02496746, -0.0385387 ,  0.01649689,  0.01429029,\n",
+       "         0.04338812, -0.05614391, -0.01632982,  0.03378268,  0.01393604,\n",
+       "        -0.03859077,  0.01855484,  0.00241599, -0.00985778,  0.00530987,\n",
+       "         0.03700508, -0.06107654, -0.00972089,  0.02251891,  0.01154722,\n",
+       "         0.00913082, -0.0267815 , -0.01723521,  0.0136464 ,  0.01965802,\n",
+       "         0.04769301, -0.02218902, -0.01268643,  0.00650465,  0.00985247,\n",
+       "         0.0029873 ], dtype=float32),\n",
+       " array([ 0.00877787,  0.03242666, -0.00026327, -0.01881958, -0.00730135,\n",
+       "         0.03559063, -0.01825701, -0.01065201,  0.01681685,  0.01074173,\n",
+       "         0.02253641,  0.0082016 ,  0.02200216,  0.00088347, -0.0205142 ,\n",
+       "        -0.01339685,  0.01239092, -0.01781665,  0.01000167, -0.01227449,\n",
+       "        -0.03044926,  0.00296532,  0.01440197, -0.01035894,  0.01061506,\n",
+       "        -0.00530907,  0.00484147, -0.02209524,  0.00735557,  0.01712263,\n",
+       "        -0.00231011, -0.01255511, -0.00114341, -0.01413104,  0.02112199,\n",
+       "         0.01123461,  0.01380601, -0.00019924, -0.02128731,  0.01526375,\n",
+       "         0.02988552, -0.02491145, -0.00939747,  0.00798917,  0.0135474 ,\n",
+       "         0.01258122, -0.03753063, -0.04039029, -0.01517935,  0.00668549,\n",
+       "         0.02796665,  0.01242495,  0.0059546 , -0.01216253,  0.0372387 ,\n",
+       "         0.01762399, -0.00170241,  0.0003667 ,  0.00895109, -0.03517802,\n",
+       "        -0.00762667,  0.01357641, -0.02436312,  0.01829541, -0.01330634,\n",
+       "        -0.02818829, -0.01139517, -0.01664645,  0.00769452,  0.01209339,\n",
+       "        -0.00416979, -0.01296107, -0.0064631 ,  0.0050506 , -0.01833598,\n",
+       "         0.02872021, -0.00062401,  0.0109796 , -0.01280711,  0.01152301,\n",
+       "        -0.01085931,  0.02023655,  0.00272896, -0.00558658,  0.03704501,\n",
+       "        -0.01837787, -0.00414707,  0.00713773, -0.01023714,  0.0090292 ,\n",
+       "         0.00089387, -0.01082103,  0.02051528,  0.01287969, -0.0074691 ,\n",
+       "        -0.01942614,  0.01223695, -0.0136801 , -0.01567431,  0.01466064,\n",
+       "         0.04967042,  0.02889016, -0.005946  , -0.00131571, -0.0110809 ,\n",
+       "         0.00165396, -0.01279759, -0.01407798, -0.01902512, -0.01361593,\n",
+       "        -0.00631681, -0.00142478,  0.01678663,  0.00815052, -0.00193329,\n",
+       "        -0.00845464, -0.00746565,  0.03766166, -0.01099476,  0.00489809,\n",
+       "         0.01403449,  0.01477709, -0.00150515,  0.00462877, -0.01271886,\n",
+       "         0.00072193,  0.00815068, -0.04432011, -0.00604029, -0.00264471,\n",
+       "         0.01325564, -0.01315497,  0.00713541, -0.0137267 , -0.01845939,\n",
+       "        -0.02801731,  0.01673851, -0.00593479, -0.01457028,  0.01636872,\n",
+       "        -0.00751132, -0.01056858,  0.01126528,  0.01645665,  0.02689397,\n",
+       "         0.01920939,  0.01767929, -0.00843761, -0.01002457, -0.00844629,\n",
+       "         0.02888541, -0.00503441, -0.00025836,  0.01326172, -0.00968244,\n",
+       "         0.00430614, -0.00964946,  0.00635843,  0.00445558, -0.00235765,\n",
+       "         0.00160239, -0.00325711,  0.03206096, -0.00511734,  0.01108837,\n",
+       "         0.0014369 ,  0.02616214,  0.01631057, -0.00778238, -0.04322761,\n",
+       "        -0.00086197,  0.01174034, -0.00230315, -0.01354581,  0.01665967,\n",
+       "        -0.02281472, -0.0123808 , -0.02901287, -0.04143119, -0.00477564,\n",
+       "         0.00608404, -0.00701787, -0.00686041,  0.01422733, -0.02854553,\n",
+       "        -0.01464688, -0.00404892,  0.00348112,  0.02299088, -0.02302668,\n",
+       "         0.01208024,  0.01010513, -0.01571813,  0.01446694, -0.00129136,\n",
+       "        -0.00054684, -0.00328883,  0.01649218,  0.03326375, -0.00185443,\n",
+       "         0.02091988, -0.00814938, -0.0088084 ,  0.02302703, -0.01156406,\n",
+       "         0.04080933,  0.02902327, -0.01330268, -0.00385899, -0.00826302,\n",
+       "        -0.02295679,  0.00658087, -0.0056047 , -0.01404469,  0.00368797,\n",
+       "        -0.01484573,  0.00689151,  0.02035506,  0.02181732,  0.02151672,\n",
+       "         0.0004279 , -0.00763045, -0.01551796, -0.02054572, -0.03275407,\n",
+       "         0.00623783,  0.007831  , -0.02604559,  0.01956206,  0.0161521 ,\n",
+       "         0.02634443, -0.03285164, -0.01301691,  0.01066694,  0.01585914,\n",
+       "        -0.0187955 ,  0.01046878, -0.00189302, -0.01132144, -0.00140048,\n",
+       "         0.02645635, -0.04300842, -0.00639437,  0.01285532, -0.00437311,\n",
+       "         0.01163111, -0.015357  , -0.00531165,  0.01102756,  0.00182517,\n",
+       "         0.02303016, -0.00949884, -0.02009463,  0.00573564,  0.00076009,\n",
+       "         0.00078505], dtype=float32)]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/t2v/t2v.py b/examples/t2v/t2v.py
deleted file mode 100644
index 279bbcc9..00000000
--- a/examples/t2v/t2v.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from EduNLP.Vector import get_pretrained_t2v
-import json
-from tqdm import tqdm
-from EduNLP.SIF.segment import seg
-from EduNLP.SIF.tokenization import tokenize
-from EduNLP.Pretrain import GensimWordTokenizer
-
-def load_items():
-    test_items = [
-        {'ques_content':'有公式$\\FormFigureID{wrong1?}$和公式$\\FormFigureBase64{wrong2?}$，如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'},
-        {'ques_content':'如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'},
-        {'ques_content':'<div>Below is a discussion on a website.<br><table border=\1'},
-    ]
-    for line in test_items:
-        yield line
-    # """or use your jsonfile like this"""
-    # work_file_path = "../../../data/OpenLUNA.json"
-    # with open(work_file_path, 'r', encoding="utf-8") as f:
-    #     for line in f:
-    #         yield json.loads(line)
-
-
-token_items = []
-for item in tqdm(load_items(), "sifing"):
-    # transform content into special marks('g','m','a','s'), except text('t') and formula('f').
-    # 'general' means symbolize the Formula in figure format and use 'linear' method for formula segmentation
-    tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
-    token_item = tokenizer(item["ques_content"])
-    if token_item:
-        token_items.append(token_item.tokens)
-
-# make a model -> t2v
-t2v = get_pretrained_t2v("d2v_eng_256")
-print(t2v(token_items))
\ No newline at end of file
diff --git a/examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin b/examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin
new file mode 100644
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literal 0
HcmV?d00001


From ee09181f96a9409b71765d0238e7191b4bd7dee7 Mon Sep 17 00:00:00 2001
From: karin0018 <karin0018@users.noreply.github.com>
Date: Wed, 11 Aug 2021 15:50:36 +0800
Subject: [PATCH 003/149] [examples] update i2v

---
 examples/i2v/get_pretrained_i2v.ipynb | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/examples/i2v/get_pretrained_i2v.ipynb b/examples/i2v/get_pretrained_i2v.ipynb
index 6c1ee0ff..8708a3d3 100644
--- a/examples/i2v/get_pretrained_i2v.ipynb
+++ b/examples/i2v/get_pretrained_i2v.ipynb
@@ -70,13 +70,20 @@
   {
    "cell_type": "markdown",
    "source": [
-    "根据题目所属学科选择预训练模型：  \n",
-    "| 预训练模型名称 | 模型训练数据的所属学科 |\n",
-    "| -------------- | ---------------------- |\n",
-    "| d2v_all_256    | 全学科                 |\n",
-    "| d2v_sci_256    | 理科                   |\n",
-    "| d2v_eng_256    | 英语                   |\n",
-    "| d2v_lit_256    | 文科                   |\n"
+    "根据题目所属学科选择预训练模型：    \n",
+    "\n",
+    "| 预训练模型名称 | 模型训练数据的所属学科 |  \n",
+    "\n",
+    "| -------------- | ---------------------- |  \n",
+    "\n",
+    "| d2v_all_256    | 全学科                 |  \n",
+    "\n",
+    "| d2v_sci_256    | 理科                   |  \n",
+    "\n",
+    "| d2v_eng_256    | 英语                   |  \n",
+    "\n",
+    "| d2v_lit_256    | 文科                   |   \n",
+    "\n"
    ],
    "metadata": {}
   },

From 460a852f864ac42c05f2b3868a6778f5e15c2357 Mon Sep 17 00:00:00 2001
From: karin0018 <karin0018@users.noreply.github.com>
Date: Wed, 11 Aug 2021 16:44:32 +0800
Subject: [PATCH 004/149] [examples] update i2v

---
 examples/i2v/get_pretrained_i2v.ipynb | 17 ++++++-----------
 1 file changed, 6 insertions(+), 11 deletions(-)

diff --git a/examples/i2v/get_pretrained_i2v.ipynb b/examples/i2v/get_pretrained_i2v.ipynb
index 8708a3d3..2e600423 100644
--- a/examples/i2v/get_pretrained_i2v.ipynb
+++ b/examples/i2v/get_pretrained_i2v.ipynb
@@ -72,17 +72,12 @@
    "source": [
     "根据题目所属学科选择预训练模型：    \n",
     "\n",
-    "| 预训练模型名称 | 模型训练数据的所属学科 |  \n",
-    "\n",
-    "| -------------- | ---------------------- |  \n",
-    "\n",
-    "| d2v_all_256    | 全学科                 |  \n",
-    "\n",
-    "| d2v_sci_256    | 理科                   |  \n",
-    "\n",
-    "| d2v_eng_256    | 英语                   |  \n",
-    "\n",
-    "| d2v_lit_256    | 文科                   |   \n",
+    " 预训练模型名称 | 模型训练数据的所属学科   \n",
+    " -------------- | ----------------------   \n",
+    " d2v_all_256    | 全学科                   \n",
+    " d2v_sci_256    | 理科                     \n",
+    " d2v_eng_256    | 英语                     \n",
+    " d2v_lit_256    | 文科                      \n",
     "\n"
    ],
    "metadata": {}

From 71a77d49c93f818ee23ca9cbbe3dde7c269382cf Mon Sep 17 00:00:00 2001
From: karin0018 <karin0018@users.noreply.github.com>
Date: Sun, 15 Aug 2021 14:20:24 +0800
Subject: [PATCH 005/149] [other] delete warnings

---
 EduNLP/Formula/viz/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/EduNLP/Formula/viz/__init__.py b/EduNLP/Formula/viz/__init__.py
index 2d0ba898..a461dca7 100644
--- a/EduNLP/Formula/viz/__init__.py
+++ b/EduNLP/Formula/viz/__init__.py
@@ -2,5 +2,5 @@
 # 2021/3/8 @ tongshiwei
 
 import warnings
-warnings.warn("Do not use this package")
+# warnings.warn("Do not use this package")
 from .tree_viz import TreePlotter, ForestPlotter

From f3d2fefd498fe70a57324bafdf9abc419e75edf2 Mon Sep 17 00:00:00 2001
From: karin0018 <karin0018@users.noreply.github.com>
Date: Sun, 15 Aug 2021 14:21:43 +0800
Subject: [PATCH 006/149] [examples] update formula

---
 examples/formula/formula.ipynb | 17 +++--------------
 1 file changed, 3 insertions(+), 14 deletions(-)

diff --git a/examples/formula/formula.ipynb b/examples/formula/formula.ipynb
index 794ed7da..6a200367 100644
--- a/examples/formula/formula.ipynb
+++ b/examples/formula/formula.ipynb
@@ -22,18 +22,7 @@
     "from EduNLP.Formula import FormulaGroup\n",
     "from EduNLP.Formula.viz import ForestPlotter"
    ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n",
-      "/home/lvrui/.local/lib/python3.8/site-packages/EduNLP/Formula/viz/__init__.py:5: UserWarning: Do not use this package\n",
-      "  warnings.warn(\"Do not use this package\")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "metadata": {}
   },
   {
@@ -74,7 +63,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "查看公式切分后的结点元素:"
+    "- 查看公式切分后的结点元素:"
    ],
    "metadata": {}
   },
@@ -109,7 +98,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "查看公式的抽象语法分析树："
+    "- 查看公式的抽象语法分析树："
    ],
    "metadata": {}
   },

From affbf4552de3ae3d4b0185adb8fc483e26ec7ad7 Mon Sep 17 00:00:00 2001
From: karin0018 <karin0018@users.noreply.github.com>
Date: Sun, 15 Aug 2021 14:22:55 +0800
Subject: [PATCH 007/149] [examples] update Tokenizer

---
 examples/Tokenizer/test_stopwords.txt |   9 ++
 examples/Tokenizer/tokenizer.ipynb    | 166 ++++++++++++++++++++++----
 2 files changed, 155 insertions(+), 20 deletions(-)
 create mode 100644 examples/Tokenizer/test_stopwords.txt

diff --git a/examples/Tokenizer/test_stopwords.txt b/examples/Tokenizer/test_stopwords.txt
new file mode 100644
index 00000000..8183ecf4
--- /dev/null
+++ b/examples/Tokenizer/test_stopwords.txt
@@ -0,0 +1,9 @@
+一旦
+一时
+一来
+一样
+一次
+一片
+一番
+一直
+一致
\ No newline at end of file
diff --git a/examples/Tokenizer/tokenizer.ipynb b/examples/Tokenizer/tokenizer.ipynb
index d21d7408..042aaa9a 100644
--- a/examples/Tokenizer/tokenizer.ipynb
+++ b/examples/Tokenizer/tokenizer.ipynb
@@ -7,48 +7,174 @@
     "\n",
     "## 概述\n",
     "\n",
-    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目文本转换成令牌序列。按照试题文本类型分为以下四部分：   \n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
     "\n",
-    "1. 分句（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
-    "  \n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\n",
     "\n",
+    "### 文本解析\n",
     "\n",
-    "2. 标记解析（text-tokenization）：一个句子（不含公式）是由若干“标记”按顺序构成的，将一个句子切分为若干标记的过程称为“标记解析”。每个标记为一个“令牌”（token）。    \n",
+    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\n",
     "\n",
-    "3. 公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \n",
+    "1. 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
     "  \n",
-    "4. 综合解析（item-tokenization）：将带公式的句子切分为若干标记的过程。每个标记为一个“令牌”（token）。    \n"
+    "\n",
+    "2. 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\n",
+    "    - 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\n",
+    "    - 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\n",
+    "\n",
+    "### 公式解析\n",
+    "\n",
+    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \n",
+    "  "
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "## 标记解析\n",
+    "## 词解析\n",
     "\n",
-    "先使用 `jieba` 分词工具得到分词序列，再过滤掉停用词 -  [stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n"
+    "词解析分为两个主要步骤：\n",
+    "1. 分词：  \n",
+    "    - 词组解析：使用分词工具切分并提取题目文本中的词。  \n",
+    "    本项目目前支持的分词工具有：`jieba`   \n",
+    "    - 单字解析：按字符划分。\n",
+    "    \n",
+    "      \n",
+    "2. 筛选：过滤指定的停用词。   \n",
+    "    本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n",
+    "    你也可以使用自己的停用词表，具体使用方法见下面的示例。\n"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 1,
    "source": [
     "# 导入模块\n",
-    "from EduNLP.SIF.tokenization.text import tokenize \n",
-    "\n",
+    "from EduNLP.SIF.tokenization.text import tokenize "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
     "# 输入\n",
-    "text = \"三角函数是基本初等函数之一\"\n",
-    "# 输出\n",
-    "print(tokenize(item))"
+    "text = \"三角函数是基本初等函数之一\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 词组解析\n",
+    "\n",
+    "分词粒度参数选择 word： `granularity = \"word\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
+    "tokenize(text, granularity=\"word\")"
    ],
    "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "['三角函数', '初等', '函数']\n"
-     ]
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三角函数', '初等', '函数']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 单字解析\n",
+    "\n",
+    "分词粒度参数选择 word： `granularity = \"char\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
+    "tokenize(text, granularity=\"char\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三', '角', '函', '数', '基', '初', '函', '数']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 停用词表"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "source": [
+    "# 获取自己的停用词表\n",
+    "spath = \"test_stopwords.txt\"\n",
+    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\n",
+    "stopwords = get_stopwords(spath)\n",
+    "stopwords"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# 输出：传入停用词表（stopwords）\n",
+    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三角函数', '是', '基本', '初等', '函数', '之一']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
     }
    ],
    "metadata": {}
@@ -57,7 +183,7 @@
    "cell_type": "markdown",
    "source": [
     "## 公式解析\n",
-    "切分出 latex 公式的每个标记符号。"
+    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
    ],
    "metadata": {}
   },

From ca6dfeb86a297ebd37f53ba47d9fd197ed70c615 Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Fri, 20 Aug 2021 18:10:39 +0800
Subject: [PATCH 008/149] [examples] add some detailed descriptions

---
 examples/Tokenizer/tokenizer.ipynb    | 224 +++++++++++++++++++++-----
 examples/formula/formula.ipynb        |   2 +-
 examples/i2v/get_pretrained_i2v.ipynb | 183 +++++++++++----------
 examples/i2v/i2v.ipynb                |  11 +-
 examples/t2v/get_pretrained_t2v.ipynb | 157 +++---------------
 5 files changed, 298 insertions(+), 279 deletions(-)

diff --git a/examples/Tokenizer/tokenizer.ipynb b/examples/Tokenizer/tokenizer.ipynb
index 042aaa9a..6edeb97c 100644
--- a/examples/Tokenizer/tokenizer.ipynb
+++ b/examples/Tokenizer/tokenizer.ipynb
@@ -32,7 +32,23 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 词解析\n",
+    "## 文本解析"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 句解析\n",
+    "\n",
+    "待实现..."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 词解析\n",
     "\n",
     "词解析分为两个主要步骤：\n",
     "1. 分词：  \n",
@@ -59,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "source": [
     "# 输入\n",
     "text = \"三角函数是基本初等函数之一\""
@@ -70,7 +86,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 词组解析\n",
+    "#### 词组解析\n",
     "\n",
     "分词粒度参数选择 word： `granularity = \"word\"` "
    ],
@@ -78,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "source": [
     "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
     "tokenize(text, granularity=\"word\")"
@@ -92,7 +108,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 10
+     "execution_count": 3
     }
    ],
    "metadata": {}
@@ -100,7 +116,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 单字解析\n",
+    "#### 单字解析\n",
     "\n",
     "分词粒度参数选择 word： `granularity = \"char\"` "
    ],
@@ -108,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "source": [
     "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
     "tokenize(text, granularity=\"char\")"
@@ -122,7 +138,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 14
+     "execution_count": 4
     }
    ],
    "metadata": {}
@@ -130,13 +146,13 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 停用词表"
+    "#### 停用词表"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "source": [
     "# 获取自己的停用词表\n",
     "spath = \"test_stopwords.txt\"\n",
@@ -153,14 +169,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 15
+     "execution_count": 5
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "source": [
     "# 输出：传入停用词表（stopwords）\n",
     "tokenize(text,granularity=\"word\",stopwords=stopwords)"
@@ -174,7 +190,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 8
+     "execution_count": 6
     }
    ],
    "metadata": {}
@@ -189,39 +205,167 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 7,
    "source": [
     "# 导入模块\n",
-    "from EduNLP.SIF.tokenization.formula import tokenize\n",
-    "\n",
-    "# 输入\n",
-    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\"\n",
-    "\n",
-    "# 输出\n",
-    "\n",
-    "# 输出形式选择普通序列（linear）\n",
-    "print('linear: ',tokenize(formula,method=\"linear\"))\n",
-    "\n",
-    "# 输出形式选择抽象语法分析树（ast）\n",
-    "print('ast : ',tokenize(formula,method=\"ast\",return_type = \"list\", ord2token=False))\n",
-    "\n",
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token \n",
-    "print('ast & ord2token: ',tokenize(formula,method=\"ast\",return_type = \"list\", ord2token=True))\n",
+    "from EduNLP.SIF.tokenization.formula import tokenize"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输入"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "1. 如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "tokenize(formula, method=\"linear\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "2. 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "    > 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
     "\n",
+    "    > 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "3. 如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " '{ }',\n",
+       " 'mathord',\n",
+       " '+',\n",
+       " 'mathord',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "4. 如果您除了 `3.` 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
     "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\n",
-    "print('ast & ord2token & var_numbering: ',tokenize(formula,method=\"ast\",return_type = \"list\", ord2token=True, var_numbering=True))\n",
-    "\n"
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
    ],
    "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "linear:  ['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']\n",
-      "ast :  ['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']\n",
-      "ast & ord2token:  ['mathord', '{ }', 'mathord', '+', 'mathord', '{ }', '\\\\frac', '+', 'textord', '=', 'mathord']\n",
-      "ast & ord2token & var_numbering:  ['mathord_con', '{ }', 'mathord_0', '+', 'mathord_1', '{ }', '\\\\frac', '+', 'textord', '=', 'mathord_0']\n"
-     ]
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord_con',\n",
+       " '{ }',\n",
+       " 'mathord_0',\n",
+       " '+',\n",
+       " 'mathord_1',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord_0']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
     }
    ],
    "metadata": {}
@@ -231,7 +375,7 @@
    "source": [
     "## 综合解析\n",
     "\n",
-    "标记解析 + 公式解析。特殊符号将转换成常量，例如：\n",
+    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\n",
     "```python\n",
     "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\n",
     "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\n",
diff --git a/examples/formula/formula.ipynb b/examples/formula/formula.ipynb
index 6a200367..f748a90a 100644
--- a/examples/formula/formula.ipynb
+++ b/examples/formula/formula.ipynb
@@ -7,7 +7,7 @@
     "\n",
     "## 概述\n",
     "\n",
-    "Formula 首先在分词功能中（SIF/segment ==> SIF/tokenization）中对原始文本的公式做切分处理，之后在 Formula/ast 中提供 [公式解析树] 功能，可以将数学公式的抽象语法分析树用文本或图片的形式表示出来。  \n",
+    "Formula 首先在分词功能中对原始文本的公式做切分处理，另外提供 [公式解析树] 功能，可以将数学公式的抽象语法分析树用文本或图片的形式表示出来。  \n",
     "\n",
     "本模块另提供公式变量标准化的功能，如判断几个子公式内的‘x’为同一变量。"
    ],
diff --git a/examples/i2v/get_pretrained_i2v.ipynb b/examples/i2v/get_pretrained_i2v.ipynb
index 2e600423..9fe707b7 100644
--- a/examples/i2v/get_pretrained_i2v.ipynb
+++ b/examples/i2v/get_pretrained_i2v.ipynb
@@ -27,16 +27,7 @@
    "source": [
     "from EduNLP import get_pretrained_i2v"
    ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "metadata": {}
   },
   {
@@ -84,10 +75,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "source": [
-    "i2v = get_pretrained_i2v(\"d2v_sci_256\")\n",
-    "print(i2v(item))"
+    "i2v = get_pretrained_i2v(\"d2v_sci_256\")"
    ],
    "outputs": [
     {
@@ -95,91 +85,98 @@
      "name": "stderr",
      "text": [
       "EduNLP, INFO Use pretrained t2v model d2v_sci_256\n",
-      "downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as /home/lvrui/.EduNLP/model/general_science_256.zip\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Downloading /home/lvrui/.EduNLP/model/general_science_256.zip 100.00%: 1.90GB | 1.90GB\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "downloader, INFO /home/lvrui/.EduNLP/model/general_science_256.zip is unzip to /home/lvrui/.EduNLP/model/general_science_256\n"
+      "downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as /home/lvrui/.EduNLP/model/general_science_256.zip\n",
+      "downloader, INFO file existed, skipped\n"
      ]
-    },
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 注意：\n",
+    "    默认的 EduNLP 项目存储地址为根目录(`~/.EduNLP`)，模型存储地址为项目存储地址下的 `model` 文件夹。您可以通过修改下面的环境变量来修改模型存储地址：\n",
+    "    - EduNLP 项目存储地址：`EDUNLPPATH = xx/xx/xx`\n",
+    "    - 模型存储地址：`EDUNLPMODELPATH = xx/xx/xx`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "print(i2v(item))"
+   ],
+   "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "([array([-2.38646612e-01,  6.78282678e-02, -2.70181447e-01,  1.64666966e-01,\n",
-      "        3.01246569e-02, -1.82321504e-01,  9.00692567e-02,  1.39259361e-02,\n",
-      "        8.87747705e-02,  3.15691501e-01,  9.60902050e-02, -2.27211684e-01,\n",
-      "       -2.58575261e-01,  6.71877190e-02,  9.72832963e-02, -1.98090047e-01,\n",
-      "        8.28761086e-02, -2.25134045e-01, -1.76834762e-01, -3.76207419e-02,\n",
-      "        6.22433126e-01, -2.67150372e-01,  8.10120702e-02, -1.59442604e-01,\n",
-      "       -1.80032104e-01, -1.31213591e-01,  2.74172515e-01,  1.82820112e-01,\n",
-      "        8.61180499e-02,  2.31776193e-01,  1.79873109e-01,  1.53902888e-01,\n",
-      "       -1.38063684e-01, -1.95138857e-01, -1.09946504e-01,  1.13749191e-01,\n",
-      "        2.26243772e-02,  7.80012235e-02, -2.41019651e-01,  6.24938346e-02,\n",
-      "       -4.91447784e-02, -2.19206046e-02,  7.43985176e-02, -1.93959504e-01,\n",
-      "        9.67528448e-02,  4.00890917e-01,  2.80190617e-01,  3.05524945e-01,\n",
-      "       -4.65332866e-01, -2.88085137e-02, -1.95519075e-01,  1.81624573e-02,\n",
-      "       -2.14567229e-01,  9.81318355e-02, -2.60762572e-01, -2.37531736e-01,\n",
-      "        3.44704688e-01,  1.50553077e-01, -2.98939735e-01, -3.80659550e-01,\n",
-      "       -1.21090114e-01,  5.90847358e-02,  6.49232268e-02,  1.01377197e-01,\n",
-      "        1.22882634e-01,  1.31840616e-01,  1.26869485e-01, -1.54032171e-01,\n",
-      "        6.84209634e-03, -1.34808928e-01, -1.51006639e-01,  1.13991253e-01,\n",
-      "       -2.44503647e-01, -8.86185318e-02,  5.08614145e-02, -6.05630055e-02,\n",
-      "        3.02319266e-02, -5.88020943e-02, -4.33565304e-02,  3.18464190e-01,\n",
-      "       -2.43573096e-02,  7.20316470e-02, -1.32487357e-01,  1.58072487e-02,\n",
-      "       -1.33229955e-03, -2.00035229e-01, -8.04887619e-03, -2.81720281e-01,\n",
-      "       -1.64224923e-01, -4.84411605e-02, -2.24565342e-01, -1.70706406e-01,\n",
-      "        1.45024151e-01, -1.30645692e-01, -1.84055284e-01, -6.77889064e-02,\n",
-      "       -2.18218490e-01, -7.54996613e-02,  1.74331084e-01,  4.51817252e-02,\n",
-      "        7.11327717e-02,  8.20667148e-02, -1.54022738e-01, -2.01157495e-01,\n",
-      "        1.16600037e-01,  9.50898379e-02, -1.08904034e-01, -1.51480287e-02,\n",
-      "       -8.67420509e-02, -5.53172417e-02, -9.68716219e-02,  7.35177752e-03,\n",
-      "        6.65248930e-02, -2.26601094e-01,  6.92174658e-02,  1.34601086e-01,\n",
-      "        2.25853592e-01,  1.34206057e-01, -8.84069949e-02,  1.41585499e-01,\n",
-      "        2.43607357e-01,  8.15536641e-03, -6.81986213e-02,  2.50327110e-01,\n",
-      "        1.03366420e-01, -6.21427223e-02,  5.28883487e-02,  7.18744704e-03,\n",
-      "       -1.06151044e-01, -9.02994908e-03,  1.86166000e-02,  7.66558200e-02,\n",
-      "       -2.26106957e-01,  3.27168033e-02,  1.91411540e-01,  2.77558297e-01,\n",
-      "       -1.65268779e-01,  5.86843975e-02,  9.81277376e-02,  3.20868284e-01,\n",
-      "       -2.57624593e-02, -2.09616646e-01,  1.14249721e-01,  1.22667916e-01,\n",
-      "       -8.78639799e-03, -3.97502147e-02, -3.51181030e-02,  1.36992261e-01,\n",
-      "        4.27117229e-01,  1.43339723e-01, -1.52684569e-01,  7.94557929e-02,\n",
-      "        1.40015110e-01,  1.05225910e-02,  1.68755472e-01, -4.86548431e-02,\n",
-      "        5.86069599e-02,  9.45951939e-02,  1.88357439e-02,  1.42038196e-01,\n",
-      "        4.01655644e-01,  6.68841675e-02, -5.52068591e-01, -1.85107216e-01,\n",
-      "        1.11172728e-01, -6.88473955e-02,  9.25668925e-02,  2.25377038e-01,\n",
-      "       -1.70589730e-01,  2.10852250e-01,  4.33011979e-01, -3.39060128e-01,\n",
-      "        7.44158542e-03, -1.47019088e-01, -4.82755564e-02,  8.07425156e-02,\n",
-      "        1.82245448e-01, -6.77390322e-02, -5.62187284e-02,  1.52256146e-01,\n",
-      "        2.22834423e-01,  3.34665984e-01, -2.60272548e-02,  1.03390038e-01,\n",
-      "       -3.96476947e-02,  1.40223369e-01, -3.94263297e-01, -4.81414124e-02,\n",
-      "       -9.04471427e-02, -1.01487234e-01,  3.86349201e-01, -4.08536009e-02,\n",
-      "        4.17221561e-02, -3.44486445e-01,  2.28582658e-02,  8.65597930e-03,\n",
-      "        9.48065370e-02,  1.13664225e-01,  7.57174492e-02, -6.70918226e-02,\n",
-      "       -2.76283026e-01, -2.82264426e-02,  1.18102543e-01, -5.32101169e-02,\n",
-      "       -2.39996426e-02,  2.78141703e-02,  2.07452640e-01, -9.74735692e-02,\n",
-      "        8.04340020e-02,  4.25180979e-02, -1.34585306e-01,  2.18971297e-01,\n",
-      "        1.17185367e-02, -1.18722573e-01,  2.41390932e-02,  1.29328389e-02,\n",
-      "       -7.56185055e-02, -1.14210650e-01,  7.66615868e-02, -2.96691954e-02,\n",
-      "        1.80785861e-02, -2.76242346e-01, -2.63213754e-01, -2.42437288e-01,\n",
-      "        1.77747458e-01,  4.11453784e-01, -6.25603944e-02,  2.83305943e-01,\n",
-      "        9.83239859e-02, -2.67412305e-01,  1.50755286e-01,  2.54022274e-02,\n",
-      "        1.23227865e-01,  7.44485529e-03,  8.24907571e-02, -2.45544506e-04,\n",
-      "       -1.21799987e-02,  4.10213545e-02, -2.60983467e-01, -9.83531028e-02,\n",
-      "       -1.91514686e-01, -2.42492985e-02, -2.21523643e-01, -4.43225615e-02,\n",
-      "        9.25253630e-02, -5.05034365e-02,  2.15248734e-01,  2.56709158e-01,\n",
-      "        6.49087429e-02, -8.12986307e-03,  1.28661385e-02, -2.32116710e-02,\n",
-      "        1.69111729e-01,  2.24174489e-03,  2.37702616e-02, -1.39817741e-04],\n",
+      "([array([-2.38860980e-01,  7.09681511e-02, -2.71706015e-01,  1.64714813e-01,\n",
+      "        2.81243492e-02, -1.82386801e-01,  9.22331214e-02,  1.31783364e-02,\n",
+      "        9.15176645e-02,  3.14464062e-01,  9.37800854e-02, -2.28523940e-01,\n",
+      "       -2.60597020e-01,  6.49375990e-02,  9.75619778e-02, -1.97933778e-01,\n",
+      "        8.29798505e-02, -2.26491719e-01, -1.77030653e-01, -3.56038064e-02,\n",
+      "        6.22844934e-01, -2.66110301e-01,  8.00080523e-02, -1.60827965e-01,\n",
+      "       -1.78654417e-01, -1.33000776e-01,  2.76004016e-01,  1.79546073e-01,\n",
+      "        8.71006995e-02,  2.33958483e-01,  1.76031828e-01,  1.55402005e-01,\n",
+      "       -1.38987333e-01, -1.92975491e-01, -1.09528497e-01,  1.12305783e-01,\n",
+      "        2.32549626e-02,  7.75609687e-02, -2.43636876e-01,  6.35311157e-02,\n",
+      "       -4.82399836e-02, -2.24204548e-02,  7.49862418e-02, -1.91449642e-01,\n",
+      "        9.72701237e-02,  4.00750965e-01,  2.81992704e-01,  3.07581365e-01,\n",
+      "       -4.68867749e-01, -3.03025767e-02, -1.95257351e-01,  1.79073047e-02,\n",
+      "       -2.15334237e-01,  9.98005569e-02, -2.62755096e-01, -2.39337608e-01,\n",
+      "        3.44270498e-01,  1.50241479e-01, -2.96006531e-01, -3.81666899e-01,\n",
+      "       -1.19041964e-01,  6.18071109e-02,  6.49120063e-02,  9.94637012e-02,\n",
+      "        1.23297565e-01,  1.29930690e-01,  1.27305657e-01, -1.53804764e-01,\n",
+      "        7.04720244e-03, -1.33500487e-01, -1.51161134e-01,  1.13862932e-01,\n",
+      "       -2.44814962e-01, -8.95622373e-02,  4.76458520e-02, -5.92206642e-02,\n",
+      "        2.88407020e-02, -5.88610955e-02, -4.25557904e-02,  3.20446432e-01,\n",
+      "       -2.61463765e-02,  7.19539896e-02, -1.32161498e-01,  1.62227061e-02,\n",
+      "        1.20197656e-03, -2.03355268e-01, -6.83294982e-03, -2.82588631e-01,\n",
+      "       -1.61395460e-01, -5.05547188e-02, -2.27462381e-01, -1.70932785e-01,\n",
+      "        1.41351461e-01, -1.30069017e-01, -1.83039993e-01, -6.79691881e-02,\n",
+      "       -2.15642393e-01, -7.84436688e-02,  1.77202985e-01,  4.50607650e-02,\n",
+      "        7.02605024e-02,  8.01992565e-02, -1.55584306e-01, -2.00563252e-01,\n",
+      "        1.17082551e-01,  9.73844752e-02, -1.10356934e-01, -1.37866074e-02,\n",
+      "       -8.57235789e-02, -5.56467362e-02, -9.36827138e-02,  6.82030804e-03,\n",
+      "        6.92379624e-02, -2.28701755e-01,  6.70390204e-02,  1.34586483e-01,\n",
+      "        2.25231394e-01,  1.33322045e-01, -8.82911906e-02,  1.42205298e-01,\n",
+      "        2.41012901e-01,  7.94170424e-03, -7.02124536e-02,  2.51370400e-01,\n",
+      "        1.04983136e-01, -6.39194548e-02,  5.24720028e-02,  7.16757867e-03,\n",
+      "       -1.08169973e-01, -1.08731678e-02,  1.69618204e-02,  7.87692815e-02,\n",
+      "       -2.26539060e-01,  3.29003595e-02,  1.91522852e-01,  2.75921494e-01,\n",
+      "       -1.64055750e-01,  5.83723187e-02,  9.84422341e-02,  3.21688712e-01,\n",
+      "       -2.62310840e-02, -2.08140060e-01,  1.14425711e-01,  1.23823956e-01,\n",
+      "       -8.62085819e-03, -4.14005108e-02, -3.41566652e-02,  1.34680912e-01,\n",
+      "        4.27634180e-01,  1.42883554e-01, -1.54787973e-01,  7.96157196e-02,\n",
+      "        1.40678003e-01,  1.39171826e-02,  1.66003749e-01, -4.85638082e-02,\n",
+      "        5.88261709e-02,  9.51106697e-02,  1.81014258e-02,  1.44485429e-01,\n",
+      "        4.01205927e-01,  6.77596256e-02, -5.52676022e-01, -1.87850371e-01,\n",
+      "        1.12366609e-01, -6.84190989e-02,  9.48949978e-02,  2.23454669e-01,\n",
+      "       -1.69843137e-01,  2.09085494e-01,  4.29946512e-01, -3.36349100e-01,\n",
+      "        6.12608856e-03, -1.46142125e-01, -5.11092655e-02,  8.06671828e-02,\n",
+      "        1.81744993e-01, -6.78945482e-02, -5.77093139e-02,  1.52337164e-01,\n",
+      "        2.21259117e-01,  3.35705757e-01, -2.51778495e-02,  1.03662543e-01,\n",
+      "       -4.21361588e-02,  1.43061429e-01, -3.92947495e-01, -4.89463992e-02,\n",
+      "       -9.15660262e-02, -1.00108273e-01,  3.86523217e-01, -4.25569601e-02,\n",
+      "        4.10154127e-02, -3.41399819e-01,  2.13903114e-02,  8.09015241e-03,\n",
+      "        9.56344381e-02,  1.12729572e-01,  7.25207478e-02, -6.64384067e-02,\n",
+      "       -2.73666024e-01, -2.79651750e-02,  1.18422434e-01, -5.22459708e-02,\n",
+      "       -2.47057881e-02,  2.84700710e-02,  2.07451075e-01, -9.74238589e-02,\n",
+      "        8.08936954e-02,  4.07307222e-02, -1.35277033e-01,  2.18436554e-01,\n",
+      "        1.28792310e-02, -1.20433331e-01,  2.41929386e-02,  1.28128864e-02,\n",
+      "       -7.39881098e-02, -1.12995692e-01,  7.69245178e-02, -2.87000872e-02,\n",
+      "        1.64782573e-02, -2.78794408e-01, -2.64403820e-01, -2.43874848e-01,\n",
+      "        1.77457914e-01,  4.11631197e-01, -6.09753132e-02,  2.84967333e-01,\n",
+      "        9.81074646e-02, -2.68213183e-01,  1.52153388e-01,  2.42148209e-02,\n",
+      "        1.24371536e-01,  6.02926640e-03,  8.22689310e-02,  2.82294262e-04,\n",
+      "       -1.40584474e-02,  4.09389734e-02, -2.58334547e-01, -9.83026102e-02,\n",
+      "       -1.91695184e-01, -2.61005852e-02, -2.21736208e-01, -4.36628833e-02,\n",
+      "        9.49840024e-02, -5.16017936e-02,  2.17577979e-01,  2.58604765e-01,\n",
+      "        6.33814484e-02, -7.10158283e-03,  9.87893157e-03, -2.26405971e-02,\n",
+      "        1.67435139e-01,  2.90897069e-03,  2.35914681e-02,  5.43428905e-06],\n",
       "      dtype=float32)], None)\n"
      ]
     }
diff --git a/examples/i2v/i2v.ipynb b/examples/i2v/i2v.ipynb
index f6c901f1..507c994e 100644
--- a/examples/i2v/i2v.ipynb
+++ b/examples/i2v/i2v.ipynb
@@ -26,16 +26,7 @@
    "source": [
     "from EduNLP.I2V import D2V"
    ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "metadata": {}
   },
   {
diff --git a/examples/t2v/get_pretrained_t2v.ipynb b/examples/t2v/get_pretrained_t2v.ipynb
index 7a9b9acf..c0982e81 100644
--- a/examples/t2v/get_pretrained_t2v.ipynb
+++ b/examples/t2v/get_pretrained_t2v.ipynb
@@ -31,16 +31,7 @@
     "from EduNLP.Pretrain import GensimWordTokenizer\n",
     "from EduNLP.Vector import get_pretrained_t2v"
    ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "metadata": {}
   },
   {
@@ -82,7 +73,7 @@
      "output_type": "stream",
      "name": "stderr",
      "text": [
-      "sifing: 3it [00:01,  1.70it/s]\n"
+      "sifing: 3it [00:01,  1.69it/s]\n"
      ]
     }
    ],
@@ -108,8 +99,7 @@
    "execution_count": 3,
    "source": [
     "# make a model -> t2v\n",
-    "t2v = get_pretrained_t2v(\"d2v_sci_256\")\n",
-    "print(t2v(token_items))"
+    "t2v = get_pretrained_t2v(\"d2v_sci_256\")"
    ],
    "outputs": [
     {
@@ -119,131 +109,28 @@
       "downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as /home/lvrui/.EduNLP/model/general_science_256.zip\n",
       "downloader, INFO file existed, skipped\n"
      ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "[array([ 0.06439726, -0.00687894,  0.00189043, -0.09257554,  0.05736929,\n",
-      "       -0.11668563, -0.10532057, -0.05405452, -0.0999706 ,  0.06865267,\n",
-      "       -0.02944315,  0.01962002, -0.05162324,  0.20003118,  0.0697258 ,\n",
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-      "        0.17466484,  0.06978347, -0.04626915,  0.0546972 ,  0.04325071,\n",
-      "        0.08251449,  0.02235107,  0.03021882,  0.06331849,  0.047343  ,\n",
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-      "        0.0398024 , -0.05226437, -0.08767968,  0.05370751, -0.0163406 ,\n",
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-      "       -0.03500445,  0.0698294 ,  0.12072821,  0.05175976, -0.07125726,\n",
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-      "       -0.01352143,  0.03196577, -0.02445628, -0.00369316,  0.08269379,\n",
-      "        0.01450656,  0.00063832, -0.03835842,  0.04345626,  0.01187227,\n",
-      "       -0.00093305,  0.07115235, -0.0076325 ,  0.08701758,  0.04713482,\n",
-      "        0.01264468,  0.06189806, -0.08814917, -0.05333196, -0.00481453,\n",
-      "       -0.0817222 , -0.0235838 ,  0.06427557, -0.1294789 ,  0.0511005 ,\n",
-      "       -0.10060691, -0.09959193,  0.06534179,  0.06727127,  0.02504219,\n",
-      "        0.10331362,  0.08312651,  0.01973537, -0.05313597,  0.00906576,\n",
-      "       -0.11144827,  0.04190619,  0.03756697,  0.07309648,  0.09901105,\n",
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-      "       -0.04350903, -0.01523179, -0.03405392, -0.04706051, -0.10176124,\n",
-      "        0.0520957 ,  0.10467171,  0.00502419, -0.06434394,  0.0264335 ,\n",
-      "        0.10456662, -0.05373713, -0.08392601, -0.00148737, -0.06927754,\n",
-      "       -0.0069511 ,  0.09514498, -0.18718064,  0.01543353,  0.02678617,\n",
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-      "        0.0149443 , -0.11481634,  0.04835226, -0.06356786,  0.02818446,\n",
-      "       -0.02565291,  0.04153972,  0.09173201,  0.11746994, -0.06592416,\n",
-      "       -0.01033555,  0.05218597,  0.11985964, -0.0172619 , -0.03459224,\n",
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-      "       -0.01374356, -0.10934446, -0.03206901, -0.12148175,  0.04096264,\n",
-      "       -0.01937677], dtype=float32), array([ 1.22610450e-01,  7.39429221e-02, -2.68309414e-02, -1.84546083e-01,\n",
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-      "       -1.42867237e-01,  1.29421800e-01,  2.14075577e-02,  3.73517089e-02,\n",
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-      "        5.03023490e-02, -1.11040197e-01, -2.50613671e-02,  4.82857861e-02,\n",
-      "        2.04586625e-01, -3.70786563e-02, -6.29268736e-02,  2.80499402e-02,\n",
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-      "        6.75811395e-02,  7.92743489e-02, -7.70717114e-02,  3.17969285e-02,\n",
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-      "       -2.32963506e-02,  1.05430134e-01, -3.59771699e-02, -2.36975159e-02,\n",
-      "       -4.43807915e-02,  1.24202995e-02, -1.07181400e-01, -1.09083049e-01,\n",
-      "        3.56643759e-02,  4.33324724e-02, -4.62887101e-02, -1.12822786e-01,\n",
-      "       -1.13101877e-01, -1.50340945e-02,  3.69048156e-02, -1.74667407e-02,\n",
-      "        3.87318060e-02,  8.24990198e-02, -5.50500900e-02, -3.55045162e-02,\n",
-      "       -5.20531945e-02, -8.91519189e-02, -6.51585311e-02, -2.01782491e-02,\n",
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-      "       -4.37625051e-02,  7.20124990e-02, -3.91341820e-02, -5.63132912e-02,\n",
-      "       -1.66460853e-02, -4.70754877e-02, -5.94733916e-02,  5.71954772e-02,\n",
-      "        1.34367839e-01,  5.22203483e-02, -1.29193619e-01,  5.35804071e-02,\n",
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-      "        9.84300151e-02, -2.11597569e-02,  1.38912156e-01, -5.72326407e-02,\n",
-      "        9.92633402e-02, -3.83374430e-02,  1.25535233e-02,  3.79917845e-02,\n",
-      "       -8.31375867e-02,  1.03896938e-01,  1.42428083e-02, -2.02765595e-02,\n",
-      "        6.21048585e-02, -2.32907698e-01, -3.22894454e-02, -2.10774969e-02,\n",
-      "        4.16508280e-02,  1.09569095e-01,  5.74630126e-02, -1.04363054e-01,\n",
-      "       -4.68452498e-02,  2.47965753e-02, -1.88521612e-02,  7.36825392e-02,\n",
-      "        9.84796137e-02,  4.85460646e-02,  3.88997458e-02, -1.08046465e-01,\n",
-      "       -1.04036734e-01,  1.25101388e-01, -6.56153634e-02, -1.89379063e-02,\n",
-      "       -1.50958700e-02, -7.24976510e-02,  7.86617473e-02,  3.27034593e-02,\n",
-      "        5.27941734e-02,  1.18618578e-01, -2.80179754e-02,  2.87339203e-02,\n",
-      "       -1.55507982e-01, -9.19391289e-02,  3.76508455e-03, -5.51198199e-02,\n",
-      "        1.52254015e-01, -1.63303297e-02,  1.53180743e-02, -4.80292551e-02,\n",
-      "        3.64428870e-02,  4.72433344e-02, -1.54690584e-02,  1.38526782e-01,\n",
-      "       -2.07128674e-02, -6.04419131e-03,  8.58368129e-02,  2.59652175e-02,\n",
-      "        3.62342522e-02,  1.93398967e-02,  1.85270697e-01,  8.60779434e-02,\n",
-      "        5.13356440e-02,  9.97062027e-02, -1.60114333e-01, -7.86597505e-02,\n",
-      "       -1.56474188e-02, -4.79406789e-02, -9.83885676e-02,  1.37612447e-01,\n",
-      "       -1.12792589e-01,  8.41441303e-02, -2.76834853e-02, -7.47333467e-02,\n",
-      "        5.91405258e-02,  8.87417868e-02,  1.24135986e-01,  1.16535001e-01,\n",
-      "        7.25473464e-02, -5.59153333e-02, -8.62004235e-02, -2.05151532e-02,\n",
-      "       -5.09192795e-02, -1.02337520e-03, -6.75873319e-03,  9.50852856e-02,\n",
-      "        1.14124052e-01,  1.21425636e-01,  1.50401089e-02,  2.82184426e-02,\n",
-      "        8.28925986e-03, -7.12435395e-02, -3.39830741e-02,  3.50209065e-02,\n",
-      "       -2.47368943e-02, -1.32899368e-02,  5.53644374e-02,  2.38398798e-02,\n",
-      "       -2.79304106e-02, -4.90840599e-02, -6.04364052e-02, -1.19904675e-01,\n",
-      "        2.09521875e-02,  1.43528074e-01, -2.40091328e-02, -8.17930847e-02,\n",
-      "        2.31443103e-02,  9.93111953e-02, -2.10919082e-02, -8.92970189e-02,\n",
-      "        6.02104515e-02, -9.31999236e-02,  5.16967252e-02,  6.72613308e-02,\n",
-      "       -2.68879086e-01,  8.30310310e-05,  1.95715223e-02, -9.48403254e-02,\n",
-      "        3.57986689e-02,  7.16455355e-02,  4.67495993e-02, -7.20672682e-02,\n",
-      "        5.46650030e-03,  3.63146327e-02,  2.60427035e-02,  4.11444530e-02,\n",
-      "       -2.85640303e-02,  2.30800770e-02, -4.83432189e-02,  1.26891539e-01,\n",
-      "       -1.80475771e-01, -1.37855738e-01,  1.61161378e-01, -2.38378569e-02,\n",
-      "        7.05108345e-02, -6.50920421e-02,  6.49009272e-02,  2.87603238e-03,\n",
-      "        2.45792121e-02,  5.64777888e-02,  1.05535127e-01, -8.80619362e-02,\n",
-      "       -2.43405942e-02,  5.27076647e-02,  1.44338518e-01,  1.88424165e-04,\n",
-      "       -5.45456521e-02,  2.62364335e-02, -7.24560320e-02,  2.49359254e-02,\n",
-      "       -1.95902586e-02, -1.22845350e-02,  8.02877247e-02, -1.18080072e-01,\n",
-      "       -4.97216135e-02, -1.31015852e-01,  9.49402303e-02, -1.05913930e-01],\n",
-      "      dtype=float32)]\n"
-     ]
     }
    ],
    "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 注意：\n",
+    "    默认的 EduNLP 项目存储地址为根目录(`~/.EduNLP`)，模型存储地址为项目存储地址下的 `model` 文件夹。您可以通过修改下面的环境变量来修改模型存储地址：\n",
+    "    - EduNLP 项目存储地址：`EDUNLPPATH = xx/xx/xx`\n",
+    "    - 模型存储地址：`EDUNLPMODELPATH = xx/xx/xx`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "t2v(token_items)"
+   ],
+   "outputs": [],
+   "metadata": {}
   }
  ],
  "metadata": {

From b6eb168dcaa524635d42fa1390e262bf9dbb5db5 Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Fri, 20 Aug 2021 18:17:55 +0800
Subject: [PATCH 009/149] [examples] add some detailed descriptions

---
 examples/Tokenizer/tokenizer.ipynb | 31 +++++++++++++++---------------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/examples/Tokenizer/tokenizer.ipynb b/examples/Tokenizer/tokenizer.ipynb
index 6edeb97c..6fc353c7 100644
--- a/examples/Tokenizer/tokenizer.ipynb
+++ b/examples/Tokenizer/tokenizer.ipynb
@@ -15,12 +15,12 @@
     "\n",
     "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\n",
     "\n",
-    "1. 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
+    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
     "  \n",
     "\n",
-    "2. 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\n",
-    "    - 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\n",
-    "    - 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\n",
+    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\n",
+    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\n",
+    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\n",
     "\n",
     "### 公式解析\n",
     "\n",
@@ -50,16 +50,17 @@
    "source": [
     "### 词解析\n",
     "\n",
-    "词解析分为两个主要步骤：\n",
-    "1. 分词：  \n",
-    "    - 词组解析：使用分词工具切分并提取题目文本中的词。  \n",
+    "词解析分为两个主要步骤：  \n",
+    "\n",
+    "（1） 分词：  \n",
+    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \n",
     "    本项目目前支持的分词工具有：`jieba`   \n",
-    "    - 单字解析：按字符划分。\n",
+    "- 单字解析：按字符划分。\n",
     "    \n",
     "      \n",
-    "2. 筛选：过滤指定的停用词。   \n",
-    "    本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n",
-    "    你也可以使用自己的停用词表，具体使用方法见下面的示例。\n"
+    "（2） 筛选：过滤指定的停用词。   \n",
+    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n",
+    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\n"
    ],
    "metadata": {}
   },
@@ -239,7 +240,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "1. 如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
    ],
    "metadata": {}
   },
@@ -266,7 +267,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "2. 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
     "    > 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
     "\n",
     "    > 因此，ast 可以看做是公式的语法结构表征。"
@@ -296,7 +297,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "3. 如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
     "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
    ],
    "metadata": {}
@@ -335,7 +336,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "4. 如果您除了 `3.` 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
    ],
    "metadata": {}
   },

From 5b08a4f950d578264f4d6d4c3ca5ea2640d36a6c Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Fri, 20 Aug 2021 18:19:51 +0800
Subject: [PATCH 010/149] [examples] add some detailed descriptions

---
 examples/Tokenizer/tokenizer.ipynb | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/examples/Tokenizer/tokenizer.ipynb b/examples/Tokenizer/tokenizer.ipynb
index 6fc353c7..4819b00d 100644
--- a/examples/Tokenizer/tokenizer.ipynb
+++ b/examples/Tokenizer/tokenizer.ipynb
@@ -268,9 +268,8 @@
    "cell_type": "markdown",
    "source": [
     "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
-    "    > 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
-    "\n",
-    "    > 因此，ast 可以看做是公式的语法结构表征。"
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
    ],
    "metadata": {}
   },

From 1efe36442ca4b949552a13fe2b5e36d08691f296 Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Sun, 22 Aug 2021 16:45:50 +0800
Subject: [PATCH 011/149] [feat] add support to \textf{}

---
 EduNLP/SIF/parser/parser.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index db290946..ac8787d2 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -1,4 +1,5 @@
 from EduNLP.Formula.ast import str2ast, katex_parse
+import re
 
 
 class Parser:
@@ -221,8 +222,13 @@ def get_token(self):
             formula_start = self.head
             while self.head < len(self.text) and self.text[self.head] != '$':
                 ch_informula = self.text[self.head]
+                if re.match(
+                        r"\\textf\{.+\}", self.text[self.head:]) is not None:
+                # 公式中的文本应该使用 \\textf{文本} 标记（遵循 latex 语法）
+                    self.head += re.match(
+                        r"\\textf\{.+\}", self.text[self.head:]).span()[1]-1
                 if flag and self.is_chinese(ch_informula):
-                    # latex 中出现中文字符，打印且只打印一次 warning
+                    # latex 中出现非法中文字符，打印且只打印一次 warning
                     print("Warning: there is some chinese characters in formula!")
                     self.warnning = 1
                     flag = 0
@@ -230,7 +236,7 @@ def get_token(self):
             if self.head >= len(self.text):
                 self.call_error()
                 return self.error
-            # 检查latex公式的完整性和可解析性
+            # 检查 latex 公式的完整性和可解析性
             if not self._is_formula_legal(self.text[formula_start:self.head]):
                 self.call_error()
                 return self.error

From b3dd6f5ade8334aec4f2515e43eff4630c33405b Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Sun, 22 Aug 2021 17:36:29 +0800
Subject: [PATCH 012/149] add tests for \textf{}

---
 EduNLP/SIF/parser/parser.py   |  4 ++--
 tests/test_sif/test_parser.py | 10 ++++++++++
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index ac8787d2..67e9a6ac 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -11,7 +11,7 @@ def __init__(self, data):
         self.error_postion = 0
         self.error_flag = 0
         self.modify_flag = 0
-        self.warnning = 0
+        self.warning = 0
         self.fomula_illegal_flag = 0
         self.fomula_illegal_message = ''
 
@@ -230,7 +230,7 @@ def get_token(self):
                 if flag and self.is_chinese(ch_informula):
                     # latex 中出现非法中文字符，打印且只打印一次 warning
                     print("Warning: there is some chinese characters in formula!")
-                    self.warnning = 1
+                    self.warning = 1
                     flag = 0
                 self.head += 1
             if self.head >= len(self.text):
diff --git a/tests/test_sif/test_parser.py b/tests/test_sif/test_parser.py
index 34f996d2..8af2ca2b 100644
--- a/tests/test_sif/test_parser.py
+++ b/tests/test_sif/test_parser.py
@@ -34,3 +34,13 @@ def test_parser():
     text_parser = Parser(text)
     text_parser.description_list()
     assert text_parser.fomula_illegal_flag == 1
+
+    text = r"支持公式中添加文本，但要符合语法，如$\textf{这是文本}ABCD$是允许的"
+    text_parser = Parser(text)
+    text_parser.description_list()
+    assert text_parser.warning == 0
+
+    text = r"支持公式中添加文本，但要符合语法，如若出现：$这是文本ABCD$，会有warning"
+    text_parser = Parser(text)
+    text_parser.description_list()
+    assert text_parser.warning == 1

From ff2a8dfd9244ea7835cdce0a2ffed36f6626ad9f Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Mon, 23 Aug 2021 21:32:43 +0800
Subject: [PATCH 013/149] [fix] update some examples

---
 examples/pretrain/gensim/d2v_bow_tfidf.ipynb | 547 +++++++++---
 examples/pretrain/gensim/w2v_stem_text.ipynb | 158 ++--
 examples/pretrain/prepare_dataset.ipynb      |  56 +-
 examples/sif/sif.ipynb                       | 848 ++++++++++++++-----
 examples/sif/sif_additon.ipynb               | 733 ----------------
 examples/utils/data.ipynb                    |  17 +-
 6 files changed, 1159 insertions(+), 1200 deletions(-)
 delete mode 100644 examples/sif/sif_additon.ipynb

diff --git a/examples/pretrain/gensim/d2v_bow_tfidf.ipynb b/examples/pretrain/gensim/d2v_bow_tfidf.ipynb
index 66a77fa6..8b8f1671 100644
--- a/examples/pretrain/gensim/d2v_bow_tfidf.ipynb
+++ b/examples/pretrain/gensim/d2v_bow_tfidf.ipynb
@@ -3,94 +3,109 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# 1. load and tokenize test_items"
+    "# 基于 gensim 的模型训练举例"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 概述\n",
+    "\n",
+    "您可以使用自己的数据和模型参数来训练和使用自己的模型。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入模块"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "import json\n",
+    "from tqdm import tqdm\n",
+    "from EduNLP.Pretrain import GensimWordTokenizer, train_vector\n",
+    "from EduNLP.Vector import D2V, W2V\n",
+    "from EduNLP.SIF.segment import seg\n",
+    "from EduNLP.SIF.tokenization import tokenize\n",
+    "import time"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 准备模型训练数据"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "source": [
-    "from platform import processor\r\n",
-    "from gensim import corpora,models\r\n",
-    "# from collections import defaultdict\r\n",
-    "import json\r\n",
-    "from tqdm import tqdm\r\n",
-    "from EduNLP.Pretrain import GensimWordTokenizer,train_vector\r\n",
-    "from EduNLP.Vector import D2V\r\n",
-    "from EduNLP.SIF.segment import seg\r\n",
-    "from EduNLP.SIF.tokenization import tokenize\r\n",
-    "import time\r\n",
-    "\r\n",
-    "output_file_head = \"test\"  # subject = english | liberal | science |all\r\n",
-    "baseDir = \"E:/Workustc/lunadata/d2v\"\r\n",
-    "# baseDir = \"/home/qlh/data_pretrain\"\r\n",
-    "work_file_path = baseDir + \"/data/\" + output_file_head + \"_raw.json\"\r\n",
-    "\r\n",
-    "test_items = [{'ques_content':'有公式$\\\\FormFigureID{wrong1?}$和公式$\\\\FormFigureBase64{wrong2?}$，如图$\\\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$'},\r\n",
-    "             {\"ques_content\":\"Human machine interface for lab abc computer applications\"},\r\n",
-    "             {\"ques_content\": \"A survey of user opinion of computer system response time\"},\r\n",
-    "             {\"ques_content\": \"The EPS user interface management system\"},\r\n",
-    "             {\"ques_content\": \"System and human system engineering testing of EPS\"},\r\n",
-    "             {\"ques_content\": \"Relation of user perceived response time to error measurement\"},\r\n",
-    "             {\"ques_content\": \"The generation of random binary unordered trees\"},\r\n",
-    "             {\"ques_content\": \"The intersection graph of paths in trees\"},\r\n",
-    "             {\"ques_content\": \"Graph minors IV Widths of trees and well quasi ordering\"},\r\n",
-    "             {\"ques_content\": \"Graph minors A survey\"}\r\n",
-    "             ]\r\n",
-    "\r\n",
-    "def load_items():\r\n",
-    "    for line in test_items:\r\n",
-    "        yield line\r\n",
-    "    # with open(work_file_path, 'r', encoding=\"utf-8\") as f:\r\n",
-    "    #     for line in f:\r\n",
-    "    #         yield json.loads(line)\r\n",
-    "\r\n",
-    "def data2Token():\r\n",
-    "    # 线性分词，而不使用ast\r\n",
-    "    tokenization_params = {\r\n",
-    "        \"formula_params\": {\r\n",
-    "            \"method\": \"linear\",\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    "    \r\n",
-    "    token_items = []\r\n",
-    "    count = 1\r\n",
-    "    for item in tqdm(load_items(), \"sifing\"):\r\n",
-    "        count = count + 1\r\n",
-    "        # -------------------------------------------- # \r\n",
-    "        # \"\"\"除文本、公式外，其他转化为特殊标记\"\"\"\r\n",
-    "        # seg_ret = seg(item[\"ques_content\"], symbol=\"gmas\")\r\n",
-    "        # token_item = tokenize(seg_ret, **tokenization_params)\r\n",
-    "        tokenizer = GensimWordTokenizer(symbol=\"gmas\", general=True)\r\n",
-    "        token_item = tokenizer(item[\"ques_content\"])\r\n",
-    "\r\n",
-    "        # -------------------------------------------- # \r\n",
-    "        if token_item:\r\n",
-    "            # print(\"[i] = \", count)\r\n",
-    "            # print(\"[tokens] = \", token_item)\r\n",
-    "            token_items.append(token_item.tokens)\r\n",
-    "    print(\"[data2Token] finish ========================> num = \",len(token_items))\r\n",
-    "    return token_items\r\n",
-    "\r\n",
-    "token_items = data2Token()\r\n",
-    "token_items[0]"
+    "test_items = [{'ques_content':'有公式$\\\\FormFigureID{wrong1?}$和公式$\\\\FormFigureBase64{wrong2?}$，如图$\\\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$'},\n",
+    "             {\"ques_content\":\"Human machine interface for lab abc computer applications\"},\n",
+    "             {\"ques_content\": \"A survey of user opinion of computer system response time\"},\n",
+    "             {\"ques_content\": \"The EPS user interface management system\"},\n",
+    "             {\"ques_content\": \"System and human system engineering testing of EPS\"},\n",
+    "             {\"ques_content\": \"Relation of user perceived response time to error measurement\"},\n",
+    "             {\"ques_content\": \"The generation of random binary unordered trees\"},\n",
+    "             {\"ques_content\": \"The intersection graph of paths in trees\"},\n",
+    "             {\"ques_content\": \"Graph minors IV Widths of trees and well quasi ordering\"},\n",
+    "             {\"ques_content\": \"Graph minors A survey\"}\n",
+    "             ]\n",
+    "\n",
+    "def load_items():\n",
+    "    for line in test_items:\n",
+    "        yield line\n",
+    "\n",
+    "\n",
+    "def data2Token():\n",
+    "    # 线性分词\n",
+    "    tokenization_params = {\n",
+    "        \"formula_params\": {\n",
+    "            \"method\": \"linear\",\n",
+    "        }\n",
+    "    }\n",
+    "    \n",
+    "    token_items = []\n",
+    "    count = 1\n",
+    "    for item in tqdm(load_items(), \"sifing\"):\n",
+    "        count = count + 1\n",
+    "        # -------------------------------------------- # \n",
+    "        # \"\"\"除文本、公式外，其他转化为特殊标记\"\"\"\n",
+    "        tokenizer = GensimWordTokenizer(symbol=\"gmas\", general=True)\n",
+    "        token_item = tokenizer(item[\"ques_content\"])\n",
+    "\n",
+    "        # -------------------------------------------- # \n",
+    "        if token_item:\n",
+    "            token_items.append(token_item.tokens)\n",
+    "    print(\"[data2Token] finish ========================> num = \",len(token_items))\n",
+    "    return token_items\n",
+    "\n",
+    "token_items = data2Token()\n",
+    "print(token_items[0])"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stderr",
      "text": [
-      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n",
-      "sifing: 10it [00:00, 18.57it/s]"
+      "sifing: 10it [00:00, 114.91it/s]"
      ]
     },
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[data2Token] finish ========================> num =  10\n"
+      "[data2Token] finish ========================> num =  10\n",
+      "['公式', '[FORMULA]', '公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '[SEP]', 'z', '=', 'x', '+', '7', 'y', '最大值', '[MARK]']\n"
      ]
     },
     {
@@ -99,41 +114,13 @@
      "text": [
       "\n"
      ]
-    },
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['公式',\n",
-       " '[FORMULA]',\n",
-       " '公式',\n",
-       " '[FORMULA]',\n",
-       " '如图',\n",
-       " '[FIGURE]',\n",
-       " 'x',\n",
-       " ',',\n",
-       " 'y',\n",
-       " '约束条件',\n",
-       " '[SEP]',\n",
-       " 'z',\n",
-       " '=',\n",
-       " 'x',\n",
-       " '+',\n",
-       " '7',\n",
-       " 'y',\n",
-       " '最大值',\n",
-       " '[MARK]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 1
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "source": [
     "len(token_items[0])"
    ],
@@ -146,7 +133,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 2
+     "execution_count": 3
     }
    ],
    "metadata": {
@@ -158,7 +145,90 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# 2. train and test model by 'bow'"
+    "### 也可从文件导入数据\n",
+    "例如："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "source": [
+    "from EduData import get_data\n",
+    "\n",
+    "# 导入项目提供的数据，存放路径：\"../../data/\"  \n",
+    "get_data(\"open-luna\", \"../../data/\")\n",
+    "\n",
+    "\n",
+    "def load_items():\n",
+    "    with open(\"../../../data/OpenLUNA.json\", encoding=\"utf-8\") as f:\n",
+    "        for line in f:\n",
+    "            yield json.loads(line)\n",
+    "\n",
+    "\n",
+    "tokenizer = GensimWordTokenizer(symbol=\"gm\")\n",
+    "sif_items = []\n",
+    "for item in tqdm(load_items(), \"sifing\"):\n",
+    "    sif_item = tokenizer(\n",
+    "        item[\"stem\"]\n",
+    "    )\n",
+    "    if sif_item:\n",
+    "        sif_items.append(sif_item.tokens)\n",
+    "\n",
+    "sif_items[0]\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "downloader, INFO http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json is saved as ../../data/OpenLUNA.json\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Downloading ../../data/OpenLUNA.json 100.00%: 269KB | 269KB\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "sifing: 0it [00:00, ?it/s]\n"
+     ]
+    },
+    {
+     "output_type": "error",
+     "ename": "JSONDecodeError",
+     "evalue": "Expecting property name enclosed in double quotes: line 2 column 1 (char 2)",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_21138/1902388532.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGensimWordTokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"gm\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0msif_items\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mload_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sifing\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     sif_item = tokenizer(\n\u001b[1;32m     17\u001b[0m         \u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"stem\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1186\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/tmp/ipykernel_21138/1902388532.py\u001b[0m in \u001b[0;36mload_items\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../../../data/OpenLUNA.json\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3.8/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 357\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    351\u001b[0m         \"\"\"\n\u001b[1;32m    352\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 2 column 1 (char 2)"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## EduNLP.Vector.D2V 模块的训练方法"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 1. 基于词频训练模型"
    ],
    "metadata": {
     "pycharm": {
@@ -168,10 +238,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "source": [
-    "from EduNLP.Pretrain import train_vector\r\n",
-    "#10 dimension with fasstext method\r\n",
     "train_vector(token_items, \"../../../data/d2v/gensim_luna_stem_tf_\", method=\"bow\")"
    ],
    "outputs": [
@@ -190,7 +258,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 3
+     "execution_count": 6
     }
    ],
    "metadata": {
@@ -199,13 +267,18 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 模型测试"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "source": [
-    "from EduNLP.Vector import D2V\r\n",
-    "\r\n",
-    "d2v = D2V(\"../../../data/d2v/gensim_luna_stem_tf_bow.bin\", method = \"bow\")\r\n",
+    "d2v = D2V(\"../../../data/d2v/gensim_luna_stem_tf_bow.bin\", method = \"bow\")\n",
     "print(d2v(token_items[1]))"
    ],
    "outputs": [
@@ -226,16 +299,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# 3. train and test model by 'tfidf'"
+    "### 2. 基于 tfidf 训练模型"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "source": [
-    "from EduNLP.Pretrain import train_vector\r\n",
-    "#10 dimension with fasstext method\r\n",
     "train_vector(token_items, \"../../../data/d2v/gensim_luna_stem_tf_\", method=\"tfidf\")"
    ],
    "outputs": [
@@ -255,46 +326,250 @@
       ]
      },
      "metadata": {},
-     "execution_count": 5
+     "execution_count": 7
     }
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 模型测试"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "source": [
-    "from EduNLP.Vector import D2V\r\n",
-    "\r\n",
-    "d2v = D2V(\"../../../data/d2v/gensim_luna_stem_tf_tfidf.bin\", method = \"tfidf\")\r\n",
-    "vec_size = d2v.vector_size\r\n",
-    "print(\"vec_size = \", vec_size)\r\n",
-    "d2v(token_items[1])"
+    "d2v = D2V(\"../../../data/d2v/gensim_luna_stem_tf_tfidf.bin\", method = \"tfidf\")\n",
+    "vec_size = d2v.vector_size\n",
+    "print(\"vec_size = \", vec_size)\n",
+    "print(d2v(token_items[1]))"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "vec_size =  63\n"
+      "vec_size =  63\n",
+      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.37858374396389033, 0.37858374396389033, 0.37858374396389033, 0.2646186811599866, 0.37858374396389033, 0.2646186811599866, 0.37858374396389033, 0.37858374396389033, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 3. 基于 Doc2Vec 训练模型"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "# 10 dimension with doc2vec method\n",
+    "train_vector(token_items, \"../../../data/w2v/gensim_luna_stem_tf_\", 10, method=\"d2v\")\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "EduNLP, INFO Epoch #0: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #1: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #2: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #3: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #4: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #5: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #6: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #7: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #8: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #9: loss-0.0000 \n",
+      "EduNLP, INFO model is saved to ../../../data/w2v/gensim_luna_stem_tf_d2v_10.bin\n"
      ]
     },
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[(15, 0.37858374396389033),\n",
-       " (16, 0.37858374396389033),\n",
-       " (17, 0.37858374396389033),\n",
-       " (18, 0.2646186811599866),\n",
-       " (19, 0.37858374396389033),\n",
-       " (20, 0.2646186811599866),\n",
-       " (21, 0.37858374396389033),\n",
-       " (22, 0.37858374396389033)]"
+       "'../../../data/w2v/gensim_luna_stem_tf_d2v_10.bin'"
       ]
      },
      "metadata": {},
-     "execution_count": 6
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "d2v = D2V(\"../../../data/w2v/gensim_luna_stem_tf_d2v_10.bin\", method=\"d2v\")\n",
+    "vec_size = d2v.vector_size\n",
+    "print(\"vec_size = \", vec_size)\n",
+    "print(d2v(token_items[1]))\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "vec_size =  10\n",
+      "[-0.00211227  0.00167636  0.02313529 -0.04260717 -0.01389424 -0.03898989\n",
+      "  0.01181044  0.01069339 -0.03934718  0.00038158]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## EduNLP.Vector.W2V 模块支持的训练方法"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 1. 基于 FastText 训练模型"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "# 10 dimension with fasstext method\n",
+    "train_vector(token_items, \"../../../data/w2v/gensim_luna_stem_t_\",\n",
+    "             10, method=\"fasttext\")\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "EduNLP, INFO Epoch #0: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #1: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #2: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #3: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #4: loss-0.0000 \n",
+      "EduNLP, INFO model is saved to ../../../data/w2v/gensim_luna_stem_t_fasttext_10.bin\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'../../../data/w2v/gensim_luna_stem_t_fasttext_10.bin'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 模型测试"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "source": [
+    "w2v = W2V(\"../../../data/w2v/gensim_luna_stem_t_fasttext_10.bin\", method=\"fasttext\")\n",
+    "w2v[\"[FORMULA]\"]\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([-0.00434524, -0.00836839, -0.02108332,  0.00493213,  0.00461454,\n",
+       "        0.01070305, -0.01737931,  0.0210843 , -0.00525515,  0.00918209],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 2. 基于 cbow 训练模型"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "source": [
+    "train_vector(token_items, \"../../../data/w2v/gensim_luna_stem_t_\", 10, method=\"cbow\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "EduNLP, INFO Epoch #0: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #1: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #2: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #3: loss-0.0000 \n",
+      "EduNLP, INFO Epoch #4: loss-0.0000 \n",
+      "EduNLP, INFO model is saved to ../../../data/w2v/gensim_luna_stem_t_cbow_10.kv\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'../../../data/w2v/gensim_luna_stem_t_cbow_10.kv'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 模型测试"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "source": [
+    "w2v = W2V(\"../../../data/w2v/gensim_luna_stem_t_cbow_10.kv\",\n",
+    "          method=\"fasttext\")\n",
+    "w2v[\"[FORMULA]\"]\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([-0.0156765 ,  0.00329737, -0.04140369, -0.07689971, -0.01493463,\n",
+       "        0.02475806, -0.00877463,  0.05539609, -0.02750023,  0.0224804 ],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
     }
    ],
    "metadata": {}
@@ -302,9 +577,8 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
   },
   "language_info": {
    "codemirror_mode": {
@@ -316,7 +590,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.8.5"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  },
  "nbformat": 4,
diff --git a/examples/pretrain/gensim/w2v_stem_text.ipynb b/examples/pretrain/gensim/w2v_stem_text.ipynb
index 01a38b20..9de78acc 100644
--- a/examples/pretrain/gensim/w2v_stem_text.ipynb
+++ b/examples/pretrain/gensim/w2v_stem_text.ipynb
@@ -3,28 +3,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "d:\\env\\python3.8\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n",
-      "sifing: 792it [00:00, 845.20it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "['已知', '集合', '[FORMULA]', '[FORMULA]']"
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "import json\n",
     "from tqdm import tqdm\n",
@@ -43,24 +21,30 @@
     "]\n",
     "\n",
     "sif_items[0]"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": true
+   }
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "source": [
+    "len(sif_items)"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "792"
+      "text/plain": [
+       "792"
+      ]
      },
-     "execution_count": 2,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 2
     }
    ],
-   "source": [
-    "len(sif_items)"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -71,10 +55,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "source": [
+    "# 100 dimension with skipgram method\n",
+    "train_vector(sif_items, \"../../../data/w2v/gensim_luna_stem_t_\", 100)"
+   ],
    "outputs": [
     {
-     "name": "stderr",
      "output_type": "stream",
+     "name": "stderr",
      "text": [
       "EduNLP, INFO Epoch #0: loss-0.0000 \n",
       "EduNLP, INFO Epoch #1: loss-0.0000 \n",
@@ -85,18 +73,16 @@
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "'../../../data/w2v/gensim_luna_stem_t_sg_100.kv'"
+      "text/plain": [
+       "'../../../data/w2v/gensim_luna_stem_t_sg_100.kv'"
+      ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
-   "source": [
-    "# 100 dimension with skipgram method\n",
-    "train_vector(sif_items, \"../../../data/w2v/gensim_luna_stem_t_\", 100)"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -107,10 +93,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
+   "source": [
+    "# 50 dimension with cbow method\n",
+    "train_vector(sif_items, \"../../../data/w2v/gensim_luna_stem_t_\", 50, method=\"cbow\")"
+   ],
    "outputs": [
     {
-     "name": "stderr",
      "output_type": "stream",
+     "name": "stderr",
      "text": [
       "EduNLP, INFO Epoch #0: loss-0.0000 \n",
       "EduNLP, INFO Epoch #1: loss-0.0000 \n",
@@ -121,18 +111,16 @@
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "'../../../data/w2v/gensim_luna_stem_t_cbow_50.kv'"
+      "text/plain": [
+       "'../../../data/w2v/gensim_luna_stem_t_cbow_50.kv'"
+      ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     }
    ],
-   "source": [
-    "# 50 dimension with cbow method\n",
-    "train_vector(sif_items, \"../../../data/w2v/gensim_luna_stem_t_\", 50, method=\"cbow\")"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -143,10 +131,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "source": [
+    "# 10 dimension with fasstext method\n",
+    "train_vector(sif_items, \"../../../data/w2v/gensim_luna_stem_t_\", 10, method=\"fasttext\")"
+   ],
    "outputs": [
     {
-     "name": "stderr",
      "output_type": "stream",
+     "name": "stderr",
      "text": [
       "EduNLP, INFO Epoch #0: loss-0.0000 \n",
       "EduNLP, INFO Epoch #1: loss-0.0000 \n",
@@ -157,18 +149,16 @@
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "'../../../data/w2v/gensim_luna_stem_t_fasttext_10.bin'"
+      "text/plain": [
+       "'../../../data/w2v/gensim_luna_stem_t_fasttext_10.bin'"
+      ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     }
    ],
-   "source": [
-    "# 10 dimension with fasstext method\n",
-    "train_vector(sif_items, \"../../../data/w2v/gensim_luna_stem_t_\", 10, method=\"fasttext\")"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -179,22 +169,44 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "source": [
+    "from EduNLP.Vector import W2V\n",
+    "\n",
+    "w2v = W2V(\"../../../data/w2v/gensim_luna_stem_t_sg_100.kv\")\n",
+    "w2v[\"[FORMULA]\"]"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "array([-0.16754825,  0.2707899 ,  0.01005908, -0.03040857,  0.10938002,\n       -0.28348687,  0.19054936,  0.41737646, -0.3885515 , -0.14650987,\n        0.1157743 , -0.2406684 , -0.11294927,  0.12082661,  0.1759571 ,\n        0.17807944,  0.07178611, -0.16182491, -0.18266837, -0.52223957,\n       -0.05876796,  0.0450548 ,  0.26906556,  0.02253102,  0.1025768 ,\n        0.29827935, -0.441235  , -0.06949052, -0.22638813, -0.10846554,\n       -0.05917242,  0.12802479,  0.21151058, -0.4611071 , -0.16157094,\n        0.32488874,  0.36630565, -0.36908495,  0.24223483, -0.3510737 ,\n       -0.15079798,  0.10832163,  0.00392658, -0.20019084,  0.18827583,\n       -0.17247967, -0.27385622,  0.17878376,  0.05156241,  0.30575123,\n       -0.16626868,  0.01431947,  0.05540735,  0.03373449,  0.36685058,\n       -0.05511234,  0.09583379, -0.09495933,  0.01121055,  0.18113017,\n        0.29060405,  0.06472825,  0.20568778, -0.02780204, -0.17310621,\n        0.23243082,  0.2480153 ,  0.07856195, -0.03825858,  0.10257348,\n       -0.02105796,  0.4248383 ,  0.03114873, -0.09995517,  0.16022007,\n        0.08843125,  0.06128069, -0.03922344,  0.02587396,  0.03067247,\n        0.1209543 , -0.05948736, -0.25567266,  0.53167033, -0.4149    ,\n        0.08551055,  0.42399153,  0.18317291,  0.12455773, -0.10759205,\n        0.17496923,  0.2781072 ,  0.25744784,  0.1921185 ,  0.43071204,\n        0.09138201, -0.37603223, -0.07436363,  0.2961049 ,  0.02517671],\n      dtype=float32)"
+      "text/plain": [
+       "array([-0.16754825,  0.2707899 ,  0.01005908, -0.03040857,  0.10938002,\n",
+       "       -0.28348687,  0.19054936,  0.41737646, -0.3885515 , -0.14650987,\n",
+       "        0.1157743 , -0.2406684 , -0.11294927,  0.12082661,  0.1759571 ,\n",
+       "        0.17807944,  0.07178611, -0.16182491, -0.18266837, -0.52223957,\n",
+       "       -0.05876796,  0.0450548 ,  0.26906556,  0.02253102,  0.1025768 ,\n",
+       "        0.29827935, -0.441235  , -0.06949052, -0.22638813, -0.10846554,\n",
+       "       -0.05917242,  0.12802479,  0.21151058, -0.4611071 , -0.16157094,\n",
+       "        0.32488874,  0.36630565, -0.36908495,  0.24223483, -0.3510737 ,\n",
+       "       -0.15079798,  0.10832163,  0.00392658, -0.20019084,  0.18827583,\n",
+       "       -0.17247967, -0.27385622,  0.17878376,  0.05156241,  0.30575123,\n",
+       "       -0.16626868,  0.01431947,  0.05540735,  0.03373449,  0.36685058,\n",
+       "       -0.05511234,  0.09583379, -0.09495933,  0.01121055,  0.18113017,\n",
+       "        0.29060405,  0.06472825,  0.20568778, -0.02780204, -0.17310621,\n",
+       "        0.23243082,  0.2480153 ,  0.07856195, -0.03825858,  0.10257348,\n",
+       "       -0.02105796,  0.4248383 ,  0.03114873, -0.09995517,  0.16022007,\n",
+       "        0.08843125,  0.06128069, -0.03922344,  0.02587396,  0.03067247,\n",
+       "        0.1209543 , -0.05948736, -0.25567266,  0.53167033, -0.4149    ,\n",
+       "        0.08551055,  0.42399153,  0.18317291,  0.12455773, -0.10759205,\n",
+       "        0.17496923,  0.2781072 ,  0.25744784,  0.1921185 ,  0.43071204,\n",
+       "        0.09138201, -0.37603223, -0.07436363,  0.2961049 ,  0.02517671],\n",
+       "      dtype=float32)"
+      ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
-   "source": [
-    "from EduNLP.Vector import W2V\n",
-    "\n",
-    "w2v = W2V(\"../../../data/w2v/gensim_luna_stem_t_sg_100.kv\")\n",
-    "w2v[\"[FORMULA]\"]"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -205,23 +217,25 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
   },
   "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
+   "pygments_lexer": "ipython3",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "file_extension": ".py"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 2
 }
\ No newline at end of file
diff --git a/examples/pretrain/prepare_dataset.ipynb b/examples/pretrain/prepare_dataset.ipynb
index 0f1acb0e..1ae3e61a 100644
--- a/examples/pretrain/prepare_dataset.ipynb
+++ b/examples/pretrain/prepare_dataset.ipynb
@@ -3,59 +3,65 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "source": [
+    "from EduData import get_data\n",
+    "\n",
+    "get_data(\"open-luna\", \"../../data/\")\n"
+   ],
    "outputs": [
     {
-     "name": "stderr",
      "output_type": "stream",
+     "name": "stderr",
      "text": [
-      "downloader, INFO http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json is saved as ..\\..\\data\\OpenLUNA.json\n"
+      "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!\n",
+      "  warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n",
+      "downloader, INFO http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json is saved as ../../data/OpenLUNA.json\n"
      ]
     },
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "Downloading ..\\..\\data\\OpenLUNA.json 100.00%: 275142 | 275142\n"
+      "Downloading ../../data/OpenLUNA.json 100.00%: 269KB | 269KB\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "'../../data/'"
+      "text/plain": [
+       "'../../data/OpenLUNA.json'"
+      ]
      },
-     "execution_count": 1,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 1
     }
    ],
-   "source": [
-    "from EduData import get_data\n",
-    "\n",
-    "get_data(\"open-luna\", \"../../data/\")\n"
-   ]
+   "metadata": {
+    "collapsed": true
+   }
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
   },
   "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
+   "pygments_lexer": "ipython3",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "file_extension": ".py"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 2
 }
\ No newline at end of file
diff --git a/examples/sif/sif.ipynb b/examples/sif/sif.ipynb
index 8bd80a7e..2076e126 100644
--- a/examples/sif/sif.ipynb
+++ b/examples/sif/sif.ipynb
@@ -2,35 +2,39 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "source": [
+    "# SIF4Sci 使用示例\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
+   ],
    "metadata": {
     "collapsed": true,
     "pycharm": {
      "name": "#%% md\n"
     }
-   },
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "# Code for beginner to learn how to use SIF4Sci\n",
+    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
     "\n",
-    "In this notebook, we will show you the basic usage to apply SIF to prepare data for conducting scientific experiments.\n",
-    "\n",
-    "We use the demo item (an exercise from LUNA) shown in the following Figure.\n",
-    "![Figure](../../asset/_static/item.png).\n",
-    "The SIF expression of this item can be written as follows:"
-   ]
+    "![Figure](../../asset/_static/item.png)"
+   ],
+   "metadata": {}
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "cell_type": "markdown",
+   "source": [
+    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "source": [
     "item = {\n",
     "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\n",
@@ -38,6 +42,18 @@
     "}\n",
     "item[\"stem\"]"
    ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -45,26 +61,35 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 加载图片：`$\\\\FigureID{1}$`"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
+   "source": [
+    "from PIL import Image\n",
+    "img = Image.open(\"../../asset/_static/item_figure.png\")\n",
+    "figures = {\"1\": img}\n",
+    "img"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x23347BF9D60>",
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAACICAYAAADzlJeRAAAe3klEQVR4nO2df1RT9/3/nyE/SWJyBdSUOgs72mKP1XhK24j2CGon4FagbtTMtpZt6rDzR7rjKt+2C+rnHGxdBbr2xB9zgc4j1NMJbpXg2hlsp2DnjrSHVaqcgquWbFqNLUgQkvf3DwgiSSA/7uUm8f04530O3Ny87zu5ed7X+8fr/XoJCCEEFAolrIjhuwEUCsUTKkwKJQyhwqRQwhAR3w2IBJqamuBwOHy+rtFokJKSMo4tokQ7VJgAGhoa0NzcDLvdjn//+99oa2uDw+FAa2srAIBhmFHf73K58O233wIAYmNj8cADDwAAZs+ejblz50Kr1UKn00Emk3H6OSjRg+Bum5Vtbm5Gc3MzrFYrPvnkE7S2toJhGPT09KC3t5f160mlUsTGxqKrqwsMw2D69OlYunQpdDodMjMzWb8eJTqIemHa7XbU19fjT3/6E/72t79BoVDA6XSiq6uL13YxDIOuri48/vjjyM/PR2ZmJpKSknhtEyV8iEph2mw21NbW4tChQzh16hSkUulQVzMckcvliImJgUwmQ3Z2NgwGA7RaLd/NovBIVAmzuroar732Gs6fPw8AuHnzJs8tCg61Wg2FQoFXX30VK1asGHOMS4k+Il6YNpsNf/jDH7Bz504ACGvLGChyuRxOpxOLFy/G+vXr6Zj0LiJihdnU1IQ33ngDdXV1cLlcoy5nRAMMw0AkEmHnzp14/vnn+W4OhWMiTpjNzc0oLCxEa2sr7HY7380Zd5RKJWQyGRVolBMxwrTZbFi/fj0++OAD3Lhxg+/m8A4VaHQT9sK02+347W9/i8rKyqgaP7KFUqmESqXCn//8Z+h0Or6bQ2GJsPaV3bdvH5KTk7Fv3z4qSh90dXXh66+/RlZWFjZs2HBXdu+jEhKGdHZ2Ep1OR9RqNQFAi59FJpORSZMmEYvFwuLdqCNlZRdYrI/iD2FnMfft24dZs2ahqamJjiUDxOFw4MqVK9Dr9XjqqadYsJ5tKJ+fjUNsNI4SGHw/Gdxcv36d5OXlUSvJUhGLxWTSpEmksbEx6HtyoSyNACBr6li80RS/CAuL2dDQgPvvvx/vv/8+tZIs0dfXhytXruBHP/oRSkpKAq+grRw78QrK0thvG2VseJ+VLS0txbZt2+ikBYeoVCrMmzcP1dXVfrr3WbB2/nlsPnk/dgr+Dw9eOImN07lrX0NDA4Db+17/+c9/4uuvv/Z67uzZs5GcnAwASEpKuqNEFXya65/97GeEYRjeu313QxGLxSQ5OZmcO3duzPtSt2YNqSOEkAtlJA2Df7NAT08PsVqtxGg0Ep1OR2JjYwkAwjAMYRiGiESigD6TUqkkDMMQpVJJABCtVkuys7OJ0WgMqQsfDvAizOvXr5NHH3106MbQMn4lLi6OHDlyxOe9cY8rb5fQhHnu3Dny8ssvk+TkZCISicbtQcwwDImNjSUZGRnEZDKR9vb2ED7F+DPuwmxvbycpKSlEIpHw/iO9W8uUKVPIO++843lzLpSRNcOXRurWEKSVkUAXSxobG0lhYSFJSEggKpUqYEvIdpHL5USpVJLExERiMpnI9evXQ/kJjwvjKsyzZ8+SxMRE3n+YtIBMnDiRvP3227dvzoUykjZChBfK0vwWZk9PDzGbzSQxMTGshydyuZwoFAqSnZ3N8novu4ybMM+ePUs0Gg3vN4aW2yUuLm5InAPH0siAwbxAytKGn+u7O9vZ2Uk2bdpEJkyYMDTWi5TCMAxJSEggZrN5nFTgP+MizPb2dmopw7TExcWRjRs3BnxPOzs7ycqVK4lcLue9qxpqUSqVYSdQzoXZ3t5OkpKSeP/yafFdGIbxW5w9PT3EaDSS+Ph4IhaLeW87m8Ut0JqaGk414Q+cCpOKMnIKwzBk165do97PqqoqotFoiEKh4L29XBa1Wk10Oh2vM7mcCfP69eskJSWF9y+ZFv/LlClTvC6luDcVqFQq3ts4noVhGGI0GklPTw9XMvEJJ54/DocDjzzyCFpaWtiuOuqRy+WQSCQecW6Tk5OhVqvvODcnJwcA8NFHH+G///0vrl27ht7eXvT19QUdnnPKlCloaGgYiixfX1+PZ555Bt98802QnyiyUSgUiI+Px5EjR8Y1ciEnwnzqqafw/vvvo6+vj+2qowaGYYYiuLtFl5OTM5RuQavVshodr6GhAXa7Hc3Nzaivr4fNZsNXX30FAIiJiUF/f//QuSqVCrt27cJf/vIXfPTRR9RdEkB8fDxKSkqwevXqcbke68Kkvq+eiEQiKJVKdHV1ITU1FUuXLkV6enpY5Dxxi7WjowN//etfceLECXz33XdwuVx3iJUyEFZ00aJFOHjwIPfpLtjsFzc2NpKEhATexwZ8F7frmUgkIjqdjpSUlESU7+bhw4eJRqMharWayOVyIpVKSUxMDO/fazgUsVhMZsyY4ZfPcSiwJszOzk4ybdo03r84vopMJiNKpZJotdqIE+JwTCYTSU9PJ+3t7aSqqoqsWLGCnDt3jphMJpKZmUkkEknULZMEU6ZMmULOnj3L2X1gRZg9PT0kNTWV9y+Lj6JWq0liYiLZvn17xDlKD6enp4esWLGC/PKXvyQ9PT3k448/Jh9//DHZtGkTKS0tveM8s9k8tDtEKBTyfg/4KhqNhrMHMCvCNBqNUb+2NVKMKpWKPPfcc5w+NceLc+fOEa1WS6qqqoYE6S49PT0kMzOTWK1Wj/e1t7eT7du3k8TERCKXy3m/L3yUhISEUXfrBEvIwmxvbw9rp2U2i1KpJNnZ2WHhGcIWVVVVRKfTkXPnznmI0l06OzuJVqslnZ2dPus5e/Ysee6554hSqSRSqZT3ezWexdf6byiEPCs7b948NDU1hVJFWCMSiSCXy5Gbm4utW7dGzU55h8OBoqIi2Gw2mEymMdecRSIRioqKYLFYRp2RdDgceOutt1BSUoLu7m5Oco6GI1OmTMHevXvx5JNPslJfSMLct28fNm/eHJVxemQyGWQyGX7yk59g27Zt0Gg0fDeJNWw2G/R6PXJycpCamur3+86cOYOLFy+itLR0zHMdDgcqKirw6quvoru7Gz09PaE0OSKIj4/Hhx9+yIojQtDCtNlsmDt3Lmw2W8iNCCcUCgViYmKwbt06bNmyJepS4NXX18NgMMBsNge1Trl//34sXLgwoLQMFRUV2Lx5M3p6etDd3R3wNSOJpKQkWK3WkHtWQUfJW7duXVS5aYnFYjAMg+LiYvzvf//Djh07ok6UxcXFKC8vR2NjY9DOAyaTCZWVlQENX55//nlcuXIFBw4cQHJyMmJjY4O6diTQ0dGBH/zgByFnnwvKYjY3NyM9PT1qurAMw2DZsmV46623ok6MwIB3T15eHhYuXIglS5aEXN/UqVORl5cHq9Ua1Pf1u9/9Djt27IDdbofT6Qy5PeGGRCKBVqvF6dOng64jKGEuWrQIVqs16IuGCxMmTMC9994Ls9kctQl5mpqaUFBQAJPJBJFIxFq9/f392Lp1a9C/A5vNhnXr1uH48eNR84AfjkqlwpYtW1BUVBTU+wMWZjRYS7FYDIVCgW3btmH9+vV8N4czysrKcOTIEVRVVaGtrY31+v/xj3/gxo0bwQWUHqShoQErV67E9evXo26CKJTJoIDHmC+++GJEi5JhGOTn56O9vT1qRWm326HX63Hx4kVYLBZORAkACxYsQGtrK2pra4OuIz09HZcvX8ZvfvMbxMfHs9e4MOCbb75BTk5OUOPNgCxmJFtLsViMyZMn47333ovabisAtLa2oqCgABs3bsTUqVM5v96sWbOQlZUFs9kc8k4Zm82GvLw8fPbZZ7h58yZLLeQXsViM/Px8HDhwIKD3BWQxI9VaMgyDRYsWoaWlJapFWVFRAb1eD7PZPC6iBICWlhaYzWbo9fqQZyI1Gg0aGxuxefPmqJmE6+vrw9GjR1FfXx/Q+/y2mE1NTcjKyoq4fZZxcXF45ZVXYDAY+G4KZzgcDhgMBjgcDphMJpw5c2bc23D16lVUVlaipqaGlfqamprw5JNP4sqVK6zUxzfJycn48ssv/T7fb4tpMpkiSpRisRjTpk3DyZMno1qUHR0dyMjIwJw5c/Dzn/+cF1ECQEJCAlJSUrBjxw5W6tPpdDh//jx0Oh3kcjkrdfLJ1atX8fvf/97v8/0SpsPhwKFDkZO+dOLEifjhD3+IL774gvcIAVxSW1uLvLw8mEwmzJo1i+/mYNmyZTh27NhQ9q5QYRgmarq23333HYqLi/3u7vslzOrqalbXwLgkISEB5eXlOHz4MPfhH3ikqKgIlZWVsFqtQQfe4oKamhoYDAZ0dHSwVmdxcTEsFgsSExNZq5MPent7/e5R+DXGnDlzJlpbW0NuGNdoNBpUVVUhPT2d76ZwhtsBfenSpViwYAHfzfGKvztRAqWjowNZWVkR8Vv0hUqlwhdffDHmpogxLWZra6vPJKLhRGJiIiwWS1SL0j0BZzQaw1aUwIBX0KpVq1BYWMhqvUlJSWhsbERqamrE9OBG4nQ6/Vo6GVOYe/bsCfs1pXvvvRcnT54c17if482OHTtgMBhgsVgi4kc5ffp0yGQy7N69m9V6GYbBxx9/jLS0tIh0hu/u7kZJScmYY80xhXngwIGwDWMokUiQkpKClpaWqNnAPBK3A/qNGzewc+dOzrx4uKC0tBTvvvsu6xvpZTIZTpw4gSeeeMIjCHYk4HQ6x1zXHHWM2dHRgYceeiisJhfcyOVyzJkzB3V1dRE/Y+eL5uZmFBQUwGg0IiEhge/mBMXUqVOh1+tRU1PDyWbzTZs2oaKiIuIcX8Za1xzVYtbX18PlcrHeqFCRSCSYM2cOjh8/HrWi3L17NwoLC1FTUxOxogSAS5cuoaSkBAUFBSF7BnmjrKwMRqMREydOZL1uLrl27Rqam5t9vj6qMA8dOhSW48v7778fx48fj8rlEIfDgYKCAnz66aewWq24dOkS300KGZFIhKVLlwa9BWosDAYDnnvuuYjq1nZ3d6OystLn6z67sg6HAxMnTuTkKRcKKSkpaGxsjEpL6XZAX7t2LaZPn853c1jn7bffRk5ODlasWMFJ/Xq9HrW1tWH3m/VFQkKCT5dDnxazqakp7CzStGnTgt41H+7U1tYObWiORlECgNlsxmuvvcbZOmRVVRUeffTRiJi1BgaWlXxNjPkU5ocffhhWkz6JiYk4ceJEVEWrc2MwGFBZWQmLxRJW3znbnDlzBjU1NdDr9Zz5XR87dgyPPPIIxGIxJ/WzSVdXF959912vr/nsyoZT+BCNRgOLxRJ165Tu/YdPP/10QGEkIx22d6KMxG63Y968eRHhIeSrO+vTYv7rX//itEH+kpCQgJqamqgTZUNDAzIyMlBaWnpXiRIYuKdz5sxBcXExJ/UzDAOr1Ypp06ZxUj+bOBwOr37FXoVpt9vDIoK2SqXCiy++GHWbm4uLi7F169aQwkhGOkuWLMHp06cD3kDsLxqNBseOHUNcXBwn9bOJt904XoXZ3NzMu7uTWCyGVqvlbIqdD+x2O7KysgAA27dvHzMtQbRTVVWFoqIiVneiDMe9P1SlUnFSPxt0dXXhk08+8TjuU5h8RyybPHkyjhw5wmsb2KSpqQkZGRnYuHEjK7Fdo4GWlhZUVVUhLy+PsyWO1atXY/HixWE9U+ut1+BVmGfPnuW1K5uQkID33nsvapZFdu/ejaKiItTU1ECpVPLdnLDi6tWreOmll1BQUMDZNQ4ePBjWezm/+uorjweTV2F+9tln49Igb0TTuNLhcECv1+PTTz+FxWKJCi8eLpg6dSo0Gg3Kyso4qV8mk4X1eFOpVHq453kVJl8/ILFYjMWLF0fFuLK1tRXz5s1DTk4OVq5cyVssnkihpKSE1bAkIwnn8abL5fIYZ/ucleWD+Ph4HDx4kJdrs0l1dTX0ej2qqqrGLYxkpHPmzBmYzWYYDAbOMsi5x5tCoZCT+oOlt7fX4zPfIUzi6se3Xd9BoIoF1ApgHD3yGIaB2WwOOzfAQHA4HCgsLMSRI0fQ2NiIq1ev8t2kiKKtrQ0mk4mVGLW++OMf/xh2Xdre3l6cP3/+jmN3CFMQI8K1K/+DTCwGbnYD4+QLLBaL8dhjjyEzM3N8LsgB7ng0c+bMwQsvvEC7rkHS39+PnJwczoYzDMPgjTfeCLsu7UgvJY+ubMfFi4iJCTptZlCoVCpUVFSM6zXZpL6+Hnl5eSgpKQmLMJKRTmpqKux2O2e/iWeffRbf//73Oak7WDo7O+/432Nxx27/dvAvMaCWDPzpugV81wfIZIBUCPQOWlOxGJAPntM7zMIOPw4ncGOY6R3x2gSXGEaj0dM5nThxy+kCAQBBDCRCIQQjj0MAsUiEGBD09/fDCcHA+MHZDycAxIggFbhunx8jgjRGENAXNhbFxcU4ffo0rFbrXe8wwCYmkwlZWVlISUnhZIbebDZj8eLFuHbtGut1B8PIYY+HabTbrw9GLegDbt4C4BwQJQA4nED/rUEBigfGoDe6gRu3AKkMEA8elwsHusI3uoF+IaBwe/p7vjbpnslesm4R9DsJYoRiSEUiCOGCk4w8LoY4hqDPRQAIIBLGQAACp9OJGJEYUmEMBK5+9LoA8dD/TrAVj8FutyMjIwMA8PLLL1NRsox7MqiwsJCTyUitVouVK1eG7S6U0fusfX1AL24LSyYEnH23/46RDEwSqSUAhIBw8LjLCQyehlu3AJHw9nuGvaaWSlFd9a6nWIgTzhghRAJgQHTigb+JE06BAMJBoxcjGCk2AYRC0bAPJYAwZtDSDuIKOE2vJ01NTZg3bx6MRiP14uGQS5cuobS0FHl5eZzU//rrr2PChAmc1B0qvoXpFpbDOSAsMQCh884Jof5bgxZzsLhfixllOnrwNZFIhPzlyzFn9hzv5xF3d3XkceL9+DhRVlYGg8EAq9Ua1m5e0QKXYUlkMhn2798flt5YQ8J0ufrQ6yIDyxXEOeyUQaspV9y2lsCgYCXDllQGu7YOJwDh7eNCIdDvvP2ewddUKhXeLC8FiRF6eToIICDu7isAkAFLJxBCCIL+IbNHfLyffdxhJC9evBhxYSQjHTYS5PoiNzc3LCfshn7TAggAVz/iJ09GTP+ICRKHE8AIa4k+oNcJSBWD3Vnh4OuDY1P3cbET6O67/Z6bt6BQxWHz/9sCgUQGydBkDEF/fx/6BwUoEQrgdPaht78Pvf1OYKhbGwO4+geOuwSD7yfod7pAQOB09sM15v+B0dzcjKysLKxatQrLly8P8N0UNuAyLInJZAo7q+kRwaChoQF5eXkjBtxiQNbH2rpmXFwcLl++HBHOBBUVFdizZw/MZjN1GOCZhIQE6PV6NDY2sv7b4Ttix8hIBv71AmVCwDn2af6gUChQVFQU9qJ0e/GcOHECVquVijIMuHr1KoxGI/R6Pet179q1i1erOdJ100OYdwhGMdgdheP2ZFCISKVS/OpXv2KnMo4Il2SwFE/YTpDrRqvVYtKkSazWGQgjDZWHMDUaze1wF90jZltDJBKsZbglg6V4wnaCXDe7du3iLXv1mBYzKSmJsxCK4W4twzUZLMUTLhLk5ubmYsqUKazVFwgjrbXXMSYXDr7hbC1tNhvmzZsHtVqNX//619SLJwJoaWmByWRiPScKH1ZTLpdj9uzZdxzzKsz4+HjWLy4UCvHMM8+wXm+oNDQ0ICsrC6WlpWGdDJbiCRcJcnNzczF58mTW6vMHiUTiEZ7VqzC5MOcPP/xw2EVR37FjB7Zu3QqLxXLXhpGMdLhIkPvSSy+Nq1dXT08PUlJS7jjmVZhs77pXq9XYsGEDq3WGwvBksNu3b6dePBEO2wlyc3NzIZFIxj6RJaRSqUfgOa/CfOCBB1h9YgiFwrDZBN3c3IyMjAysWrUKy5Yt47s5FBZgOyyJRqPBY489xkLL/OPBBx/0OOZVmAsWLGBtsVUkEmH58uVhMekTLclgKZ6wnSB3w4YNkEqlLLRsdEQiERYvXuxx3KswdTodawGfFQoF1q1bx0pdwRKNyWApnrC5EyUzM3NcjIlSqfS6ddCrMBmGYW1m9p577uE1IZA7jOTChQtpGMm7gNTUVNhsNlRXV4dUj0wmQ05ODkut8o3D4fAaocGnrywbYpLL5di4cWPI9QRLdXU1CgoKUFVVFbXJYCmesLUTpbCwkHOrqdVqvV7DpzCfeOKJkPvYTqeTs7Teo+FwOGAwGHDkyBFYLBbqgH6XwVaCXJ1Ox2mEA6lUiqefftrraz6FqdPpQs74NXPmzHHPP2Kz2ZCVlYX77rsPL7zwAvXiuUu5dOkSjEZjyDlR1qxZw1KLPJFKpT5XK0btyjqdwe/1EolE474c4U4GW1JSctclg6V4wkaC3CVLlnAWsCs+Pt7DscCNT2HKZDI8/vjjQV/U12wTV9BksBRvhJogV6fTcZJSYazVilE3Sj/77LNBO7Q7nc5xydhFk8FSxiKUBLkymQxz585lv1EAfvGLX/h8bVRhZmZmBt2dTU1N5XxGyx1GkiaDpYxGqAly8/PzIRCwGyh84cKFo86/jCpMhmHw0EMPBXxRqVSKJ598MuD3BUJZWRmKiopgtVrDLpASJfwIJUFueno6q+NMhmG8BDm/kzFj/qxatSrg/WmjzTaFit1uh16vx8WLF2GxWKgDOsVvgk2Qy7aDzMSJE8fUx5jCzM3NDfjCEonE52xTKLS2tiIrKws5OTlYvnw59eKhBEywCXLZGmeq1Wrs2rVrzPPGFGYwnvaPPvpoQOf7gzsZrNlspslgKUET7E6UH//4x6xcPy4uzi9j51f4yg0bNkCtVvt1YZFIFNIyy0hoMlgK2wSTIHfJkiUhp6f011oCXgI+++Lee+/F119/7dfFKyoqguoCj6SjowMFBQV4+umnacQ6CuucOXMGFy9eRGlp6ZjnOhwOyOVy+CkXryQnJ+PLL7/061y/HwEGgwEKhWLM8wghrIwvaTJYCtcEkiBXJpOFtPynVqtx+PBhv8/3W5ijLYYO5+bNmyELs6ioCOXl5bBardSLh8IpJpMJlZWVfoUlCTZmlUgkQn5+fkCzu34Lk2EY/PSnPx0z5Mj3vvc9vy8+EpvNhoyMDKjVapoMljIuBJIgN9iZWZVKhTfffDOg9wQ0ml23bt2Y3dmZM2cG1AA3TU1NyMrKgtFopGEkKeOKvwlytVptwB5AarUa+/fvD7gbHJAwtVot8vPzfVrNYGdk3clgLRYLTQZL4QV/wpI88sgjAdUpFouxaNGioCZCA57/3bZtm09PIIVCEdD4kiaDpYQTYyXITUlJCchi3nPPPTh48GBQbQlYmBqNBqtWrfJqmgOZkaXJYCnhyGhhSZKSkvxeLomLi8OxY8eCnskNasX09ddf92o1XS6XXw2pqKhAYWEhzGYzDSNJCStaWlpgNpt9Oh/4EwharVZjx44dIa1OBCVMmUyGoqIij4kgh8Mx6pSyO4wkTQZLCWdGS5A7Vhws97hy9erVIbXBb8+fkTgcDsyePRsXLly447iv6lpbW1FQUIC1a9fSiHWUiODo0aNQq9XYsmXL0LH4+Hhcu3bN6/kSiQQPP/wwjh8/HvJe5KCd/2QyGd555507Nnv6inZQW1uLgoICmEwmKkpKxOAtQe5oET1mz57NiiiBEIQJDMRDefbZZ4ca4q3/bTAYUFlZCYvFQpPBUiKOkQlyfW2YTklJwQcffMBa1I7Q3OUxMBHk9vYZvh3LnQz2vvvuo8lgKRHLyAS53nZZpaSkoLGxkdVQrSELUyaT4dChQ3eY+OHJYGkYSUqkMzxB7vAJT3dAALZFCbAgTGDAIygtLQ0zZsygyWApUYk7Qa5UKoVQKERsbCy0Wi0nogQCEKZlrQACwYgyvxxuX50lS5bAarWitbUVf//736kXDyXqKC0txX/+8x/I5XLk5OTg9OnTnGUaCGi5pK18PmZ8/grIniwAFqwVZGPvmjqQPVk4cOAAtmzZgsuXL7PeSK1Wy8oXkJ6ePvR3RUVFUHFGKRSRSIS+vj5OrxGAMNtQPn8GPn+FYM9AfGVY1gqQjbpBoVIo0U9HRwc6OjrueMgDACxrIcjeO/TvmjqCPViLtdiDYOTh/xiz7SgOnVqD3MGLtJXPR/beNJRtpqKk3D0kJSWNEGUbyucLIMgG6ggBGSy5tQIIslvw4IzgruO3MNuOHsIp7EX24PhyFSpByElspP4ClLuWgV7kJpThAtmD4SYqa08d1mAW7g9SH34Ksw1HD53CmrrBJ8KFMmDTDMwvpxM8lLuXtvJV2HQqDWWVG+Gpvxl4sCwXwfYn/RtjtpVj/ozP8cqwp4JlrQDZe9egbsSTgkK5Oxi0lrO4mWPxy2K2HT2EU2uGq78N51sApD2IILvQFEqEcwGfnwLSgh1EjoEfcTwGu7Gv3JalZe0MbDoFrKnzZsIplLuAtvPg0sl0TIspEAyIcG/2bceC7L1pKLtAgpoGplCigun3YxaAU59f8Pqypfy2800wBL0fk0K56xlcu0wru4CTQ8sTnuv9wUCFSaGEQls55s/YhFNDB9JQdiH0ZUQqTAolDGFldwmFQmEXKkwKJQz5/6GFNae7ZI7lAAAAAElFTkSuQmCC\n"
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ],
+      "image/png": "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"
      },
-     "execution_count": 2,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
-   "source": [
-    "from PIL import Image\n",
-    "img = Image.open(\"../../asset/_static/item_figure.png\")\n",
-    "figures = {\"1\": img}\n",
-    "img"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -75,7 +100,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Preparation"
+    "## 导入模块"
    ],
    "metadata": {
     "collapsed": false
@@ -83,11 +108,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [],
+   "execution_count": 2,
    "source": [
-    "from EduNLP.SIF import sif4sci"
+    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
    ],
+   "outputs": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -98,23 +123,85 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Verification\n",
-    "\n",
-    "## Auto Correction"
+    "## 验证题目格式"
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "is_sif(item['stem'])"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
     }
-   }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "## Segment and Tokenization\n",
+    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "is_sif(text)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "to_sif(text)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 题目切分及令牌化\n",
     "\n",
-    "After we verify an item obeys SIF, we can further process it, i.e., segment and tokenization."
+    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
    ],
    "metadata": {
     "collapsed": false,
@@ -126,7 +213,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Segment"
+    "### 题目切分"
    ],
    "metadata": {
     "collapsed": false,
@@ -135,162 +222,220 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 基本切分\n",
+    "分离文本、公式、图片和特殊符号。"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")\n"
+    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\n",
+    "segments"
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "execution_count": 4,
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
+      ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 12
     }
-   ]
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本部分"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
+   "source": [
+    "segments.text_segments"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
+       " '的斜边',\n",
+       " ', 直角边',\n",
+       " ', ',\n",
+       " '.',\n",
+       " '的三边所围成的区域记为',\n",
+       " ',黑色部分记为',\n",
+       " ', 其余部分记为',\n",
+       " '.在整个图形中随机取一点，此点取自',\n",
+       " '的概率分别记为',\n",
+       " ',则']"
+      ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 13
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\n",
-    "segments"
+    "- 公式部分"
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
+   "source": [
+    "segments.formula_segments\n"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n '的斜边',\n ', 直角边',\n ', ',\n '.',\n '的三边所围成的区域记为',\n ',黑色部分记为',\n ', 其余部分记为',\n '.在整个图形中随机取一点，此点取自',\n '的概率分别记为',\n ',则']"
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I,II,III',\n",
+       " 'p_1,p_2,p_3']"
+      ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 15
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "segments.text_segments"
+    "- 图片部分"
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
+   "source": [
+    "segments.figure_segments"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "[\\FigureID{1}]"
+      "text/plain": [
+       "[\\FigureID{1}]"
+      ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 16
     }
    ],
-   "source": [
-    "segments.figure_segments"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
+   "source": [
+    "segments.figure_segments[0].figure"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x23347BF9D60>",
-      "image/png": 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\n"
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ],
+      "image/png": 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"
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 17
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "segments.figure_segments[0].figure"
+    "- 特殊符号"
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
+   "source": [
+    "segments.ques_mark_segments"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "['ABC',\n 'BC',\n 'AB',\n 'AC',\n '\\\\bigtriangleup ABC',\n 'I',\n 'II',\n 'III',\n 'I,II,III',\n 'p_1,p_2,p_3']"
+      "text/plain": [
+       "['\\\\SIFChoice']"
+      ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 19
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "segments.formula_segments"
+    "#### 标记化切分  \n",
+    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    " - symbol:\n",
+    "    - \"t\": text\n",
+    "    - \"f\": formula\n",
+    "    - \"g\": figure\n",
+    "    - \"m\": question mark"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "['\\\\SIFChoice']"
+      "text/plain": [
+       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
+      ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 11
     }
    ],
-   "source": [
-    "segments.ques_mark_segments"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -301,7 +446,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tokenization"
+    "### 令牌化\n",
+    "\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
+    "\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
    ],
    "metadata": {
     "collapsed": false,
@@ -313,10 +462,10 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "outputs": [],
    "source": [
     "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
    ],
+   "outputs": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -327,7 +476,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "#### Text"
+    "- 文本解析结果"
    ],
    "metadata": {
     "collapsed": false,
@@ -339,19 +488,53 @@
   {
    "cell_type": "code",
    "execution_count": 12,
+   "source": [
+    "tokens.text_tokens"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "['如图',\n '古希腊',\n '数学家',\n '希波',\n '克拉底',\n '研究',\n '几何图形',\n '此图',\n '三个',\n '半圆',\n '三个',\n '半圆',\n '直径',\n '直角三角形',\n '斜边',\n '直角',\n '三边',\n '围成',\n '区域',\n '记',\n '黑色',\n '记',\n '其余部分',\n '记',\n '图形',\n '中',\n '随机',\n '取',\n '一点',\n '此点',\n '取自',\n '概率',\n '记']"
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " '斜边',\n",
+       " '直角',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " '概率',\n",
+       " '记']"
+      ]
      },
-     "execution_count": 12,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 12
     }
    ],
-   "source": [
-    "tokens.text_tokens"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -362,7 +545,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "#### Formula"
+    "#### 公式解析结果"
    ],
    "metadata": {
     "collapsed": false,
@@ -374,19 +557,45 @@
   {
    "cell_type": "code",
    "execution_count": 13,
+   "source": [
+    "tokens.formula_tokens"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "['ABC',\n 'BC',\n 'AB',\n 'AC',\n '\\\\bigtriangleup',\n 'ABC',\n 'I',\n 'II',\n 'III',\n 'I',\n ',',\n 'II',\n ',',\n 'III',\n 'p',\n '_',\n '1',\n ',',\n 'p',\n '_',\n '2',\n ',',\n 'p',\n '_',\n '3']"
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
      },
-     "execution_count": 13,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 13
     }
    ],
-   "source": [
-    "tokens.formula_tokens"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -395,18 +604,22 @@
    }
   },
   {
-   "cell_type": "code",
-   "execution_count": 14,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "['A',\n 'B',\n 'C',\n 'B',\n 'C',\n 'A',\n 'B',\n 'A',\n 'C',\n '\\\\bigtriangleup',\n 'A',\n 'B',\n 'C',\n 'I',\n 'I',\n 'I',\n 'I',\n 'I',\n 'I',\n 'I',\n ',',\n 'I',\n 'I',\n ',',\n 'I',\n 'I',\n 'I',\n 'p',\n '1',\n '_',\n ',',\n 'p',\n '2',\n '_',\n ',',\n 'p',\n '3',\n '_']"
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "cell_type": "markdown",
+   "source": [
+    "- 自定义参数，得到定制化解析结果"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
    "source": [
     "sif4sci(\n",
     "    item[\"stem\"],\n",
@@ -414,12 +627,47 @@
     "    tokenization=True,\n",
     "    tokenization_params={\n",
     "        \"formula_params\": {\n",
-    "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"list\"\n",
+    "            \"method\": \"linear\",\n",
     "        }\n",
     "    }\n",
     ").formula_tokens"
    ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -428,18 +676,17 @@
    }
   },
   {
-   "cell_type": "code",
-   "execution_count": 15,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "['mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n '\\\\bigtriangleup',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n ',',\n 'mathord',\n 'mathord',\n ',',\n 'mathord',\n 'mathord',\n 'mathord',\n 'mathord',\n 'textord',\n '_',\n ',',\n 'mathord',\n 'textord',\n '_',\n ',',\n 'mathord',\n 'textord',\n '_']"
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "source": [
     "sif4sci(\n",
     "    item[\"stem\"],\n",
@@ -448,11 +695,30 @@
     "    tokenization_params={\n",
     "        \"formula_params\":{\n",
     "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"list\",\n",
-    "            \"ord2token\": True\n",
     "        }\n",
     "    }\n",
-    ").formula_tokens"
+    ").formula_tokens\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<Formula: ABC>,\n",
+       " <Formula: BC>,\n",
+       " <Formula: AB>,\n",
+       " <Formula: AC>,\n",
+       " <Formula: \\bigtriangleup ABC>,\n",
+       " <Formula: I>,\n",
+       " <Formula: II>,\n",
+       " <Formula: III>,\n",
+       " <Formula: I,II,III>,\n",
+       " <Formula: p_1,p_2,p_3>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
    ],
    "metadata": {
     "collapsed": false,
@@ -462,53 +728,78 @@
    }
   },
   {
-   "cell_type": "code",
-   "execution_count": 16,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "['mathord_0',\n 'mathord_1',\n 'mathord_2',\n 'mathord_0',\n 'mathord_1',\n 'mathord_0',\n 'mathord_1',\n 'mathord_0',\n 'mathord_1',\n '\\\\bigtriangleup',\n 'mathord_0',\n 'mathord_1',\n 'mathord_2',\n 'mathord_0',\n 'mathord_0',\n 'mathord_0',\n 'mathord_0',\n 'mathord_0',\n 'mathord_0',\n 'mathord_0',\n ',',\n 'mathord_0',\n 'mathord_0',\n ',',\n 'mathord_0',\n 'mathord_0',\n 'mathord_0',\n 'mathord_0',\n 'textord',\n '_',\n ',',\n 'mathord_0',\n 'textord',\n '_',\n ',',\n 'mathord_0',\n 'textord',\n '_']"
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "cell_type": "markdown",
+   "source": [
+    "- 语法树展示："
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
    "source": [
-    "sif4sci(\n",
+    "f = sif4sci(\n",
     "    item[\"stem\"],\n",
     "    figures=figures,\n",
     "    tokenization=True,\n",
     "    tokenization_params={\n",
     "        \"formula_params\":{\n",
     "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"list\",\n",
+    "            \"return_type\": \"ast\",\n",
     "            \"ord2token\": True,\n",
-    "            \"var_numbering\": True\n",
+    "            \"var_numbering\": True,\n",
     "        }\n",
     "    }\n",
-    ").formula_tokens"
+    ").formula_tokens\n",
+    "f\n"
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "[<Formula: ABC>,\n <Formula: BC>,\n <Formula: AB>,\n <Formula: AC>,\n <Formula: \\bigtriangleup ABC>,\n <Formula: I>,\n <Formula: II>,\n <Formula: III>,\n <Formula: I,II,III>,\n <Formula: p_1,p_2,p_3>]"
+      "text/plain": [
+       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
+      ]
      },
-     "execution_count": 17,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 109
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "source": [
+    "for i in range(0, len(f)):\n",
+    "    ForestPlotter().export(\n",
+    "        f[i], root_list=[node for node in f[i]],\n",
+    "    )\n",
+    "# plt.show()\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "source": [
     "sif4sci(\n",
     "    item[\"stem\"],\n",
@@ -517,13 +808,61 @@
     "    tokenization_params={\n",
     "        \"formula_params\":{\n",
     "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"formula\",\n",
+    "            \"return_type\": \"list\",\n",
     "            \"ord2token\": True,\n",
-    "            \"var_numbering\": True\n",
     "        }\n",
     "    }\n",
     ").formula_tokens"
    ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -532,18 +871,15 @@
    }
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "[<networkx.classes.digraph.DiGraph at 0x23356118d00>,\n <networkx.classes.digraph.DiGraph at 0x23356118c70>,\n <networkx.classes.digraph.DiGraph at 0x23356118d30>,\n <networkx.classes.digraph.DiGraph at 0x23356118e80>,\n <networkx.classes.digraph.DiGraph at 0x233561189a0>,\n <networkx.classes.digraph.DiGraph at 0x233561187c0>,\n <networkx.classes.digraph.DiGraph at 0x233561185e0>,\n <networkx.classes.digraph.DiGraph at 0x233561186a0>,\n <networkx.classes.digraph.DiGraph at 0x23347bc7b50>,\n <networkx.classes.digraph.DiGraph at 0x23347bc7a30>]"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
    "source": [
     "sif4sci(\n",
     "    item[\"stem\"],\n",
@@ -552,14 +888,61 @@
     "    tokenization_params={\n",
     "        \"formula_params\":{\n",
     "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"ast\",\n",
     "            \"ord2token\": True,\n",
+    "            \"return_type\": \"list\",\n",
     "            \"var_numbering\": True\n",
     "        }\n",
     "    }\n",
-    ").formula_tokens\n",
-    "\n",
-    "#### Figure"
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_0',\n",
+       " 'mathord_2',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
    ],
    "metadata": {
     "collapsed": false,
@@ -571,9 +954,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Downstream tasks\n",
+    "## 综合训练\n",
     "\n",
-    "### Word to vector"
+    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
    ],
    "metadata": {
     "collapsed": false,
@@ -584,20 +967,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 96,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\n",
+    "        symbol=\"fgm\")"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
+      "text/plain": [
+       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
+      ]
      },
-     "execution_count": 19,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 96
     }
    ],
-   "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=True, symbol=\"fgm\")"
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -608,23 +994,25 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
   },
   "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
+   "pygments_lexer": "ipython3",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "file_extension": ".py"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 2
 }
\ No newline at end of file
diff --git a/examples/sif/sif_additon.ipynb b/examples/sif/sif_additon.ipynb
deleted file mode 100644
index bdefc769..00000000
--- a/examples/sif/sif_additon.ipynb
+++ /dev/null
@@ -1,733 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "source": [
-    "from EduNLP.SIF import is_sif, to_sif,sif4sci"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## is_sif"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    " text = '若$x,y$满足约束条件' \\\n",
-    "     '$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，' \\\n",
-    "    '则$z=x+7 y$的最大值$\\\\SIFUnderline$'\n",
-    "    \n",
-    "is_sif(text)\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 4
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
-    "is_sif(text)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 5
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## to_sif"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
-    "to_sif(text)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 6
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## sif4sci\n",
-    " to_symbolize:\n",
-    " - \"t\": text\n",
-    " - \"f\": formula\n",
-    " - \"g\": figure\n",
-    " - \"m\": question mark"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "source": [
-    " test_item = r\"如图所示，则$\\bigtriangleup ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$\"\n",
-    " t1 = sif4sci(test_item)\n",
-    " t1"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图所示', '\\\\bigtriangleup', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 14
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "source": [
-    "t1.describe()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "{'t': 2, 'f': 2, 'g': 1, 'm': 1}"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 15
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "source": [
-    "with t1.filter('fgm'):\n",
-    "    print(t1)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "['如图所示', '面积']\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "source": [
-    "with t1.filter(keep='t'):\n",
-    "    print(t1)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "['如图所示', '面积']\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "source": [
-    "with t1.filter():\n",
-    "    print(t1)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "['如图所示', '\\\\bigtriangleup', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "t1.text_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图所示', '面积']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 20
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "source": [
-    "t1.formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\bigtriangleup', 'ABC']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 23
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "source": [
-    "t1.figure_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[\\FigureID{1}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 24
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "source": [
-    "t1.ques_mark_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\SIFBlank']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 25
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "source": [
-    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\"}})"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图所示', <Formula: \\bigtriangleup ABC>, '面积', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 26
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "source": [
-    "sif4sci(test_item, symbol=\"tfgm\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 27
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "source": [
-    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图所示', '\\\\bigtriangleup', 'A', 'B', 'C', '面积', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 28
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "source": [
-    " test_item_1 = {\n",
-    "     \"stem\": r\"若$x=2$, $y=\\sqrt{x}$，则下列说法正确的是$\\SIFChoice$\",\n",
-    "     \"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]\n",
-    "  }"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "source": [
-    " tls = [\n",
-    "         sif4sci(e, symbol=\"gm\",\n",
-    "         tokenization_params={\n",
-    "            \"formula_params\": {\n",
-    "                 \"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True, \"var_numbering\": True,\n",
-    "                 \"link_variable\": False}\n",
-    "         })\n",
-    "         for e in ([test_item_1[\"stem\"]] + test_item_1[\"options\"])\n",
-    "     ]"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "source": [
-    "tls"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['mathord_0', '=', 'textord', 'mathord_1', '=', 'mathord_0', '{ }', '\\\\sqrt', '说法', '正确', '[MARK]'],\n",
-       " ['mathord_0', '<', 'mathord_1'],\n",
-       " ['mathord_0', '=', 'mathord_1'],\n",
-       " ['mathord_0', '<', 'mathord_1']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 33
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "source": [
-    "tls[1:]"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['mathord_0', '<', 'mathord_1'],\n",
-       " ['mathord_0', '=', 'mathord_1'],\n",
-       " ['mathord_0', '<', 'mathord_1']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 34
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "source": [
-    "from EduNLP.utils import dict2str4sif\n",
-    "\n",
-    "test_item_1_str = dict2str4sif(test_item_1, tag_mode=\"head\", add_list_no_tag=False)\n",
-    "test_item_1_str "
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "'$\\\\SIFTag{stem}$若$x=2$, $y=\\\\sqrt{x}$，则下列说法正确的是$\\\\SIFChoice$$\\\\SIFTag{options}$$x < y$$\\\\SIFSep$$y = x$$\\\\SIFSep$$y < x$'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 35
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "source": [
-    "tl1 = sif4sci(\n",
-    "    test_item_1_str, \n",
-    "    symbol=\"gm\", \n",
-    "    tokenization_params={\n",
-    "        \"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True}\n",
-    "    })\n",
-    "    "
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "source": [
-    "tl1.get_segments()[0]"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\SIFTag{stem}']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 37
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "source": [
-    "tl1.get_segments()[1:3]"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['[TEXT_BEGIN]', '[TEXT_END]'],\n",
-       " ['[FORMULA_BEGIN]', 'mathord', '=', 'textord', '[FORMULA_END]']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 38
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "source": [
-    "tl1.get_segments(add_seg_type=False)[0:3]"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['\\\\SIFTag{stem}'],\n",
-       " ['mathord', '=', 'textord'],\n",
-       " ['mathord', '=', 'mathord', '{ }', '\\\\sqrt']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 39
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "source": [
-    "test_item_2 = {\"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]}"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "source": [
-    "test_item_2_str = dict2str4sif(test_item_2, tag_mode=\"head\", add_list_no_tag=False)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "source": [
-    "test_item_2_str"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "'$\\\\SIFTag{options}$$x < y$$\\\\SIFSep$$y = x$$\\\\SIFSep$$y < x$'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 43
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "source": [
-    "tl2 = sif4sci(test_item_2_str, symbol=\"gms\",\n",
-    "     tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})\n",
-    "tl2 "
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\SIFTag{options}', 'x', '<', 'y', '[SEP]', 'y', '=', 'x', '[SEP]', 'y', '<', 'x']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 44
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "source": [
-    "tl2.get_segments(add_seg_type=False)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['\\\\SIFTag{options}'],\n",
-       " ['x', '<', 'y'],\n",
-       " ['[SEP]'],\n",
-       " ['y', '=', 'x'],\n",
-       " ['[SEP]'],\n",
-       " ['y', '<', 'x']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 45
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "source": [
-    "tl2.get_segments(add_seg_type=False, drop=\"s\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['\\\\SIFTag{options}'], ['x', '<', 'y'], ['y', '=', 'x'], ['y', '<', 'x']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 46
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "source": [
-    "tl3 = sif4sci(test_item_1[\"stem\"], symbol=\"gs\")\n",
-    "tl3.text_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['说法', '正确']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 47
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "source": [
-    "tl3.formula_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['x', '=', '2'], ['y', '=', '\\\\sqrt', '{', 'x', '}']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 48
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "source": [
-    "tl3.figure_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 49
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "source": [
-    "tl3.ques_mark_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['\\\\SIFChoice']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 50
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.5",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.5 64-bit"
-  },
-  "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/examples/utils/data.ipynb b/examples/utils/data.ipynb
index 536e4aeb..581dd632 100644
--- a/examples/utils/data.ipynb
+++ b/examples/utils/data.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 语义成分分解"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -48,7 +55,7 @@
    "execution_count": 4,
    "source": [
     "# 给题目各个部分加标签\n",
-    "dict2str4sif(item) # doctest: +ELLIPSIS"
+    "dict2str4sif(item)"
    ],
    "outputs": [
     {
@@ -68,7 +75,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "source": [
-    "dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS"
+    "dict2str4sif(item, add_list_no_tag=True)"
    ],
    "outputs": [
     {
@@ -88,7 +95,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "source": [
-    "dict2str4sif(item, tag_mode=\"head\") # doctest: +ELLIPSIS"
+    "dict2str4sif(item, tag_mode=\"head\")"
    ],
    "outputs": [
     {
@@ -108,7 +115,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "dict2str4sif(item, tag_mode=\"tail\") # doctest: +ELLIPSIS"
+    "dict2str4sif(item, tag_mode=\"tail\")"
    ],
    "outputs": [
     {
@@ -128,7 +135,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "source": [
-    "dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS"
+    "dict2str4sif(item, add_list_no_tag=False)"
    ],
    "outputs": [
     {

From 9382147ffe69306888c73d55c63657370a739bf5 Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Mon, 23 Aug 2021 21:39:04 +0800
Subject: [PATCH 014/149] [fix] parser

---
 EduNLP/SIF/parser/parser.py   |  9 ++-------
 tests/test_sif/test_parser.py | 10 ----------
 2 files changed, 2 insertions(+), 17 deletions(-)

diff --git a/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index 67e9a6ac..ff482cdf 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -11,7 +11,7 @@ def __init__(self, data):
         self.error_postion = 0
         self.error_flag = 0
         self.modify_flag = 0
-        self.warning = 0
+        self.warnning = 0
         self.fomula_illegal_flag = 0
         self.fomula_illegal_message = ''
 
@@ -222,15 +222,10 @@ def get_token(self):
             formula_start = self.head
             while self.head < len(self.text) and self.text[self.head] != '$':
                 ch_informula = self.text[self.head]
-                if re.match(
-                        r"\\textf\{.+\}", self.text[self.head:]) is not None:
-                # 公式中的文本应该使用 \\textf{文本} 标记（遵循 latex 语法）
-                    self.head += re.match(
-                        r"\\textf\{.+\}", self.text[self.head:]).span()[1]-1
                 if flag and self.is_chinese(ch_informula):
                     # latex 中出现非法中文字符，打印且只打印一次 warning
                     print("Warning: there is some chinese characters in formula!")
-                    self.warning = 1
+                    self.warnning = 1
                     flag = 0
                 self.head += 1
             if self.head >= len(self.text):
diff --git a/tests/test_sif/test_parser.py b/tests/test_sif/test_parser.py
index 8af2ca2b..34f996d2 100644
--- a/tests/test_sif/test_parser.py
+++ b/tests/test_sif/test_parser.py
@@ -34,13 +34,3 @@ def test_parser():
     text_parser = Parser(text)
     text_parser.description_list()
     assert text_parser.fomula_illegal_flag == 1
-
-    text = r"支持公式中添加文本，但要符合语法，如$\textf{这是文本}ABCD$是允许的"
-    text_parser = Parser(text)
-    text_parser.description_list()
-    assert text_parser.warning == 0
-
-    text = r"支持公式中添加文本，但要符合语法，如若出现：$这是文本ABCD$，会有warning"
-    text_parser = Parser(text)
-    text_parser.description_list()
-    assert text_parser.warning == 1

From 1d9c14af88e72e2e590bc6b8650a52a36853f9aa Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Mon, 23 Aug 2021 21:52:10 +0800
Subject: [PATCH 015/149] [examples] add some detailed explain

---
 examples/pretrain/gensim/d2v_bow_tfidf.ipynb | 67 +++++++-------------
 1 file changed, 22 insertions(+), 45 deletions(-)

diff --git a/examples/pretrain/gensim/d2v_bow_tfidf.ipynb b/examples/pretrain/gensim/d2v_bow_tfidf.ipynb
index 8b8f1671..7bd716b8 100644
--- a/examples/pretrain/gensim/d2v_bow_tfidf.ipynb
+++ b/examples/pretrain/gensim/d2v_bow_tfidf.ipynb
@@ -152,20 +152,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "source": [
     "from EduData import get_data\n",
     "\n",
-    "# 导入项目提供的数据，存放路径：\"../../data/\"  \n",
+    "# 导入项目提供的数据，存放路径：\"../../data/\"\n",
     "get_data(\"open-luna\", \"../../data/\")\n",
     "\n",
     "\n",
     "def load_items():\n",
     "    with open(\"../../../data/OpenLUNA.json\", encoding=\"utf-8\") as f:\n",
     "        for line in f:\n",
-    "            yield json.loads(line)\n",
-    "\n",
-    "\n",
+    "            yield json.loads(line)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "downloader, INFO http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json is saved as ../../data/OpenLUNA.json\n",
+      "downloader, INFO file existed, skipped\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "source": [
     "tokenizer = GensimWordTokenizer(symbol=\"gm\")\n",
     "sif_items = []\n",
     "for item in tqdm(load_items(), \"sifing\"):\n",
@@ -177,45 +192,7 @@
     "\n",
     "sif_items[0]\n"
    ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "downloader, INFO http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json is saved as ../../data/OpenLUNA.json\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Downloading ../../data/OpenLUNA.json 100.00%: 269KB | 269KB\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "sifing: 0it [00:00, ?it/s]\n"
-     ]
-    },
-    {
-     "output_type": "error",
-     "ename": "JSONDecodeError",
-     "evalue": "Expecting property name enclosed in double quotes: line 2 column 1 (char 2)",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_21138/1902388532.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGensimWordTokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"gm\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0msif_items\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mload_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sifing\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     sif_item = tokenizer(\n\u001b[1;32m     17\u001b[0m         \u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"stem\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1186\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/tmp/ipykernel_21138/1902388532.py\u001b[0m in \u001b[0;36mload_items\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../../../data/OpenLUNA.json\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.8/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 357\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    351\u001b[0m         \"\"\"\n\u001b[1;32m    352\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 2 column 1 (char 2)"
-     ]
-    }
-   ],
+   "outputs": [],
    "metadata": {}
   },
   {
@@ -228,7 +205,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 1. 基于词频训练模型"
+    "### 1. 基于 bow 训练模型"
    ],
    "metadata": {
     "pycharm": {

From 172c36a2cd2c41a1f1c5833d7b1abcbc5e3bbd5f Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Mon, 23 Aug 2021 21:57:27 +0800
Subject: [PATCH 016/149] [fix] table

---
 examples/t2v/get_pretrained_t2v.ipynb | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/t2v/get_pretrained_t2v.ipynb b/examples/t2v/get_pretrained_t2v.ipynb
index c0982e81..ea3fa7f8 100644
--- a/examples/t2v/get_pretrained_t2v.ipynb
+++ b/examples/t2v/get_pretrained_t2v.ipynb
@@ -85,12 +85,12 @@
     "## 模型的选择与使用\n",
     "\n",
     "根据题目所属学科选择预训练模型：  \n",
-    "| 预训练模型名称 | 模型训练数据的所属学科 |\n",
-    "| -------------- | ---------------------- |\n",
-    "| d2v_all_256    | 全学科                 |\n",
-    "| d2v_sci_256    | 理科                   |\n",
-    "| d2v_eng_256    | 英语                   |\n",
-    "| d2v_lit_256    | 文科                   |"
+    " 预训练模型名称 | 模型训练数据的所属学科 \n",
+    " -------------- | ---------------------- \n",
+    " d2v_all_256    | 全学科                 \n",
+    " d2v_sci_256    | 理科                   \n",
+    " d2v_eng_256    | 英语                   \n",
+    " d2v_lit_256    | 文科                   "
    ],
    "metadata": {}
   },

From 3bf0068f81670ad862f2ec44bdc56d0223a8e3fc Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Wed, 25 Aug 2021 20:11:24 +0800
Subject: [PATCH 017/149] [BUGFIX] fix the np.error in
 gensim_vec.W2V.infer_vector

---
 EduNLP/Vector/gensim_vec.py      |    4 +-
 examples/vectorization/i2v.ipynb | 9048 +++++++++++++++++++++++++++++-
 tests/test_vec/test_vec.py       |    4 +-
 3 files changed, 8860 insertions(+), 196 deletions(-)

diff --git a/EduNLP/Vector/gensim_vec.py b/EduNLP/Vector/gensim_vec.py
index 13c5600a..23a623b8 100644
--- a/EduNLP/Vector/gensim_vec.py
+++ b/EduNLP/Vector/gensim_vec.py
@@ -62,8 +62,8 @@ def __getitem__(self, item):
         return self.wv[item] if item not in self.constants else np.zeros((self.vector_size,))
 
     def infer_vector(self, items, agg="mean", *args, **kwargs) -> np.ndarray:
-        tokens = self.infer_tokens(items, *args, **kwargs)
-        return eval("np.%s" % agg)(tokens, axis=1)
+        token_vectors = self.infer_tokens(items, *args, **kwargs)
+        return [eval("np.%s" % agg)(item, axis=0) for item in token_vectors]
 
     def infer_tokens(self, items, *args, **kwargs) -> list:
         return [list(self(*item)) for item in items]
diff --git a/examples/vectorization/i2v.ipynb b/examples/vectorization/i2v.ipynb
index 3122fbce..f38ae4fe 100644
--- a/examples/vectorization/i2v.ipynb
+++ b/examples/vectorization/i2v.ipynb
@@ -1,192 +1,8856 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# I2V\n",
-    "\n",
-    "## 概述\n",
-    "\n",
-    "使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。\n",
-    "\n",
-    "- 优点：可以使用自己的模型，另可调整训练参数，灵活性强。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 导入类"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "source": [
-    "from EduNLP.I2V import D2V"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 输入\n",
-    "\n",
-    "类型：str  \n",
-    "内容：题目文本 （text）"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "source": [
-    "item = {\n",
-    "\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
-    "}"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 输出"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "source": [
-    "model_path = \"../test_model/test_gensim_luna_stem_tf_d2v_256.bin\"\n",
-    "i2v = D2V(\"text\",\"d2v\",filepath=model_path, pretrained_t2v = False)\n",
-    "i2v "
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<EduNLP.I2V.i2v.D2V at 0x7fe2517f97c0>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 34
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "source": [
-    "i2v(item)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "([array([ 4.76559885e-02, -1.60574958e-01,  1.94614579e-03,  2.40295693e-01,\n",
-       "          2.24517003e-01, -3.24351490e-02,  4.35789041e-02, -1.65670961e-02,\n",
-       "         -7.77302235e-02,  4.23757173e-02,  4.62658405e-02,  7.54115507e-02,\n",
-       "         -4.54682261e-02, -1.82153687e-01,  5.55203669e-02,  4.23391759e-02,\n",
-       "          8.86691213e-02,  6.97413310e-02, -2.47167766e-01,  2.54209518e-01,\n",
-       "         -3.76413465e-02,  3.58376503e-02, -1.39907554e-01, -8.55517760e-02,\n",
-       "         -1.62535697e-01, -4.44540828e-02, -3.99694731e-03,  1.83905549e-02,\n",
-       "         -8.03738683e-02, -9.05910060e-02,  1.45633578e-01,  9.63102728e-02,\n",
-       "         -7.19666481e-02, -8.49684048e-03, -1.51718438e-01, -1.46381939e-02,\n",
-       "          8.34727809e-02, -7.11122975e-02,  1.66607365e-01, -1.14558250e-01,\n",
-       "         -1.72963589e-01,  4.86062802e-02, -1.63086802e-02, -3.68945636e-02,\n",
-       "          2.46143237e-01,  5.40899672e-03,  5.04904091e-02,  1.16586924e-01,\n",
-       "          7.59096816e-02,  1.20751150e-02,  1.04407202e-02,  3.19544263e-02,\n",
-       "         -6.02783300e-02,  1.18572332e-01, -2.19343737e-01,  2.67594811e-02,\n",
-       "          1.01860933e-01, -2.87170410e-02,  5.16606905e-02,  1.62313670e-01,\n",
-       "         -5.12879491e-02, -1.62193626e-02, -6.77167401e-02,  1.67254247e-02,\n",
-       "          1.10977821e-01,  8.02466944e-02, -2.00764649e-02,  1.28788516e-01,\n",
-       "         -7.20706284e-02, -6.22547232e-02,  1.06899485e-01,  4.60059335e-03,\n",
-       "         -1.99650228e-01, -1.38489634e-01,  7.20307231e-02, -4.98757213e-02,\n",
-       "         -1.94095057e-02, -5.85906627e-03,  1.47433639e-01,  4.68258560e-02,\n",
-       "          9.31144804e-02, -4.59938832e-02,  3.38427201e-02,  4.83937971e-02,\n",
-       "         -1.27312467e-01,  2.01561809e-01,  1.10482745e-01, -1.70595810e-01,\n",
-       "         -9.55015421e-02, -7.73611516e-02,  4.43056040e-02, -1.65684260e-02,\n",
-       "          1.65379923e-02, -1.26138464e-01,  8.31304193e-02,  2.06687212e-01,\n",
-       "         -1.69529378e-01,  3.43789416e-03,  1.19198427e-01, -1.38129979e-01,\n",
-       "         -1.87937781e-01, -8.27087983e-02, -1.76488962e-02,  8.51018950e-02,\n",
-       "          8.15693215e-02,  2.30262652e-02,  1.05074964e-01,  3.13350782e-02,\n",
-       "          1.53877333e-01,  1.01772640e-02,  9.17675197e-02, -1.32400826e-01,\n",
-       "          5.29836975e-02,  2.52282787e-02, -6.19753152e-02, -5.56256585e-02,\n",
-       "          3.87686864e-02,  4.30755690e-02,  7.57815093e-02,  2.63280701e-02,\n",
-       "          4.59217802e-02, -1.17288530e-01,  1.76368475e-01,  9.27482091e-04,\n",
-       "          2.64808517e-02,  9.73805785e-03,  1.90501258e-01,  1.02596413e-02,\n",
-       "         -5.55249080e-02, -1.17555618e-01, -9.98716354e-02,  1.28057361e-01,\n",
-       "         -4.52451073e-02,  7.51599446e-02, -3.01250312e-02,  6.24186322e-02,\n",
-       "          5.77449016e-02,  2.07213312e-02, -2.53734970e-03, -1.69801563e-01,\n",
-       "         -2.28750743e-02, -2.55512260e-02,  1.70693725e-01,  2.35232189e-01,\n",
-       "         -2.71384805e-01, -1.84327438e-01,  4.16823551e-02,  8.70332569e-02,\n",
-       "          1.82847306e-01,  2.76729286e-01, -4.31840494e-02, -1.38212308e-01,\n",
-       "         -3.26297544e-02, -4.25132550e-02, -1.62892416e-01,  1.91870285e-03,\n",
-       "          1.52552709e-01, -1.01523520e-02, -9.16219354e-02, -5.46490997e-02,\n",
-       "          6.06994517e-02, -6.42470419e-02,  7.96310753e-02, -5.70830703e-02,\n",
-       "         -8.82780831e-03, -3.94574478e-02,  9.63162258e-02,  1.54309124e-01,\n",
-       "          1.81100428e-01,  8.63620341e-02,  1.56518817e-02, -4.08006124e-02,\n",
-       "          5.20652272e-02,  8.38029310e-02, -1.55516326e-01,  3.57730500e-03,\n",
-       "         -1.50946556e-02,  2.84812655e-02,  1.37905419e-01,  8.77659023e-02,\n",
-       "          8.23542774e-02, -1.04377635e-01,  4.80731949e-03,  1.18891411e-02,\n",
-       "          9.32120830e-02,  7.88019150e-02, -1.44494563e-01, -7.53350407e-02,\n",
-       "         -1.13602541e-01,  5.43805361e-02,  1.64935380e-01, -2.00515296e-02,\n",
-       "          1.92917317e-01, -4.35359031e-02,  8.92477036e-02, -4.37481068e-02,\n",
-       "          4.01461311e-02, -2.59898454e-01, -1.11872263e-01, -1.25746787e-01,\n",
-       "         -2.34577611e-01, -6.69524372e-02,  5.55978045e-02, -1.91931397e-01,\n",
-       "          5.87355606e-02,  1.01886272e-01, -2.64038593e-01, -2.05450356e-02,\n",
-       "         -1.97510555e-01,  9.13371146e-02,  1.49546817e-01, -3.91026959e-02,\n",
-       "          5.94646595e-02,  1.29657034e-02, -3.72891256e-04,  5.56622408e-02,\n",
-       "          1.61776438e-01,  2.29037628e-02, -1.94774106e-01, -5.02247922e-02,\n",
-       "         -5.45939505e-02,  5.31783216e-02,  1.26433298e-01, -1.23263724e-01,\n",
-       "          8.53074417e-02, -1.41412809e-01, -7.71067888e-02,  1.21865064e-01,\n",
-       "          4.73318882e-02,  7.20091909e-02, -9.83269960e-02,  1.99413914e-02,\n",
-       "         -1.88907124e-02, -2.14710683e-02, -4.93260436e-02,  1.64937660e-01,\n",
-       "         -1.07827298e-01, -7.75848776e-02, -6.23578345e-03, -1.05760902e-01,\n",
-       "         -4.14819457e-02,  5.95730543e-02,  4.11023498e-02, -2.18305327e-02,\n",
-       "         -2.30057724e-02, -3.34391668e-02,  1.30382255e-01,  5.10290638e-02,\n",
-       "         -1.21569566e-01, -1.23630039e-01, -1.83883369e-01,  1.10945016e-01,\n",
-       "         -1.05633408e-01, -8.24846700e-02, -3.76710802e-01, -4.50239740e-02],\n",
-       "        dtype=float32)],\n",
-       " None)"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 35
-    }
-   ],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.5",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.5 64-bit"
-  },
-  "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# I2V\r\n",
+    "\r\n",
+    "## 概述\r\n",
+    "\r\n",
+    "使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。\r\n",
+    "\r\n",
+    "- 优点：可以使用自己的模型，另可调整训练参数，灵活性强。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# D2V"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入类"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "from EduNLP.I2V import D2V"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 输入\r\n",
+    "\r\n",
+    "类型：str  \r\n",
+    "内容：题目文本 （text）"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "items = [\r\n",
+    "r\"1如图几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
+    "r\"2如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
+    "]"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "model_path = \"../../data/d2v/test_d2v_256.bin\"\r\n",
+    "i2v = D2V(\"pure_text\",\"d2v\",filepath=model_path, pretrained_t2v = False)\r\n",
+    "\r\n",
+    "item_vectors, token_vectors = i2v(items)\r\n",
+    "print(item_vectors,  token_vectors)\r\n",
+    "print(len(item_vectors), item_vectors[0].shape) # For d2v, token_vector is None"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[array([ 0.10634826, -0.10273553, -0.04942605,  0.15323296,  0.25780037,\n",
+      "       -0.06630539, -0.02834828,  0.0065832 , -0.07495898,  0.06529831,\n",
+      "        0.07989643,  0.14697047, -0.06513839, -0.23898382,  0.04266138,\n",
+      "        0.15211952,  0.17704317,  0.02768603, -0.1854407 ,  0.21742067,\n",
+      "        0.00984387,  0.06774732, -0.02058181, -0.13266353, -0.20851782,\n",
+      "        0.00558438,  0.00460878, -0.05139542, -0.06485009, -0.04847892,\n",
+      "        0.09366203,  0.13947366, -0.07552954,  0.05977268, -0.09442269,\n",
+      "        0.12721013,  0.13273577, -0.02567345,  0.09778284, -0.08030444,\n",
+      "       -0.05697263, -0.02538129, -0.04187621, -0.03123644,  0.11242954,\n",
+      "       -0.00650479,  0.06117244,  0.06449873,  0.05163544,  0.01810673,\n",
+      "        0.03808153,  0.03746097,  0.1275719 ,  0.07381074, -0.14620851,\n",
+      "        0.05757713,  0.02838781, -0.12927914,  0.07510903,  0.08147711,\n",
+      "       -0.06523656, -0.01486957,  0.02304654,  0.04361376,  0.21421443,\n",
+      "       -0.02614699, -0.03873423,  0.21355043, -0.09329057, -0.09335907,\n",
+      "        0.08157952,  0.00625934, -0.05051626, -0.07981606, -0.03226125,\n",
+      "        0.08958097, -0.04725425, -0.06789905,  0.06730124,  0.06633109,\n",
+      "       -0.07370041, -0.13176298, -0.01786646,  0.11614659, -0.04729063,\n",
+      "        0.06670643,  0.12553535, -0.1768527 , -0.09982615, -0.07682986,\n",
+      "        0.06889272, -0.04036296,  0.03164692, -0.1446157 ,  0.03707608,\n",
+      "        0.13580611, -0.07748953, -0.02646814, -0.01086655, -0.09684405,\n",
+      "       -0.15440914, -0.1067182 ,  0.0349819 ,  0.10395218, -0.00877615,\n",
+      "        0.05640977, -0.01675119,  0.03014355,  0.12512377,  0.01414524,\n",
+      "        0.0211464 , -0.06591589,  0.08297148,  0.07012929, -0.10675234,\n",
+      "       -0.04061191,  0.01302199,  0.11621079,  0.00885058, -0.0304758 ,\n",
+      "       -0.01553952, -0.12865704,  0.17576885, -0.02950172, -0.01326492,\n",
+      "        0.01504841,  0.09476334, -0.03984889,  0.00763261, -0.01630022,\n",
+      "       -0.03672133,  0.01365909,  0.05986133, -0.10593513, -0.08282372,\n",
+      "       -0.08374579, -0.04833878,  0.0489895 ,  0.00875692, -0.03651298,\n",
+      "       -0.01757672, -0.01623743,  0.23217827,  0.15424854, -0.26954046,\n",
+      "       -0.04381514,  0.03540273,  0.06735364,  0.09081568,  0.07033931,\n",
+      "       -0.14287043, -0.1028204 , -0.04575491, -0.01768357, -0.08948888,\n",
+      "        0.07118583,  0.1542743 ,  0.09116074, -0.08839961,  0.01807798,\n",
+      "        0.12579061, -0.03509037,  0.08215491, -0.06514787,  0.00351805,\n",
+      "        0.04778312,  0.04185999,  0.08844823,  0.10574003, -0.01214823,\n",
+      "        0.04865406, -0.03167628,  0.02753052,  0.05206006, -0.15243334,\n",
+      "       -0.02005264, -0.00607761, -0.01364831,  0.10955886, -0.0010281 ,\n",
+      "        0.11600163, -0.04597978,  0.0601487 , -0.05470827,  0.11942582,\n",
+      "        0.00830158, -0.13237426, -0.15353964, -0.07234719,  0.04098862,\n",
+      "        0.15481706,  0.01778992,  0.14323051,  0.09922674,  0.12223933,\n",
+      "        0.0526598 ,  0.0408572 , -0.13533838,  0.02140845, -0.1030432 ,\n",
+      "       -0.17960373, -0.0495283 ,  0.14249511,  0.0465369 , -0.04416716,\n",
+      "        0.11465154, -0.2066736 , -0.02712584, -0.06553549,  0.0557267 ,\n",
+      "        0.04696037, -0.02916378,  0.07578196,  0.00608585,  0.00944843,\n",
+      "        0.02514744,  0.16452865,  0.0508883 , -0.15239422, -0.01861594,\n",
+      "       -0.1283559 ,  0.06792712,  0.0826408 , -0.05190438,  0.03456785,\n",
+      "       -0.00661102,  0.00788855,  0.02678101, -0.02560065, -0.00632013,\n",
+      "       -0.193216  , -0.1322564 , -0.03174976, -0.04030088,  0.01397508,\n",
+      "        0.07423516,  0.04445267,  0.0541687 ,  0.083343  , -0.15150657,\n",
+      "        0.00937343,  0.05487543,  0.02184621, -0.00872194, -0.00329627,\n",
+      "       -0.05453907,  0.05444683, -0.02034983, -0.0742873 , -0.01699653,\n",
+      "       -0.16100745,  0.10637001, -0.12436336, -0.03511102, -0.20044689,\n",
+      "       -0.02411474], dtype=float32), array([ 1.36041582e-01, -1.08781166e-01, -5.83929941e-02,  1.91058487e-01,\n",
+      "        3.24488580e-01, -6.02278523e-02, -4.59153317e-02, -2.24303032e-04,\n",
+      "       -8.76015052e-02,  8.96644667e-02,  1.04697935e-01,  1.83520690e-01,\n",
+      "       -7.28873909e-02, -3.05169255e-01,  4.17109728e-02,  1.82436183e-01,\n",
+      "        2.26057917e-01,  2.32112277e-02, -2.30914488e-01,  2.76603520e-01,\n",
+      "       -4.85374872e-03,  7.58373216e-02, -2.29087640e-02, -1.79299608e-01,\n",
+      "       -2.54585207e-01,  1.64706651e-02,  7.45610800e-03, -7.51212165e-02,\n",
+      "       -7.42579326e-02, -4.80351038e-02,  1.25985965e-01,  1.73480287e-01,\n",
+      "       -1.01882420e-01,  6.75791055e-02, -1.05399802e-01,  1.51736602e-01,\n",
+      "        1.63938925e-01, -3.84903289e-02,  1.10449508e-01, -9.40715224e-02,\n",
+      "       -6.31079897e-02, -3.84142622e-02, -4.99494635e-02, -4.23108302e-02,\n",
+      "        1.55812502e-01, -1.16190566e-02,  5.13239168e-02,  6.35165498e-02,\n",
+      "        6.25813156e-02,  2.21146382e-02,  6.36933595e-02,  5.06636649e-02,\n",
+      "        1.47942111e-01,  9.66929942e-02, -1.76337466e-01,  7.13716373e-02,\n",
+      "        2.70809997e-02, -1.57342017e-01,  9.11386758e-02,  9.41570252e-02,\n",
+      "       -9.42364857e-02, -5.13231056e-03,  1.36982789e-02,  5.77186793e-02,\n",
+      "        2.64365107e-01, -3.76182236e-02, -4.40398045e-02,  2.51175314e-01,\n",
+      "       -1.05481505e-01, -1.20290719e-01,  9.69798341e-02,  1.16603952e-02,\n",
+      "       -7.56024718e-02, -1.08631417e-01, -4.93911207e-02,  1.18807659e-01,\n",
+      "       -5.85292391e-02, -8.24716613e-02,  7.88261220e-02,  8.72761682e-02,\n",
+      "       -1.01744331e-01, -1.44680306e-01, -2.09336001e-02,  1.33469954e-01,\n",
+      "       -3.82428057e-02,  7.42774233e-02,  1.48893908e-01, -2.09778398e-01,\n",
+      "       -1.35315523e-01, -8.64634812e-02,  9.07569081e-02, -5.20299003e-02,\n",
+      "        5.39300404e-02, -1.71408862e-01,  3.55036259e-02,  1.70207739e-01,\n",
+      "       -8.76016989e-02, -2.80412864e-02, -2.70368508e-03, -1.22690618e-01,\n",
+      "       -1.63871482e-01, -1.22708760e-01,  3.35654393e-02,  1.26829579e-01,\n",
+      "       -1.43117364e-02,  7.96694681e-02, -3.10931951e-02,  2.37075817e-02,\n",
+      "        1.55911028e-01,  1.69575382e-02,  1.54271908e-02, -9.58202332e-02,\n",
+      "        1.09241918e-01,  8.82035494e-02, -1.39677778e-01, -5.01841754e-02,\n",
+      "        2.18386631e-02,  1.57107875e-01,  7.93855730e-03, -3.16538438e-02,\n",
+      "       -1.25909718e-02, -1.62555054e-01,  2.32526332e-01, -3.71498615e-02,\n",
+      "       -2.24065650e-02,  2.61516422e-02,  1.20939091e-01, -6.72491416e-02,\n",
+      "       -4.27399318e-05, -3.77265252e-02, -4.58711684e-02,  2.53423229e-02,\n",
+      "        8.39713439e-02, -1.19929694e-01, -1.07749395e-01, -1.10882133e-01,\n",
+      "       -4.97324318e-02,  6.16546497e-02, -1.15693109e-02, -3.91881578e-02,\n",
+      "       -2.29061693e-02, -2.30852738e-02,  3.04617375e-01,  2.08306596e-01,\n",
+      "       -3.39470148e-01, -5.02127111e-02,  5.37177026e-02,  9.11831036e-02,\n",
+      "        1.14661761e-01,  9.45202038e-02, -1.62573129e-01, -1.38315439e-01,\n",
+      "       -6.47830069e-02, -2.83303317e-02, -1.22525014e-01,  9.37087536e-02,\n",
+      "        1.96664244e-01,  1.07929356e-01, -1.03713363e-01,  1.60198733e-02,\n",
+      "        1.63076490e-01, -5.17086089e-02,  1.10869825e-01, -7.68428966e-02,\n",
+      "        8.37910455e-03,  5.56400865e-02,  6.31182268e-02,  1.21523716e-01,\n",
+      "        1.36712998e-01, -3.43602896e-02,  5.95005825e-02, -3.38348858e-02,\n",
+      "        3.81066576e-02,  6.04561493e-02, -1.77799106e-01, -3.31094116e-02,\n",
+      "       -1.12194773e-02, -3.21154818e-02,  1.08467303e-01, -4.27856669e-03,\n",
+      "        1.51000738e-01, -6.47066757e-02,  6.33488074e-02, -6.28948510e-02,\n",
+      "        1.29441455e-01,  4.04741522e-03, -1.72044575e-01, -1.91321328e-01,\n",
+      "       -8.56087804e-02,  4.62383777e-02,  2.01764986e-01,  3.43908928e-02,\n",
+      "        1.77972093e-01,  1.27216563e-01,  1.52360231e-01,  5.92516772e-02,\n",
+      "        4.44654636e-02, -1.71815068e-01,  5.64131141e-02, -1.30362645e-01,\n",
+      "       -2.15643197e-01, -6.48207739e-02,  1.80758730e-01,  6.18097410e-02,\n",
+      "       -6.83161020e-02,  1.69224367e-01, -2.53407449e-01, -4.33427393e-02,\n",
+      "       -8.90076980e-02,  7.33624548e-02,  5.79761080e-02, -3.76918130e-02,\n",
+      "        8.55930522e-02,  2.99188751e-03,  1.56438854e-02,  2.43693665e-02,\n",
+      "        2.17681393e-01,  6.79032505e-02, -1.83962315e-01, -1.40863275e-02,\n",
+      "       -1.53916925e-01,  8.76621082e-02,  9.75017548e-02, -6.90853447e-02,\n",
+      "        3.22550870e-02, -1.22331316e-02,  8.18267651e-03,  3.01490221e-02,\n",
+      "       -1.40115758e-02,  1.17691020e-02, -2.29610443e-01, -1.88834459e-01,\n",
+      "       -4.37003709e-02, -3.97654139e-02,  2.05663759e-02,  9.48096588e-02,\n",
+      "        4.75249775e-02,  6.99462742e-02,  9.24514905e-02, -1.96493998e-01,\n",
+      "        3.27189267e-02,  4.80476059e-02,  3.26564163e-02, -1.90033496e-03,\n",
+      "        3.73094343e-03, -5.62442988e-02,  5.95186092e-02, -2.72717867e-02,\n",
+      "       -8.34571719e-02, -2.22789161e-02, -1.91354036e-01,  1.27723783e-01,\n",
+      "       -1.69917479e-01, -3.11680529e-02, -2.62214512e-01, -2.82380339e-02],\n",
+      "      dtype=float32)] None\n",
+      "2 (256,)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# W2V"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "from EduNLP.I2V import W2V\r\n",
+    "\r\n",
+    "model_path = \"../../data/w2v/general_literal_300/general_literal_300.kv\"\r\n",
+    "i2v = W2V(\"pure_text\",\"w2v\",filepath=model_path, pretrained_t2v = False)\r\n",
+    "try:\r\n",
+    "    item_vectors, token_vectors = i2v(items)\r\n",
+    "    print(item_vectors) # 每个句子vector 为np.ndarray\r\n",
+    "    print(token_vectors) # 每个单词vector list\r\n",
+    "    print(len(item_vectors), item_vectors[0].shape,len(token_vectors), len(token_vectors[0]),len(token_vectors[0][0]))\r\n",
+    "except Exception as e:\r\n",
+    "    print(e)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[array([-1.34266680e-03,  5.19845746e-02, -1.98070258e-02, -4.17470075e-02,\n",
+      "        4.92814295e-02, -1.70883536e-01, -2.16597781e-01, -3.12069029e-01,\n",
+      "        8.96430463e-02, -1.31331667e-01,  9.16494895e-03, -3.22572999e-02,\n",
+      "        3.07940125e-01,  1.92060292e-01,  1.31043345e-02,  6.10962026e-02,\n",
+      "        2.21019030e-01, -3.53541046e-01,  1.34150490e-01,  1.14867561e-01,\n",
+      "        1.17448963e-01,  2.27990672e-01, -1.65213019e-01,  2.78246611e-01,\n",
+      "       -4.36594114e-02, -1.37816787e-01, -1.07707813e-01, -1.80805102e-01,\n",
+      "        1.20028563e-01, -1.14409983e-01,  6.19181581e-02, -1.79836392e-01,\n",
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+      "2 (300,) 2 55 300\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
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+  "language_info": {
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+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/tests/test_vec/test_vec.py b/tests/test_vec/test_vec.py
index d97210d8..bc7194d9 100644
--- a/tests/test_vec/test_vec.py
+++ b/tests/test_vec/test_vec.py
@@ -110,7 +110,7 @@ def test_w2v_i2v(stem_text_tokens, tmpdir, stem_data):
     )
 
     i2v = I_W2V("pure_text", "w2v", filepath)
-    i_vec, t_vec = i2v(stem_data[:1])
+    i_vec, t_vec = i2v(stem_data[:2])
     assert len(i_vec[0]) == i2v.vector_size
     assert len(t_vec[0][0]) == i2v.vector_size
 
@@ -187,7 +187,7 @@ def test_d2v(stem_text_tokens, tmpdir, stem_data):
     assert len(t2v(stem_text_tokens[:1])[0]) == t2v.vector_size
 
     i2v = I_D2V("pure_text", "d2v", filepath)
-    i_vec, t_vec = i2v(stem_data[:1])
+    i_vec, t_vec = i2v(stem_data[:2])
     assert len(i_vec[0]) == i2v.vector_size
     assert t_vec is None
 

From 654df05f9df1b543684a8e23263af01772c0b945 Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Tue, 31 Aug 2021 14:08:53 +0800
Subject: [PATCH 018/149] [FEATURE] modify is_sif,to_sif,sif4sci

---
 EduNLP/SIF/parser/parser.py |  5 +++--
 EduNLP/SIF/sif.py           | 35 +++++++++++++++++++----------------
 tests/test_sif/test_sif.py  |  2 +-
 3 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index db290946..bab1b3b9 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -2,7 +2,7 @@
 
 
 class Parser:
-    def __init__(self, data):
+    def __init__(self, data, check_formula=True):
         self.lookahead = 0
         self.head = 0
         self.text = data
@@ -13,6 +13,7 @@ def __init__(self, data):
         self.warnning = 0
         self.fomula_illegal_flag = 0
         self.fomula_illegal_message = ''
+        self.check_formula = check_formula
 
         # 定义特殊变量
         self.len_bracket = len('$\\SIFChoice$')
@@ -231,7 +232,7 @@ def get_token(self):
                 self.call_error()
                 return self.error
             # 检查latex公式的完整性和可解析性
-            if not self._is_formula_legal(self.text[formula_start:self.head]):
+            if self.check_formula and not self._is_formula_legal(self.text[formula_start:self.head]):
                 self.call_error()
                 return self.error
             self.head += 1
diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index af4fa63a..f075710f 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -10,39 +10,43 @@
 __all__ = ["is_sif", "to_sif", "sif4sci"]
 
 
-def is_sif(item):
+def is_sif(item, check_formula=True):
     r"""
     Parameters
     ----------
     item
+    check_formula: bool
+        True if check the validity of formulas in items
+        False if not check the validity of formulas in items, which is faster
 
     Returns
     -------
     when item can not be parsed correctly, raise Error;
-    when item doesn't need to be modified, return Ture;
-    when item needs to be modified, return False;
+    when item doesn't need to be modified, return Ture and original item ;
+    when item needs to be modified, return False and modified item;
 
     Examples
     --------
     >>> text = '若$x,y$满足约束条件' \
     ...        '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' \
     ...        '则$z=x+7 y$的最大值$\\SIFUnderline$'
-    >>> is_sif(text)
+    >>> flag, _ = is_sif(text)
+    >>> print(flag)
     True
     >>> text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
     >>> is_sif(text)
-    False
+    (False, '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...')
     """
-    item_parser = Parser(item)
+    item_parser = Parser(item, check_formula)
     item_parser.description_list()
     if item_parser.fomula_illegal_flag:
         raise ValueError(item_parser.fomula_illegal_message)
     if item_parser.error_flag == 0 and item_parser.modify_flag == 0:
-        return True
-    return False
+        return True, item
+    return False, item_parser.text
 
 
-def to_sif(item):
+def to_sif(item, check_formula=True):
     r"""
     Parameters
     ----------
@@ -59,14 +63,12 @@ def to_sif(item):
     >>> siftext
     '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'
     """
-    item_parser = Parser(item)
-    item_parser.description_list()
-    item = item_parser.text
-    return item
+    _, sif_item = is_sif(item, check_formula)
+    return sif_item
 
 
 def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = None, tokenization=True,
-            tokenization_params=None, errors="raise"):
+            tokenization_params=None, errors="raise", check_formula=True):
     r"""
 
     Default to use linear Tokenizer, change the tokenizer by specifying tokenization_params
@@ -189,8 +191,9 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = No
     [['已知'], ['说法', '中', '正确']]
     """
     try:
-        if safe is True and is_sif(item) is not True:
-            item = to_sif(item)
+        if safe is True:
+            flag, sif_item = is_sif(item)
+            item = sif_item if flag is not True else item
 
         ret = seg(item, figures, symbol)
 
diff --git a/tests/test_sif/test_sif.py b/tests/test_sif/test_sif.py
index 210441d2..0f1febce 100644
--- a/tests/test_sif/test_sif.py
+++ b/tests/test_sif/test_sif.py
@@ -11,7 +11,7 @@ def test_is_sif():
     text = '若$x,y$满足约束条件' \
            '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' \
            '则$z=x+7 y$的最大值$\\SIFUnderline$'
-    assert is_sif(text) == 1
+    assert is_sif(text)[0] == 1
 
     text = '公式需要满足完整性，完整的公式如' \
            '$\\begin{matrix} a & b \\\\ c & d \\end{matrix}$' \

From 0a06e06ff13c41c545829563ed798e10a102e51c Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Tue, 31 Aug 2021 14:18:34 +0800
Subject: [PATCH 019/149] [FEATURE]add params in sif to decided whether to
 check formulas

---
 EduNLP/SIF/sif.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index f075710f..dd5c40a8 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -192,7 +192,7 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = No
     """
     try:
         if safe is True:
-            flag, sif_item = is_sif(item)
+            flag, sif_item = is_sif(item, check_formula)
             item = sif_item if flag is not True else item
 
         ret = seg(item, figures, symbol)

From ee85070e48139e3bf645c715f2165a9fe4f03c9e Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 11:17:59 +0800
Subject: [PATCH 020/149] [DOC]Update documents

---
 EduNLP/Formula/Formula.py                     | 45 +++++++++
 EduNLP/Formula/ast/ast.py                     |  2 +
 EduNLP/I2V/i2v.py                             | 91 ++++++++++++++++++-
 EduNLP/ModelZoo/rnn/rnn.py                    | 29 ++++++
 EduNLP/ModelZoo/utils/masker.py               | 13 +++
 EduNLP/ModelZoo/utils/padder.py               |  1 +
 EduNLP/Pretrain/gensim_vec.py                 | 61 +++++++++++++
 EduNLP/SIF/parser/parser.py                   | 12 +++
 EduNLP/SIF/segment/segment.py                 | 33 +++++++
 .../SIF/tokenization/formula/linear_token.py  | 31 +++++++
 EduNLP/SIF/tokenization/tokenization.py       | 76 ++++++++++++++++
 EduNLP/Tokenizer/tokenizer.py                 | 29 ++++++
 EduNLP/Vector/t2v.py                          |  2 +
 13 files changed, 424 insertions(+), 1 deletion(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index f4c868be..f7d7a542 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -29,6 +29,10 @@ class Formula(object):
     <Formula: x>
     >>> f.elements
     [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
+
+    Returns
+    --------
+    the parsed abstracted syntax tree
     """
 
     def __init__(self, formula: (str, List[Dict]), variable_standardization=False, const_mathord=None,
@@ -55,6 +59,15 @@ def __init__(self, formula: (str, List[Dict]), variable_standardization=False, c
             )
 
     def variable_standardization(self, inplace=False, const_mathord=None, variable_connect_dict=None):
+        """
+        It makes same parmeters have the same number.
+
+        Parameters
+        ----------
+        inplace
+        const_mathord
+        variable_connect_dict
+        """
         const_mathord = const_mathord if const_mathord is not None else CONST_MATHORD
         ast_tree = self._ast if inplace else deepcopy(self._ast)
         var_code = variable_connect_dict["var_code"] if variable_connect_dict is not None else {}
@@ -135,6 +148,10 @@ class FormulaGroup(object):
     [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, \
 {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, \
 {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
+
+    Returns
+    --------
+    the parsed abstracted syntax forest
     """
 
     def __init__(self,
@@ -143,6 +160,17 @@ def __init__(self,
                  const_mathord=None,
                  detach=True
                  ):
+        """
+
+        Parameters
+        ----------
+        formula: str or List[Dict] or List[Formula]
+            latex formula string or the parsed abstracted syntax tree or a group of parsed abstracted syntax tree
+        variable_standardization
+        const_mathord
+        detach
+
+        """
         forest = []
         self._formulas = []
         for formula in formula_list:
@@ -186,6 +214,15 @@ def __contains__(self, item) -> bool:
         return item in self._formulas
 
     def variable_standardization(self, inplace=False, const_mathord=None, variable_connect_dict=None):
+        """
+        It makes same parmeters have the same number.
+
+        Parameters
+        ----------
+        inplace
+        const_mathord
+        variable_connect_dict
+        """
         ret = []
         for formula in self._formulas:
             ret.append(formula.variable_standardization(inplace=inplace, const_mathord=const_mathord,
@@ -220,6 +257,14 @@ def ast_graph(self) -> (nx.Graph, nx.DiGraph):
 
 
 def link_formulas(*formula: Formula, link_vars=True, **kwargs):
+    """
+
+    Parameters
+    ----------
+    formula:the parsed abstracted syntax tree
+    link_vars
+    kwargs
+    """
     forest = []
     for form in formula:
         forest += form.reset_ast(
diff --git a/EduNLP/Formula/ast/ast.py b/EduNLP/Formula/ast/ast.py
index 52e44ac5..8b3af216 100644
--- a/EduNLP/Formula/ast/ast.py
+++ b/EduNLP/Formula/ast/ast.py
@@ -8,10 +8,12 @@
 
 
 def katex_parse(formula):
+    """将公式传入katex进行语法解析"""
     return katex.katex.__parse(formula,{'displayMode':True,'trust': True}).to_list()
 
 
 def str2ast(formula: str, *args, **kwargs):
+    """给字符串的接口"""
     return ast(formula, is_str=True, *args, **kwargs)
 
 
diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 4254fe1e..4e57f079 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -12,6 +12,8 @@
 
 class I2V(object):
     """
+    It just a api, so you shouldn't use it directly. \
+    If you want to get vector from item, you can use other model like D2V and W2V.
 
     Parameters
     ----------
@@ -26,10 +28,40 @@ class I2V(object):
     pretrained_t2v: bool
     kwargs:
         the parameters passed to t2v
+
+    Examples
+    --------
+    >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
+    ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
+    ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
+    >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
+    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
+    >>> i2v(item) # doctest: +ELLIPSIS
+    ([array([ ...dtype=float32)], None)
+
+    Returns
+    -------
+    i2v model: I2V
     """
 
     def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrained_t2v=False, **kwargs):
-
+        """
+
+        Parameters
+        ----------
+        tokenizer: str
+            the tokenizer name
+        t2v: str
+            the name of token2vector model
+        args:
+            the parameters passed to t2v
+        tokenizer_kwargs: dict
+            the parameters passed to tokenizer
+        pretrained_t2v: bool
+        kwargs:
+            the parameters passed to t2v
+
+        """
         self.tokenizer: Tokenizer = get_tokenizer(tokenizer, **tokenizer_kwargs if tokenizer_kwargs is not None else {})
         if pretrained_t2v:
             logger.info("Use pretrained t2v model %s" % t2v)
@@ -46,9 +78,11 @@ def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrai
         }
 
     def __call__(self, items, *args, **kwargs):
+        """transfer item to vector"""
         return self.infer_vector(items, *args, **kwargs)
 
     def tokenize(self, items, indexing=True, padding=False, key=lambda x: x, *args, **kwargs) -> list:
+        """tokenize item"""
         return self.tokenizer(items, key=key, *args, **kwargs)
 
     def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=lambda x: x, *args,
@@ -86,8 +120,39 @@ def vector_size(self):
 
 
 class D2V(I2V):
+    """
+    Examples
+    --------
+    >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
+    ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
+    ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
+    >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
+    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
+    >>> i2v(item) # doctest: +ELLIPSIS
+    ([array([ ...dtype=float32)], None)
+
+    Returns
+    -------
+    i2v model: I2V
+    """
     def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=lambda x: x, *args,
                      **kwargs) -> tuple:
+        '''
+
+        Parameters
+        ----------
+        items:str
+        tokenize
+        indexing
+        padding
+        key
+        args
+        kwargs
+
+        Returns
+        -------
+        vector
+        '''
         tokens = self.tokenize(items, return_token=True, key=key) if tokenize is True else items
         tokens = [token for token in tokens]
         return self.t2v(tokens, *args, **kwargs), None
@@ -98,6 +163,20 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
 
 
 class W2V(I2V):
+    """
+
+    Examples
+    --------
+    >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v")
+    >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
+    >>> item_vector # doctest: +ELLIPSIS
+    array([[...]], dtype=float32)
+
+    Returns
+    -------
+    i2v model: W2V
+
+    """
     def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=lambda x: x, *args,
                      **kwargs) -> tuple:
         tokens = self.tokenize(items, return_token=True) if tokenize is True else items
@@ -116,6 +195,8 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
     "d2v_lit_256": [D2V, "d2v_lit_256"],
     "w2v_sci_300": [W2V, "w2v_sci_300"],
     "w2v_lit_300": [W2V, "w2v_lit_300"],
+    "test_w2v": [W2V, "test_w2v"],
+    "test_d2v": [D2V, "test_d2v"],
 }
 
 
@@ -131,6 +212,14 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR):
     -------
     i2v model: I2V
 
+    Examples
+    --------
+    >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
+    ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
+    ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
+    >>> i2v = get_pretrained_i2v("test_d2v") # doctest: +ELLIPSIS
+    >>> print(i2v(item)) # doctest: +ELLIPSIS
+    ([array([ ...dtype=float32)], None)
     """
     if name not in MODELS:
         raise KeyError(
diff --git a/EduNLP/ModelZoo/rnn/rnn.py b/EduNLP/ModelZoo/rnn/rnn.py
index c1594ac8..c3359fa9 100644
--- a/EduNLP/ModelZoo/rnn/rnn.py
+++ b/EduNLP/ModelZoo/rnn/rnn.py
@@ -30,6 +30,22 @@ class LM(nn.Module):
 
     def __init__(self, rnn_type: str, vocab_size: int, embedding_dim: int, hidden_size: int, num_layers=1,
                  bidirectional=False, embedding=None, model_params=None, **kwargs):
+        """
+
+        Parameters
+        ----------
+        rnn_type：str
+            Legal types including RNN, LSTM, GRU,ELMO
+        vocab_size: int
+        embedding_dim: int
+        hidden_size: int
+        num_layers
+        bidirectional
+        embedding
+        model_params
+        kwargs
+
+        """
         super(LM, self).__init__()
         rnn_type = rnn_type.upper()
         self.embedding = torch.nn.Embedding(vocab_size, embedding_dim) if embedding is None else embedding
@@ -66,6 +82,19 @@ def __init__(self, rnn_type: str, vocab_size: int, embedding_dim: int, hidden_si
             load_net(model_params, self, allow_missing=True)
 
     def forward(self, seq_idx, seq_len):
+        """
+
+        Parameters
+        ----------
+        seq_idx:Tensor
+            a list of indices
+        seq_len:Tensor
+            length
+
+        Returns
+        --------
+        a PackedSequence object
+        """
         seq = self.embedding(seq_idx)
         pack = pack_padded_sequence(seq, seq_len, batch_first=True)
         h0 = torch.zeros(self.num_layers, seq.shape[0], self.hidden_size)
diff --git a/EduNLP/ModelZoo/utils/masker.py b/EduNLP/ModelZoo/utils/masker.py
index 401c16e7..4e56d738 100644
--- a/EduNLP/ModelZoo/utils/masker.py
+++ b/EduNLP/ModelZoo/utils/masker.py
@@ -45,6 +45,19 @@ def __init__(self, mask: (int, str, ...) = 0, per=0.2, seed=None):
         self.mask = mask
 
     def __call__(self, seqs, length=None, *args, **kwargs) -> tuple:
+        """
+
+        Parameters
+        ----------
+        seqs:list
+        length
+        args
+        kwargs
+
+        Returns
+        ----------
+        list:list of masked_seq and list of masked_list
+        """
         seqs = deepcopy(seqs)
         masked_list = []
         if length is None:
diff --git a/EduNLP/ModelZoo/utils/padder.py b/EduNLP/ModelZoo/utils/padder.py
index ed86cfef..52a662ee 100644
--- a/EduNLP/ModelZoo/utils/padder.py
+++ b/EduNLP/ModelZoo/utils/padder.py
@@ -59,6 +59,7 @@ def pad_sequence(sequence: list, max_length=None, pad_val=0, clip=True):
 
     Returns
     -------
+    Modified list:padding the sequence in the same size.
 
     Examples
     --------
diff --git a/EduNLP/Pretrain/gensim_vec.py b/EduNLP/Pretrain/gensim_vec.py
index 17482b23..8acd41fe 100644
--- a/EduNLP/Pretrain/gensim_vec.py
+++ b/EduNLP/Pretrain/gensim_vec.py
@@ -30,6 +30,7 @@ class GensimWordTokenizer(object):
 
     Returns
     ----------
+    tokenizer: Tokenizer
 
     Examples
     ----------
@@ -79,6 +80,28 @@ class GensimSegTokenizer(object):  # pragma: no cover
     symbol:
         gms
         fgm
+    depth: int or None
+        0: only separate at \\SIFSep
+        1: only separate at \\SIFTag
+        2: separate at \\SIFTag and \\SIFSep
+        otherwise, separate all segments
+
+    Returns
+    ----------
+    tokenizer: Tokenizer
+
+    Examples
+    ----------
+    >>> tokenizer = GensimSegTokenizer(symbol="gms", depth=None)
+    >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+    >>> print(token_item[:10]) # doctest: +ELLIPSIS
+    [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
+    >>> tokenizer = GensimSegTokenizer(symbol="fgm", depth=None)
+    >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+    >>> print(token_item[:10])
+    [['公式'], ['[FORMULA]'], ['如图'], ['[FIGURE]'], ['[FORMULA]'],...['[FORMULA]'], ['最大值'], ['[MARK]']]
     """
     def __init__(self, symbol="gms", depth=None, flatten=False, **kwargs):
         self.symbol = symbol
@@ -117,6 +140,7 @@ def __call__(self, item, flatten=None, **kwargs):
 
 
 class MonitorCallback(CallbackAny2Vec):
+    """record the loss in each epoch"""
     def __init__(self, test_words):
         self.epoch = 0
         self._test_words = test_words
@@ -127,6 +151,43 @@ def on_epoch_end(self, model):
 
 
 def train_vector(items, w2v_prefix, embedding_dim=None, method="sg", binary=None, train_params=None):
+    """
+
+    Parameters
+    ----------
+    items：str
+    w2v_prefix
+    embedding_dim:int
+        vector_size
+    method:str
+        sg
+        cbow
+        fasttext
+        d2v
+        bow
+        tfidf
+    binary:model format
+        True:bin
+        False:kv
+    train_params
+
+    Returns
+    ----------
+    tokenizer: Tokenizer
+
+    Examples
+    ----------
+    >>> tokenizer = GensimSegTokenizer(symbol="gms", depth=None)
+    >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+    >>> print(token_item[:10]) # doctest: +ELLIPSIS
+    [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
+    >>> tokenizer = GensimSegTokenizer(symbol="fgm", depth=None)
+    >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+    >>> print(token_item[:10]) # doctest: +ELLIPSIS
+    [['公式'], ['[FORMULA]'], ['如图'], ['[FIGURE]'], ['[FORMULA]'],...['最大值'], ['[MARK]']]
+    """
     monitor = MonitorCallback(["word", "I", "less"])
     _train_params = dict(
         min_count=0,
diff --git a/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index db290946..553bd6dc 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -3,6 +3,7 @@
 
 class Parser:
     def __init__(self, data):
+        """initial data and special variable"""
         self.lookahead = 0
         self.head = 0
         self.text = data
@@ -98,6 +99,17 @@ def call_error(self):
         self.error_flag = 1
 
     def get_token(self):
+        r"""
+        Get different elements in the item.
+
+        Parameters
+        ----------
+
+        Returns
+        -------
+        elements:chinese,alphabet,number,ch_pun_list,en_pun_list,latex formula
+
+        """
         if self.head >= len(self.text):
             return self.empty
         ch = self.text[self.head]
diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index 93c5713f..656f5f69 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -16,16 +16,19 @@ class LatexFormulaSegment(str):
 
 
 class Figure(object):
+    """decode figure which has been encode by base64"""
     def __init__(self, is_base64=False):
         self.base64 = is_base64
         self.figure = None
 
     @classmethod
     def base64_to_numpy(cls, figure: str):
+        """Creat a arrary in a designated buffer"""
         return np.frombuffer(base64.b64decode(figure), dtype=np.uint8)
 
 
 class FigureFormulaSegment(Figure):
+    """Duel with figureformula, especially coding in base64"""
     def __init__(self, src, is_base64=False, figure_instance: (dict, bool) = None):
         super(FigureFormulaSegment, self).__init__(is_base64)
         self.src = src
@@ -45,6 +48,7 @@ def __repr__(self):
 
 
 class FigureSegment(Figure):
+    """Duel with figure, especially coding in base64"""
     def __init__(self, src, is_base64=False, figure_instance: (dict, bool) = None):
         super(FigureSegment, self).__init__(is_base64)
         self.src = src
@@ -77,6 +81,23 @@ class SepSegment(str):
 
 class SegmentList(object):
     def __init__(self, item, figures: dict = None):
+        """
+
+        Parameters
+        ----------
+        item
+        figures:dict
+
+        Returns
+        ----------
+        list:tokenizated item
+
+        Examples
+        --------
+        >>> test_item = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
+        >>> SegmentList(test_item)
+        ['如图所示，则三角形', 'ABC', '的面积是', '\\\\SIFBlank', '。', \\FigureID{1}]
+        """
         self._segments = []
         self._text_segments = []
         self._formula_segments = []
@@ -119,6 +140,7 @@ def __len__(self):
         return len(self._segments)
 
     def append(self, segment) -> None:
+        """add segment to corresponding segments"""
         if isinstance(segment, TextSegment):
             self._text_segments.append(len(self))
         elif isinstance(segment, (LatexFormulaSegment, FigureFormulaSegment)):
@@ -201,6 +223,15 @@ def symbolize(self, to_symbolize="fgm"):
 
     @contextmanager
     def filter(self, drop: (set, str) = "", keep: (set, str) = "*"):
+        """
+
+        Parameters
+        ----------
+        drop: set or str
+            The alphabet should be included in "tfgmas", which means drop selected segments out of return value.
+        keep: set or str
+            The alphabet should be included in "tfgmas", which means only keep selected segments in return value.
+        """
         _drop = {c for c in drop} if isinstance(drop, str) else drop
         if keep == "*":
             _keep = {c for c in "tfgmas" if c not in _drop}
@@ -223,6 +254,7 @@ def filter(self, drop: (set, str) = "", keep: (set, str) = "*"):
         self._seg_idx = None
 
     def describe(self):
+        """show the length of different segments"""
         return {
             "t": len(self._text_segments),
             "f": len(self._formula_segments),
@@ -242,6 +274,7 @@ def seg(item, figures=None, symbol=None):
 
     Returns
     -------
+    list:segmented item
 
     Examples
     --------
diff --git a/EduNLP/SIF/tokenization/formula/linear_token.py b/EduNLP/SIF/tokenization/formula/linear_token.py
index 7b5d1212..6775da9f 100644
--- a/EduNLP/SIF/tokenization/formula/linear_token.py
+++ b/EduNLP/SIF/tokenization/formula/linear_token.py
@@ -6,6 +6,35 @@
 
 def cut(formula, preserve_braces=True, with_dollar=False,
         preserve_dollar=False, number_as_tag=False, preserve_src=True):  # pragma: no cover
+    """
+
+    Parameters
+    ----------
+    formula:str
+    preserve_braces:
+        when it is False "{" and "}" will be filted
+    with_dollar:
+        have dollar or not
+    preserve_dollar:
+        keep "$"
+    number_as_tag:
+        whether switch number to tag, it just can idenify the number which is more than one bit.
+    preserve_src
+
+    Returns
+    --------
+    list
+
+    Examples
+    ----------
+    >>> cut(r"${x + y}^\\frac{1}{2} + 12.1 = 0$")
+    ['{x + y}', '^', '\\\\f', 'r', 'a', 'c', '{1}', '{2}', '+', '12.1', '=', '0']
+    >>> cut(r"${x + y}^\\frac{1}{2} + 12.1 = 0$",preserve_dollar=False)
+    ['{x + y}', '^', '\\\\f', 'r', 'a', 'c', '{1}', '{2}', '+', '12.1', '=', '0']
+    >>> cut(r"${x + y}^\\frac{1}{2} + 12.1 = 0$",number_as_tag=True)
+    ['{x + y}', '^', '\\\\f', 'r', 'a', 'c', '{1}', '{2}', '+', '{decimal}', '=', '0']
+
+    """
     class States(IntFlag):
         CHAR = 0
         MATH = 1
@@ -135,6 +164,7 @@ class States(IntFlag):
 
 
 def reduce(fea):  # pragma: no cover
+    """restore some formula"""
     rules = [
         ('a r c s i n', 'arcsin'),
         ('a r c c o s', 'arccos'),
@@ -165,6 +195,7 @@ def reduce(fea):  # pragma: no cover
 
 
 def connect_char(words):  # pragma: no cover
+    """connect and switch to list type"""
     result = []
     buffer = ""
     for w in words:
diff --git a/EduNLP/SIF/tokenization/tokenization.py b/EduNLP/SIF/tokenization/tokenization.py
index 299eaf62..49f31416 100644
--- a/EduNLP/SIF/tokenization/tokenization.py
+++ b/EduNLP/SIF/tokenization/tokenization.py
@@ -21,6 +21,17 @@ class TokenList(object):
 
     """
     def __init__(self, segment_list: SegmentList, text_params=None, formula_params=None, figure_params=None):
+        """
+
+        Parameters
+        ----------
+        segment_list:list
+            segmented item
+        text_params:dict
+        formula_params:dict
+        figure_params:dict
+
+        """
         self._tokens = []
         self._text_tokens = []
         self._formula_tokens = []
@@ -60,6 +71,22 @@ def _variable_standardization(self):
 
     @contextmanager
     def add_seg_type(self, seg_type, tar: list, add_seg_type=True, mode="delimiter"):
+        """
+        add seg tag in different position
+
+        Parameters
+        ----------
+        seg_type:str
+            t: text
+            f:formula
+        tar:list
+        add_seg_type
+            if the value==False, the function will not be executed.
+        mode:str
+            delimiter: both in the head and at the tail
+            head: only in the head
+            tail: only at the tail
+        """
         if add_seg_type is True and mode in {"delimiter", "head"}:
             if seg_type == "t":
                 tar.append(TEXT_BEGIN)
@@ -226,6 +253,15 @@ def _append(self, seg_type):
         self._segments.append((start, end, seg_type))
 
     def append(self, segment, lazy=False):
+        """
+
+        Parameters
+        ----------
+        segment
+        lazy
+            True:Doesn't distinguish parmeters.
+            False:It makes same parmeters have the same number.
+        """
         if isinstance(segment, TextSegment):
             self.append_text(segment)
         elif isinstance(segment, (LatexFormulaSegment, FigureFormulaSegment)):
@@ -268,6 +304,7 @@ def text_tokens(self):
         return [self._tokens[i] for i in self._text_tokens]
 
     def __add_token(self, token, tokens):
+        """classify token to tokens"""
         if isinstance(token, Formula):
             if self.formula_params.get("return_type") == "list":
                 tokens.extend(formula.traversal_formula(token.ast_graph, **self.formula_params))
@@ -310,6 +347,19 @@ def inner_formula_tokens(self):
 
     @contextmanager
     def filter(self, drop: (set, str) = "", keep: (set, str) = "*"):
+        """
+
+        Parameters
+        ----------
+        drop: set or str
+            The alphabet should be included in "tfgmas", which means drop selected segments out of return value.
+        keep: set or str
+            The alphabet should be included in "tfgmas", which means only keep selected segments in return value.
+
+        Returns
+        --------
+        filted list
+        """
         _drop = {c for c in drop} if isinstance(drop, str) else drop
         if keep == "*":
             _keep = {c for c in "tfgmas" if c not in _drop}
@@ -341,6 +391,32 @@ def describe(self):
 
 
 def tokenize(segment_list: SegmentList, text_params=None, formula_params=None, figure_params=None):
+    """
+    an actual api to tokenize item
+
+    Parameters
+    ----------
+    segment_list:list
+        segmented item
+    text_params:dict
+        the method to duel with text
+    formula_params:dict
+        the method to duel with formula
+    figure_params:dict
+        the method to duel with figure
+
+    Returns
+    ----------
+    tokenized item
+
+    Examples
+    --------
+    >>> items = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
+    >>> tokenize(SegmentList(items))
+    ['如图所示', '三角形', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]
+    >>> tokenize(SegmentList(items),formula_params={"method": "ast"})
+    ['如图所示', '三角形', <Formula: ABC>, '面积', '\\\\SIFBlank', \\FigureID{1}]
+    """
     return TokenList(segment_list, text_params, formula_params, figure_params)
 
 
diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py
index bb7b47e5..646fc083 100644
--- a/EduNLP/Tokenizer/tokenizer.py
+++ b/EduNLP/Tokenizer/tokenizer.py
@@ -50,6 +50,21 @@ def __init__(self, *args, **kwargs):
         }
 
     def __call__(self, items: Iterable, key=lambda x: x, *args, **kwargs):
+        """
+        Duel with text and plain text formula.
+        And filting special formula like $\\FormFigureID{…}$ and $\\FormFigureBase64{…}.
+
+        Parameters
+        ----------
+        items: str
+        key
+        args
+        kwargs
+
+        Returns
+        -------
+        token
+        """
         for item in items:
             yield tokenize(seg(key(item), symbol="gmas"), **self.tokenization_params).tokens
 
@@ -76,6 +91,20 @@ def __init__(self, *args, **kwargs):
         }
 
     def __call__(self, items: Iterable, key=lambda x: x, *args, **kwargs):
+        """
+        Duel with text and formula including special formula.
+
+        Parameters
+        ----------
+        items: str
+        key
+        args
+        kwargs
+
+        Returns
+        -------
+        token
+        """
         for item in items:
             yield tokenize(seg(key(item), symbol="gmas"), **self.tokenization_params).tokens
 
diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index ec0887ef..95977e4c 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -49,6 +49,8 @@ def vector_size(self) -> int:
     "d2v_lit_256": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_literal_256.zip", "d2v"],
     "w2v_eng_300": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/w2v/general_english_300.zip", "w2v"],
     "w2v_lit_300": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/w2v/general_literal_300.zip", "w2v"],
+    "test_w2v": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/w2v/test_w2v_256.zip", "w2v"],
+    "test_d2v": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/test_256.zip", "d2v"]
 }
 
 

From cceb3cc8bd67ecf1e4cca5a615483e853b6db8df Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 11:36:21 +0800
Subject: [PATCH 021/149] update document

---
 docs/source/api/i2v.rst | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/docs/source/api/i2v.rst b/docs/source/api/i2v.rst
index d8306edb..8dff365f 100644
--- a/docs/source/api/i2v.rst
+++ b/docs/source/api/i2v.rst
@@ -1,6 +1,24 @@
 EduNLP.I2V
 ============
 
-.. automodule:: EduNLP.I2V.i2v
+.. automodule:: EduNLP.I2V.i2v.I2V
+   :members:
+   :imported-members:
+
+EduNLP.I2V.D2V
+-------------------
+.. automodule:: EduNLP.I2V.i2v.D2V
+   :members:
+   :imported-members:
+
+EduNLP.I2V.W2V
+-------------------
+.. automodule:: EduNLP.I2V.i2v.W2V
+   :members:
+   :imported-members:
+
+EduNLP.I2V.get_pretrained_i2v
+-------------------
+.. automodule:: EduNLP.I2V.i2v.get_pretrained_i2v
    :members:
    :imported-members:

From 8040c2621309794f8b3b8c45f5695ed51af38318 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 11:47:44 +0800
Subject: [PATCH 022/149] [DOC]Update documents

---
 docs/source/api/sif.rst | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/docs/source/api/sif.rst b/docs/source/api/sif.rst
index a49f7f15..95f355fb 100644
--- a/docs/source/api/sif.rst
+++ b/docs/source/api/sif.rst
@@ -10,14 +10,14 @@ SIF
 
 Segment
 ----------
-.. automodule:: EduNLP.SIF.segment
+.. automodule:: EduNLP.SIF.segment.segment
    :members:
    :imported-members:
 
 
 Parser
 --------
-.. automodule:: EduNLP.SIF.parser
+.. automodule:: EduNLP.SIF.parser.parser
    :members:
    :imported-members:
 
@@ -40,6 +40,14 @@ text
 
 formula
 ^^^^^^^^^
-.. automodule:: EduNLP.SIF.tokenization.formula
+.. automodule:: EduNLP.SIF.tokenization.formula.formula
    :members:
    :imported-members:
+
+.. automodule:: EduNLP.SIF.tokenization.formula.ast_token
+   :members:
+   :imported-members:
+
+.. automodule:: EduNLP.SIF.tokenization.formula.linear_token
+   :members:
+   :imported-members:
\ No newline at end of file

From 4344f5e61ba91aff97dd1aa3262deb0d327e9bf1 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 12:02:14 +0800
Subject: [PATCH 023/149] [DOC]update

---
 docs/source/api/sif.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/api/sif.rst b/docs/source/api/sif.rst
index 95f355fb..4931807c 100644
--- a/docs/source/api/sif.rst
+++ b/docs/source/api/sif.rst
@@ -17,7 +17,7 @@ Segment
 
 Parser
 --------
-.. automodule:: EduNLP.SIF.parser.parser
+.. automodule:: EduNLP.SIF.parser.parser.Parser
    :members:
    :imported-members:
 

From 63c4fbc24c2d3333988649d561d209515dfd9dde Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 13:52:15 +0800
Subject: [PATCH 024/149] [DOC]Update documents

---
 EduNLP/Vector/t2v.py        | 35 +++++++++++++++++++++++++++++++++++
 docs/source/api/formula.rst |  4 ++++
 docs/source/api/vector.rst  | 26 +++++++++++++++++++++++---
 3 files changed, 62 insertions(+), 3 deletions(-)

diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index 95977e4c..6e7f0556 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -20,6 +20,16 @@
 
 
 class T2V(object):
+    """
+    Examples
+    --------
+    >>> item = [{'ques_content':'有公式$\\FormFigureID{wrong1?}$和公式$\\FormFigureBase64{wrong2?}$，\
+    ... 如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'}]
+    >>> path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+    >>> t2v = T2V('d2v',filepath=path)
+    >>> print(t2v(item)) # doctest: +ELLIPSIS
+    [array([...dtype=float32)]
+    """
     def __init__(self, model: str, *args, **kwargs):
         model = model.lower()
         self.model_type = model
@@ -55,6 +65,31 @@ def vector_size(self) -> int:
 
 
 def get_pretrained_t2v(name, model_dir=MODEL_DIR):
+    """
+
+    Parameters
+    ----------
+    name:str
+        d2v_all_256
+        d2v_sci_256
+        d2v_eng_256
+        d2v_lit_256
+        w2v_eng_300
+        w2v_lit_300
+    model_dif
+
+    Returns
+    -------
+    t2v model: T2V
+
+    Examples
+    --------
+    >>> item = [{'ques_content':'有公式$\\FormFigureID{wrong1?}$和公式$\\FormFigureBase64{wrong2?}$，\
+    ... 如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'}]
+    >>> i2v = get_pretrained_t2v("test_d2v") # doctest: +ELLIPSIS
+    >>> print(i2v(item)) # doctest: +ELLIPSIS
+    [array([...dtype=float32)]
+    """
     if name not in PRETRAINED_MODELS:
         raise KeyError(
             "Unknown pretrained model %s, use one of the provided pretrained models: %s" % (
diff --git a/docs/source/api/formula.rst b/docs/source/api/formula.rst
index a584d003..d34311c3 100644
--- a/docs/source/api/formula.rst
+++ b/docs/source/api/formula.rst
@@ -1,6 +1,10 @@
 EduNLP.Formula
 =======================
 
+.. automodule:: EduNLP.Formula.Formula
+   :members:
+   :imported-members:
+
 .. automodule:: EduNLP.Formula.ast
    :members:
    :imported-members:
diff --git a/docs/source/api/vector.rst b/docs/source/api/vector.rst
index 9081dfea..91fed6ea 100644
--- a/docs/source/api/vector.rst
+++ b/docs/source/api/vector.rst
@@ -1,10 +1,30 @@
 EduNLP.Vector
 ==========================
 
-Vector
----------------
+EduNLP.Vector.rnn
+--------------------
 
-.. automodule:: EduNLP.Vector
+.. automodule:: EduNLP.Vector.rnn
    :members:
    :imported-members:
 
+EduNLP.Vector.embedding
+-------------------------
+
+.. automodule:: EduNLP.Vector.embedding
+   :members:
+   :imported-members:
+
+EduNLP.Vector.gensim_vec
+------------------------------
+
+.. automodule:: EduNLP.Vector.gensim_vec
+   :members:
+   :imported-members:
+
+EduNLP.Vector.t2v
+--------------------
+
+.. automodule:: EduNLP.Vector.t2v
+   :members:
+   :imported-members:
\ No newline at end of file

From d82aaca0bffc3a8ae2fb0c7dcdd713101ef58fdd Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 14:00:12 +0800
Subject: [PATCH 025/149] [DOC]Update document

---
 docs/source/api/vector.rst | 18 ++----------------
 1 file changed, 2 insertions(+), 16 deletions(-)

diff --git a/docs/source/api/vector.rst b/docs/source/api/vector.rst
index 91fed6ea..1c73d4bc 100644
--- a/docs/source/api/vector.rst
+++ b/docs/source/api/vector.rst
@@ -8,23 +8,9 @@ EduNLP.Vector.rnn
    :members:
    :imported-members:
 
-EduNLP.Vector.embedding
+EduNLP.Vector
 -------------------------
 
-.. automodule:: EduNLP.Vector.embedding
-   :members:
-   :imported-members:
-
-EduNLP.Vector.gensim_vec
-------------------------------
-
-.. automodule:: EduNLP.Vector.gensim_vec
+.. automodule:: EduNLP.Vector
    :members:
    :imported-members:
-
-EduNLP.Vector.t2v
---------------------
-
-.. automodule:: EduNLP.Vector.t2v
-   :members:
-   :imported-members:
\ No newline at end of file

From 4b5294e5a008e3c048a4c56cf80f4e0b72b55dec Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 14:39:56 +0800
Subject: [PATCH 026/149] [DOC]Update documents

---
 EduNLP/I2V/i2v.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 4e57f079..e0ed5f93 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -167,7 +167,8 @@ class W2V(I2V):
 
     Examples
     --------
-    >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v")
+    >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") # doctest: +ELLIPSIS
+    ...
     >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
     >>> item_vector # doctest: +ELLIPSIS
     array([[...]], dtype=float32)
@@ -218,6 +219,7 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR):
     ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
     ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
     >>> i2v = get_pretrained_i2v("test_d2v") # doctest: +ELLIPSIS
+    ...
     >>> print(i2v(item)) # doctest: +ELLIPSIS
     ([array([ ...dtype=float32)], None)
     """

From 35f360560961d5ea717f8171aade8d2592bf0449 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 14:49:11 +0800
Subject: [PATCH 027/149] [DOC]Update document

---
 EduNLP/I2V/i2v.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index e0ed5f93..43e1d49e 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -218,7 +218,7 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR):
     >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
     ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
     ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
-    >>> i2v = get_pretrained_i2v("test_d2v") # doctest: +ELLIPSIS
+    >>> i2v = get_pretrained_i2v("test_d2v", "examples/test_model/data/d2v") # doctest: +ELLIPSIS
     ...
     >>> print(i2v(item)) # doctest: +ELLIPSIS
     ([array([ ...dtype=float32)], None)

From 508c3ee1d1a930e02c096235286487c4e2f30aea Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 14:54:52 +0800
Subject: [PATCH 028/149] [DOC]Update document

---
 EduNLP/I2V/i2v.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 43e1d49e..ec6fde2e 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -168,7 +168,7 @@ class W2V(I2V):
     Examples
     --------
     >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") # doctest: +ELLIPSIS
-    ...
+    Downloading...
     >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
     >>> item_vector # doctest: +ELLIPSIS
     array([[...]], dtype=float32)
@@ -219,7 +219,7 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR):
     ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
     ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
     >>> i2v = get_pretrained_i2v("test_d2v", "examples/test_model/data/d2v") # doctest: +ELLIPSIS
-    ...
+    Downloading...
     >>> print(i2v(item)) # doctest: +ELLIPSIS
     ([array([ ...dtype=float32)], None)
     """

From a10535a9e092869d17ad463b97c2e04b94459fb2 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 15:07:43 +0800
Subject: [PATCH 029/149] [DOC]Update documents

---
 examples/test_model/data/d2v/test_256.zip     | Bin 0 -> 4964983 bytes
 .../test_model/data/d2v/test_256/test_256.bin | Bin 0 -> 5376077 bytes
 examples/test_model/data/w2v/test_w2v_256.zip | Bin 0 -> 2106041 bytes
 .../data/w2v/test_w2v_256/test_w2v_256.kv     | Bin 0 -> 2310321 bytes
 4 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 examples/test_model/data/d2v/test_256.zip
 create mode 100644 examples/test_model/data/d2v/test_256/test_256.bin
 create mode 100644 examples/test_model/data/w2v/test_w2v_256.zip
 create mode 100644 examples/test_model/data/w2v/test_w2v_256/test_w2v_256.kv

diff --git a/examples/test_model/data/d2v/test_256.zip b/examples/test_model/data/d2v/test_256.zip
new file mode 100644
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diff --git a/examples/test_model/data/w2v/test_w2v_256/test_w2v_256.kv b/examples/test_model/data/w2v/test_w2v_256/test_w2v_256.kv
new file mode 100644
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HcmV?d00001


From c1a2ac6609dba7fe96a31f92fa7af06791445c78 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 15:21:19 +0800
Subject: [PATCH 030/149] Create i2v.py

---
 EduNLP/I2V/i2v.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index ec6fde2e..16594867 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -168,7 +168,6 @@ class W2V(I2V):
     Examples
     --------
     >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") # doctest: +ELLIPSIS
-    Downloading...
     >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
     >>> item_vector # doctest: +ELLIPSIS
     array([[...]], dtype=float32)
@@ -219,7 +218,6 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR):
     ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
     ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
     >>> i2v = get_pretrained_i2v("test_d2v", "examples/test_model/data/d2v") # doctest: +ELLIPSIS
-    Downloading...
     >>> print(i2v(item)) # doctest: +ELLIPSIS
     ([array([ ...dtype=float32)], None)
     """

From 1a6892d84b284477d5472e593d805ed4a49b8d21 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 1 Sep 2021 15:21:48 +0800
Subject: [PATCH 031/149] Create t2v.py

---
 EduNLP/Vector/t2v.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index 6e7f0556..d54b034a 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -86,7 +86,7 @@ def get_pretrained_t2v(name, model_dir=MODEL_DIR):
     --------
     >>> item = [{'ques_content':'有公式$\\FormFigureID{wrong1?}$和公式$\\FormFigureBase64{wrong2?}$，\
     ... 如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'}]
-    >>> i2v = get_pretrained_t2v("test_d2v") # doctest: +ELLIPSIS
+    >>> i2v = get_pretrained_t2v("test_d2v", "examples/test_model/data/d2v") # doctest: +ELLIPSIS
     >>> print(i2v(item)) # doctest: +ELLIPSIS
     [array([...dtype=float32)]
     """

From 90d2ca6a97ebfac3476b764190f82e92aacaa9dd Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Thu, 2 Sep 2021 21:02:05 +0800
Subject: [PATCH 032/149] update some examples

---
 examples/formula/formula.ipynb        | 17 ++++--
 examples/sif/sif.ipynb                |  4 +-
 examples/t2v/get_pretrained_t2v.ipynb |  3 +-
 examples/utils/data.ipynb             | 74 ++++++++++++++++++++-------
 4 files changed, 73 insertions(+), 25 deletions(-)

diff --git a/examples/formula/formula.ipynb b/examples/formula/formula.ipynb
index f748a90a..3ced28cf 100644
--- a/examples/formula/formula.ipynb
+++ b/examples/formula/formula.ipynb
@@ -22,7 +22,18 @@
     "from EduNLP.Formula import FormulaGroup\n",
     "from EduNLP.Formula.viz import ForestPlotter"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!\n",
+      "  warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n",
+      "/home/lvrui/.local/lib/python3.8/site-packages/EduNLP/Formula/viz/__init__.py:5: UserWarning: Do not use this package\n",
+      "  warnings.warn(\"Do not use this package\")\n"
+     ]
+    }
+   ],
    "metadata": {}
   },
   {
@@ -33,7 +44,7 @@
     "### 初始化实例\n",
     "\n",
     "- item 类型：`str or List[Dict]` \n",
-    "- item 内容：latex 公式 或 公式经解析后产生的抽象语法分析树(abstracted syntax tree)"
+    "- item 内容：latex 公式 或 公式经解析后产生的抽象语法分析树(abstracted syntax tree) "
    ],
    "metadata": {}
   },
@@ -98,7 +109,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "- 查看公式的抽象语法分析树："
+    "- 查看公式的抽象语法分析树：  "
    ],
    "metadata": {}
   },
diff --git a/examples/sif/sif.ipynb b/examples/sif/sif.ipynb
index 2076e126..41546dd9 100644
--- a/examples/sif/sif.ipynb
+++ b/examples/sif/sif.ipynb
@@ -7,7 +7,7 @@
     "\n",
     "## 概述\n",
     "\n",
-    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
+    "SIF4Sci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
    ],
    "metadata": {
     "collapsed": true,
@@ -19,7 +19,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
+    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIF4Sci 的使用方法。    \n",
     "\n",
     "![Figure](../../asset/_static/item.png)"
    ],
diff --git a/examples/t2v/get_pretrained_t2v.ipynb b/examples/t2v/get_pretrained_t2v.ipynb
index ea3fa7f8..801bcf87 100644
--- a/examples/t2v/get_pretrained_t2v.ipynb
+++ b/examples/t2v/get_pretrained_t2v.ipynb
@@ -84,7 +84,8 @@
    "source": [
     "## 模型的选择与使用\n",
     "\n",
-    "根据题目所属学科选择预训练模型：  \n",
+    "根据题目所属学科选择预训练模型：    \n",
+    "\n",
     " 预训练模型名称 | 模型训练数据的所属学科 \n",
     " -------------- | ---------------------- \n",
     " d2v_all_256    | 全学科                 \n",
diff --git a/examples/utils/data.ipynb b/examples/utils/data.ipynb
index 581dd632..dfa580f3 100644
--- a/examples/utils/data.ipynb
+++ b/examples/utils/data.ipynb
@@ -7,6 +7,13 @@
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入模块"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -18,13 +25,20 @@
      "output_type": "stream",
      "name": "stderr",
      "text": [
-      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
+      "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!\n",
+      "  warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n"
      ]
     }
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 测试数据"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -50,27 +64,49 @@
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 区分题目成分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 通过添加特殊关键词的方式，区分题目的题干和选项，选项之间按顺序做编号标记。"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "source": [
-    "# 给题目各个部分加标签\n",
-    "dict2str4sif(item)"
+    "dict2str4sif(item,key_as_tag=True,\n",
+    "             add_list_no_tag=False,\n",
+    "            #  keys=[\"options\"],\n",
+    "             tag_mode=\"head\"\n",
+    "             )\n"
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options_end}$'"
+       "'$\\\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{options}$0$\\\\SIFSep$1$\\\\SIFSep$$\\\\sqrt{2}$$\\\\SIFSep$2'"
       ]
      },
      "metadata": {},
-     "execution_count": 4
+     "execution_count": 15
     }
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -93,60 +129,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "source": [
-    "dict2str4sif(item, tag_mode=\"head\")"
+    "dict2str4sif(item, add_list_no_tag=False)"
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "'$\\\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{options}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2'"
+       "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$0$\\\\SIFSep$1$\\\\SIFSep$$\\\\sqrt{2}$$\\\\SIFSep$2$\\\\SIFTag{options_end}$'"
       ]
      },
      "metadata": {},
-     "execution_count": 7
+     "execution_count": 6
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "source": [
-    "dict2str4sif(item, tag_mode=\"tail\")"
+    "dict2str4sif(item, tag_mode=\"head\")"
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "'若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options}$'"
+       "'$\\\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{options}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2'"
       ]
      },
      "metadata": {},
-     "execution_count": 9
+     "execution_count": 7
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "source": [
-    "dict2str4sif(item, add_list_no_tag=False)"
+    "dict2str4sif(item, tag_mode=\"tail\")"
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$0$\\\\SIFSep$1$\\\\SIFSep$$\\\\sqrt{2}$$\\\\SIFSep$2$\\\\SIFTag{options_end}$'"
+       "'若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options}$'"
       ]
      },
      "metadata": {},
-     "execution_count": 10
+     "execution_count": 9
     }
    ],
    "metadata": {}

From 1d0ad185bca2e01406dc25653ba5b895d6aa541e Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Fri, 3 Sep 2021 16:05:26 +0800
Subject: [PATCH 033/149] [test] add test for tokenizer

---
 AUTHORS.md                             |  2 ++
 tests/test_tokenizer/test_tokenizer.py | 22 ++++++++++++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/AUTHORS.md b/AUTHORS.md
index 41423fa6..df16f050 100644
--- a/AUTHORS.md
+++ b/AUTHORS.md
@@ -16,5 +16,7 @@
 
 [Meikai Bao](https://github.com/BAOOOOOM)
 
+[Yuting Ning](https://github.com/nnnyt)
+
 
 The stared contributors are the corresponding authors. 
diff --git a/tests/test_tokenizer/test_tokenizer.py b/tests/test_tokenizer/test_tokenizer.py
index 9f7e03f8..acc9269c 100644
--- a/tests/test_tokenizer/test_tokenizer.py
+++ b/tests/test_tokenizer/test_tokenizer.py
@@ -8,3 +8,25 @@
 def test_tokenizer():
     with pytest.raises(KeyError):
         get_tokenizer("error")
+
+
+def test_text_tokenizer():
+    items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式\
+             $\\FormFigureBase64{wrong2?}$,$\\SIFSep$，\
+             则$z=x+7 y$的最大值为$\\SIFBlank$"]
+    tokenizer = get_tokenizer('text')
+    tokens = tokenizer(items)
+    token_list = next(tokens)[:10]
+    assert token_list == ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',',
+                          'y', '约束条件', '公式', '[FORMULA]'], token_list
+
+
+def test_pure_text_tokenzier():
+    items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式\
+             $\\FormFigureBase64{wrong2?}$,$\\SIFSep$，\
+             则$z=x+7 y$的最大值为$\\SIFBlank$"]
+    tokenizer = get_tokenizer('pure_text')
+    tokens = tokenizer(items)
+    token_list = next(tokens)[:10]
+    assert token_list == ['公式', '如图', '[FIGURE]', 'x', ',', 'y',
+                          '约束条件', '公式', '[SEP]', 'z'], token_list

From ef6f7c470458174a66169e8b93747e7fa5d2e7e8 Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Fri, 3 Sep 2021 16:53:36 +0800
Subject: [PATCH 034/149] modify examples

---
 examples/sif/sif.ipynb          | 353 ++++++++++++--------------------
 examples/sif/sif_addition.ipynb | 208 ++++++++++---------
 2 files changed, 243 insertions(+), 318 deletions(-)

diff --git a/examples/sif/sif.ipynb b/examples/sif/sif.ipynb
index 2076e126..50de8158 100644
--- a/examples/sif/sif.ipynb
+++ b/examples/sif/sif.ipynb
@@ -34,12 +34,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "source": [
-    "item = {\n",
-    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\n",
-    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\n",
-    "}\n",
+    "item = {\r\n",
+    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
+    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\r\n",
+    "}\r\n",
     "item[\"stem\"]"
    ],
    "outputs": [
@@ -51,7 +51,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 5
+     "execution_count": 1
     }
    ],
    "metadata": {
@@ -70,24 +70,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "source": [
-    "from PIL import Image\n",
-    "img = Image.open(\"../../asset/_static/item_figure.png\")\n",
-    "figures = {\"1\": img}\n",
+    "from PIL import Image\r\n",
+    "img = Image.open(\"../../asset/_static/item_figure.png\")\r\n",
+    "figures = {\"1\": img}\r\n",
     "img"
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
+      "image/png": "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",
       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ],
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"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x228D534C5F8>"
+      ]
      },
      "metadata": {},
-     "execution_count": 6
+     "execution_count": 2
     }
    ],
    "metadata": {
@@ -108,11 +108,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "source": [
     "from EduNLP.SIF import sif4sci, is_sif, to_sif"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -129,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "source": [
     "is_sif(item['stem'])"
    ],
@@ -138,11 +147,12 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "True"
+       "(True,\n",
+       " '如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$')"
       ]
      },
      "metadata": {},
-     "execution_count": 7
+     "execution_count": 4
     }
    ],
    "metadata": {}
@@ -156,9 +166,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
     "is_sif(text)"
    ],
    "outputs": [
@@ -166,21 +176,21 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "False"
+       "(False, '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...')"
       ]
      },
      "metadata": {},
-     "execution_count": 8
+     "execution_count": 5
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
-    "to_sif(text)\n"
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "to_sif(text)\r\n"
    ],
    "outputs": [
     {
@@ -191,7 +201,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 9
+     "execution_count": 6
     }
    ],
    "metadata": {}
@@ -232,9 +242,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "source": [
-    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\n",
+    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\r\n",
     "segments"
    ],
    "outputs": [
@@ -246,7 +256,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 12
+     "execution_count": 7
     }
    ],
    "metadata": {}
@@ -260,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "source": [
     "segments.text_segments"
    ],
@@ -283,7 +293,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 13
+     "execution_count": 8
     }
    ],
    "metadata": {}
@@ -297,9 +307,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "source": [
-    "segments.formula_segments\n"
+    "segments.formula_segments\r\n"
    ],
    "outputs": [
     {
@@ -319,7 +329,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 15
+     "execution_count": 9
     }
    ],
    "metadata": {}
@@ -333,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "source": [
     "segments.figure_segments"
    ],
@@ -346,14 +356,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 16
+     "execution_count": 10
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "source": [
     "segments.figure_segments[0].figure"
    ],
@@ -361,13 +371,13 @@
     {
      "output_type": "execute_result",
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ],
-      "image/png": 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"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x228D534C5F8>"
+      ]
      },
      "metadata": {},
-     "execution_count": 17
+     "execution_count": 11
     }
    ],
    "metadata": {}
@@ -381,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "source": [
     "segments.ques_mark_segments"
    ],
@@ -394,7 +404,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 19
+     "execution_count": 12
     }
    ],
    "metadata": {}
@@ -420,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "source": [
     "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
    ],
@@ -433,7 +443,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 11
+     "execution_count": 13
     }
    ],
    "metadata": {
@@ -461,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "source": [
     "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
    ],
@@ -487,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "source": [
     "tokens.text_tokens"
    ],
@@ -532,7 +542,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 12
+     "execution_count": 15
     }
    ],
    "metadata": {
@@ -556,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "source": [
     "tokens.formula_tokens"
    ],
@@ -593,7 +603,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 13
+     "execution_count": 16
     }
    ],
    "metadata": {
@@ -619,17 +629,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 17,
    "source": [
-    "sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\": {\n",
-    "            \"method\": \"linear\",\n",
-    "        }\n",
-    "    }\n",
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\": {\r\n",
+    "            \"method\": \"linear\",\r\n",
+    "        }\r\n",
+    "    }\r\n",
     ").formula_tokens"
    ],
    "outputs": [
@@ -665,7 +675,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 37
+     "execution_count": 17
     }
    ],
    "metadata": {
@@ -686,18 +696,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "source": [
-    "sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\":{\n",
-    "            \"method\": \"ast\",\n",
-    "        }\n",
-    "    }\n",
-    ").formula_tokens\n"
+   "execution_count": 18,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens\r\n"
    ],
    "outputs": [
     {
@@ -717,7 +727,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 39
+     "execution_count": 18
     }
    ],
    "metadata": {
@@ -736,49 +746,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 19,
    "source": [
-    "f = sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\":{\n",
-    "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"ast\",\n",
-    "            \"ord2token\": True,\n",
-    "            \"var_numbering\": True,\n",
-    "        }\n",
-    "    }\n",
-    ").formula_tokens\n",
-    "f\n"
+    "f = sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"return_type\": \"ast\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "            \"var_numbering\": True,\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens\r\n",
+    "f\r\n"
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
+       "[<networkx.classes.digraph.DiGraph at 0x228d54e4a20>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085ea208>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085f25f8>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085f2eb8>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085ef208>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085f3d68>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085f3f60>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085f39e8>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085f3c50>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x229085f3828>]"
       ]
      },
      "metadata": {},
-     "execution_count": 109
+     "execution_count": 19
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 20,
    "source": [
     "for i in range(0, len(f)):\n",
     "    ForestPlotter().export(\n",
@@ -786,7 +796,19 @@
     "    )\n",
     "# plt.show()\n"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'ForestPlotter' is not defined",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-20-b34613665c62>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     ForestPlotter().export(\n\u001b[0m\u001b[0;32m      3\u001b[0m         \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroot_list\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnode\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     )\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# plt.show()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'ForestPlotter' is not defined"
+     ]
+    }
+   ],
    "metadata": {}
   },
   {
@@ -799,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "source": [
     "sif4sci(\n",
     "    item[\"stem\"],\n",
@@ -814,55 +836,7 @@
     "    }\n",
     ").formula_tokens"
    ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 40
-    }
-   ],
+   "outputs": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -879,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "source": [
     "sif4sci(\n",
     "    item[\"stem\"],\n",
@@ -895,55 +869,7 @@
     "    }\n",
     ").formula_tokens"
    ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_0',\n",
-       " 'mathord_2',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " ',',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " ',',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 44
-    }
-   ],
+   "outputs": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -967,23 +893,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": null,
    "source": [
     "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\n",
     "        symbol=\"fgm\")"
    ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 96
-    }
-   ],
+   "outputs": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -995,11 +910,11 @@
  "metadata": {
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.8.5 64-bit"
+   "display_name": "Python 3.6.13 64-bit ('data': conda)"
   },
   "language_info": {
    "name": "python",
-   "version": "3.8.5",
+   "version": "3.6.13",
    "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
@@ -1010,7 +925,7 @@
    "file_extension": ".py"
   },
   "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
   }
  },
  "nbformat": 4,
diff --git a/examples/sif/sif_addition.ipynb b/examples/sif/sif_addition.ipynb
index 57830c43..c9f5424e 100644
--- a/examples/sif/sif_addition.ipynb
+++ b/examples/sif/sif_addition.ipynb
@@ -9,11 +9,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "source": [
     "from EduNLP.SIF import is_sif, to_sif,sif4sci"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
    "metadata": {}
   },
   {
@@ -25,9 +34,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "source": [
-    " text = '若$x,y$满足约束条件' \\\r\n",
+    "text = '若$x,y$满足约束条件' \\\r\n",
     "     '$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，' \\\r\n",
     "    '则$z=x+7 y$的最大值$\\\\SIFUnderline$'\r\n",
     "    \r\n",
@@ -38,18 +47,19 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "True"
+       "(True,\n",
+       " '若$x,y$满足约束条件$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，则$z=x+7 y$的最大值$\\\\SIFUnderline$')"
       ]
      },
      "metadata": {},
-     "execution_count": 4
+     "execution_count": 2
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "source": [
     "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
     "is_sif(text)"
@@ -59,11 +69,11 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "False"
+       "(False, '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...')"
       ]
      },
      "metadata": {},
-     "execution_count": 5
+     "execution_count": 3
     }
    ],
    "metadata": {}
@@ -77,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "source": [
     "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
     "to_sif(text)"
@@ -91,7 +101,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 6
+     "execution_count": 4
     }
    ],
    "metadata": {}
@@ -110,11 +120,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "source": [
-    " test_item = r\"如图所示，则$\\bigtriangleup ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$\"\r\n",
-    " t1 = sif4sci(test_item)\r\n",
-    " t1"
+    "test_item = r\"如图所示，则$\\bigtriangleup ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$\"\r\n",
+    "t1 = sif4sci(test_item)\r\n",
+    "t1"
    ],
    "outputs": [
     {
@@ -125,14 +135,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 14
+     "execution_count": 5
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "source": [
     "t1.describe()"
    ],
@@ -145,16 +155,16 @@
       ]
      },
      "metadata": {},
-     "execution_count": 15
+     "execution_count": 6
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "source": [
-    "with t1.filter('fgm'):\n",
+    "with t1.filter('fgm'):\r\n",
     "    print(t1)"
    ],
    "outputs": [
@@ -170,9 +180,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "source": [
-    "with t1.filter(keep='t'):\n",
+    "with t1.filter(keep='t'):\r\n",
     "    print(t1)"
    ],
    "outputs": [
@@ -188,9 +198,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "source": [
-    "with t1.filter():\n",
+    "with t1.filter():\r\n",
     "    print(t1)"
    ],
    "outputs": [
@@ -206,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 10,
    "source": [
     "t1.text_tokens"
    ],
@@ -219,14 +229,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 20
+     "execution_count": 10
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "source": [
     "t1.formula_tokens"
    ],
@@ -239,14 +249,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 23
+     "execution_count": 11
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "source": [
     "t1.figure_tokens"
    ],
@@ -259,14 +269,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 24
+     "execution_count": 12
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 13,
    "source": [
     "t1.ques_mark_tokens"
    ],
@@ -279,14 +289,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 25
+     "execution_count": 13
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 14,
    "source": [
     "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\"}})"
    ],
@@ -299,14 +309,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 26
+     "execution_count": 14
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "source": [
     "sif4sci(test_item, symbol=\"tfgm\")"
    ],
@@ -319,14 +329,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 27
+     "execution_count": 15
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "source": [
     "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})"
    ],
@@ -339,43 +349,43 @@
       ]
      },
      "metadata": {},
-     "execution_count": 28
+     "execution_count": 16
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 17,
    "source": [
-    " test_item_1 = {\n",
-    "     \"stem\": r\"若$x=2$, $y=\\sqrt{x}$，则下列说法正确的是$\\SIFChoice$\",\n",
-    "     \"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]\n",
-    "  }"
+    "test_item_1 = {\r\n",
+    "    \"stem\": r\"若$x=2$, $y=\\sqrt{x}$，则下列说法正确的是$\\SIFChoice$\",\r\n",
+    "    \"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]\r\n",
+    "}"
    ],
    "outputs": [],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 18,
    "source": [
-    " tls = [\n",
-    "         sif4sci(e, symbol=\"gm\",\n",
-    "         tokenization_params={\n",
-    "            \"formula_params\": {\n",
-    "                 \"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True, \"var_numbering\": True,\n",
-    "                 \"link_variable\": False}\n",
-    "         })\n",
-    "         for e in ([test_item_1[\"stem\"]] + test_item_1[\"options\"])\n",
-    "     ]"
+    "tls = [\r\n",
+    "        sif4sci(e, symbol=\"gm\",\r\n",
+    "        tokenization_params={\r\n",
+    "        \"formula_params\": {\r\n",
+    "                \"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True, \"var_numbering\": True,\r\n",
+    "                \"link_variable\": False}\r\n",
+    "        })\r\n",
+    "        for e in ([test_item_1[\"stem\"]] + test_item_1[\"options\"])\r\n",
+    "]"
    ],
    "outputs": [],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 19,
    "source": [
     "tls"
    ],
@@ -391,14 +401,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 33
+     "execution_count": 19
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 20,
    "source": [
     "tls[1:]"
    ],
@@ -413,18 +423,18 @@
       ]
      },
      "metadata": {},
-     "execution_count": 34
+     "execution_count": 20
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 21,
    "source": [
-    "from EduNLP.utils import dict2str4sif\n",
-    "\n",
-    "test_item_1_str = dict2str4sif(test_item_1, tag_mode=\"head\", add_list_no_tag=False)\n",
+    "from EduNLP.utils import dict2str4sif\r\n",
+    "\r\n",
+    "test_item_1_str = dict2str4sif(test_item_1, tag_mode=\"head\", add_list_no_tag=False)\r\n",
     "test_item_1_str "
    ],
    "outputs": [
@@ -436,21 +446,21 @@
       ]
      },
      "metadata": {},
-     "execution_count": 35
+     "execution_count": 21
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 22,
    "source": [
-    "tl1 = sif4sci(\n",
-    "    test_item_1_str, \n",
-    "    symbol=\"gm\", \n",
-    "    tokenization_params={\n",
-    "        \"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True}\n",
-    "    })\n",
+    "tl1 = sif4sci(\r\n",
+    "    test_item_1_str, \r\n",
+    "    symbol=\"gm\", \r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True}\r\n",
+    "    })\r\n",
     "    "
    ],
    "outputs": [],
@@ -458,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 23,
    "source": [
     "tl1.get_segments()[0]"
    ],
@@ -471,14 +481,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 37
+     "execution_count": 23
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 24,
    "source": [
     "tl1.get_segments()[1:3]"
    ],
@@ -492,14 +502,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 38
+     "execution_count": 24
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 25,
    "source": [
     "tl1.get_segments(add_seg_type=False)[0:3]"
    ],
@@ -514,14 +524,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 39
+     "execution_count": 25
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 26,
    "source": [
     "test_item_2 = {\"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]}"
    ],
@@ -530,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 27,
    "source": [
     "test_item_2_str = dict2str4sif(test_item_2, tag_mode=\"head\", add_list_no_tag=False)"
    ],
@@ -539,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 28,
    "source": [
     "test_item_2_str"
    ],
@@ -552,17 +562,17 @@
       ]
      },
      "metadata": {},
-     "execution_count": 43
+     "execution_count": 28
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 29,
    "source": [
-    "tl2 = sif4sci(test_item_2_str, symbol=\"gms\",\n",
-    "     tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})\n",
+    "tl2 = sif4sci(test_item_2_str, symbol=\"gms\",\r\n",
+    "     tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})\r\n",
     "tl2 "
    ],
    "outputs": [
@@ -574,14 +584,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 44
+     "execution_count": 29
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 30,
    "source": [
     "tl2.get_segments(add_seg_type=False)"
    ],
@@ -599,14 +609,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 45
+     "execution_count": 30
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 31,
    "source": [
     "tl2.get_segments(add_seg_type=False, drop=\"s\")"
    ],
@@ -619,16 +629,16 @@
       ]
      },
      "metadata": {},
-     "execution_count": 46
+     "execution_count": 31
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 32,
    "source": [
-    "tl3 = sif4sci(test_item_1[\"stem\"], symbol=\"gs\")\n",
+    "tl3 = sif4sci(test_item_1[\"stem\"], symbol=\"gs\")\r\n",
     "tl3.text_segments"
    ],
    "outputs": [
@@ -640,14 +650,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 47
+     "execution_count": 32
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 33,
    "source": [
     "tl3.formula_segments"
    ],
@@ -660,14 +670,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 48
+     "execution_count": 33
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 34,
    "source": [
     "tl3.figure_segments"
    ],
@@ -680,14 +690,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 49
+     "execution_count": 34
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 35,
    "source": [
     "tl3.ques_mark_segments"
    ],
@@ -700,7 +710,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 50
+     "execution_count": 35
     }
    ],
    "metadata": {}
@@ -717,7 +727,7 @@
   "orig_nbformat": 4,
   "language_info": {
    "name": "python",
-   "version": "3.8.5",
+   "version": "3.6.13",
    "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
@@ -729,10 +739,10 @@
   },
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.8.5 64-bit"
+   "display_name": "Python 3.6.13 64-bit ('data': conda)"
   },
   "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
   }
  },
  "nbformat": 4,

From 469af6f09e81e3e2cb2854ac9f59b3f6209ef9b2 Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Fri, 3 Sep 2021 20:47:25 +0800
Subject: [PATCH 035/149] [test] add test to cover the tokenization

---
 EduNLP/Tokenizer/tokenizer.py          |  6 ++++++
 tests/test_sif/test_tokenization.py    |  4 +++-
 tests/test_tokenizer/test_tokenizer.py | 22 ----------------------
 3 files changed, 9 insertions(+), 23 deletions(-)

diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py
index bb7b47e5..08b09e26 100644
--- a/EduNLP/Tokenizer/tokenizer.py
+++ b/EduNLP/Tokenizer/tokenizer.py
@@ -65,6 +65,12 @@ class TextTokenizer(Tokenizer):
     >>> tokens = tokenizer(items)
     >>> next(tokens)[:10]
     ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+    >>> items = ["$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$\
+    ... $\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$\
+    ... $\\SIFTag{list_3}$2$\\SIFTag{options_end}$"]
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)[:10]
+    ['[TAG]', '复数', 'z', '=', '1', '+', '2', 'i', '+', 'i']
     """
 
     def __init__(self, *args, **kwargs):
diff --git a/tests/test_sif/test_tokenization.py b/tests/test_sif/test_tokenization.py
index b904a71b..43dfde02 100644
--- a/tests/test_sif/test_tokenization.py
+++ b/tests/test_sif/test_tokenization.py
@@ -3,7 +3,7 @@
 
 import pytest
 from EduNLP.SIF.constants import Symbol
-from EduNLP.SIF.segment.segment import SegmentList
+from EduNLP.SIF.segment.segment import SegmentList, LatexFormulaSegment
 from EduNLP.SIF.tokenization import text
 from EduNLP.SIF.tokenization import formula
 from EduNLP.SIF.tokenization.tokenization import TokenList
@@ -32,3 +32,5 @@ def test_tokenization():
 
     with pytest.raises(TypeError):
         tl.append("[Unknown]")
+
+    tl.append(LatexFormulaSegment('x+y'), False)
diff --git a/tests/test_tokenizer/test_tokenizer.py b/tests/test_tokenizer/test_tokenizer.py
index acc9269c..9f7e03f8 100644
--- a/tests/test_tokenizer/test_tokenizer.py
+++ b/tests/test_tokenizer/test_tokenizer.py
@@ -8,25 +8,3 @@
 def test_tokenizer():
     with pytest.raises(KeyError):
         get_tokenizer("error")
-
-
-def test_text_tokenizer():
-    items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式\
-             $\\FormFigureBase64{wrong2?}$,$\\SIFSep$，\
-             则$z=x+7 y$的最大值为$\\SIFBlank$"]
-    tokenizer = get_tokenizer('text')
-    tokens = tokenizer(items)
-    token_list = next(tokens)[:10]
-    assert token_list == ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',',
-                          'y', '约束条件', '公式', '[FORMULA]'], token_list
-
-
-def test_pure_text_tokenzier():
-    items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式\
-             $\\FormFigureBase64{wrong2?}$,$\\SIFSep$，\
-             则$z=x+7 y$的最大值为$\\SIFBlank$"]
-    tokenizer = get_tokenizer('pure_text')
-    tokens = tokenizer(items)
-    token_list = next(tokens)[:10]
-    assert token_list == ['公式', '如图', '[FIGURE]', 'x', ',', 'y',
-                          '约束条件', '公式', '[SEP]', 'z'], token_list

From 338e7273a88a805901a46814241f70589b57cba7 Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Fri, 3 Sep 2021 22:46:22 +0800
Subject: [PATCH 036/149] [feature] modify params and comments in sif4sci

---
 EduNLP/SIF/sif.py          | 63 ++++++++++++++++++++++++++++----------
 tests/test_sif/test_sif.py | 14 +++++++++
 2 files changed, 60 insertions(+), 17 deletions(-)

diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index dd5c40a8..494eee77 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -67,26 +67,48 @@ def to_sif(item, check_formula=True):
     return sif_item
 
 
-def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = None, tokenization=True,
-            tokenization_params=None, errors="raise", check_formula=True):
+def sif4sci(item: str, figures: (dict, bool) = None, safe_mode: int = 2, symbol: str = None, tokenization=True,
+            tokenization_params=None, errors="raise"):
     r"""
 
     Default to use linear Tokenizer, change the tokenizer by specifying tokenization_params
 
     Parameters
     ----------
-    item
-    figures
-    safe
-    symbol
-    tokenization
-    tokenization_params:
-        method: which tokenizer to be used, "linear" or "ast"
-        The parameters only useful for "linear":
-
-        The parameters only useful for "ast":
-            ord2token: whether to transfer the variables (mathord) and constants (textord) to special tokens.
-            var_numbering: whether to use number suffix to denote different variables
+    item: str
+    figures:
+        when it is a dict, it means the id-to-instance information for figures in 'FormFigureID{...}' format,
+        when it is a bool, it means whether to instantiate figures in 'FormFigureBase64{...}' format
+    safe_mode: int
+        when safe = 2, use is_sif and check formula in item
+        when safe = 1, use is_sif but don't check formula in item
+        when safe = 0, don't use is_sif and don't check anything in item
+    symbol: str
+        The combination of "t","f","g","m","a","s", which determine what types of segments to be symbolize.
+        "t": text,
+        "f": formula,
+        "g": figuew,
+        "m": mask,
+        "a": tab,
+        "s": sep
+    tokenization: bool
+        whether to tokenize item after segmentation
+    tokenization_params: dict
+        the dict of text_params, formula_params and figure_params in tokenization
+        For formula_params:
+            method: which tokenizer to be used, "linear" or "ast"
+            The parameters only useful for "linear":
+                skip_figure_formula: whether to skip the formula in figure format
+                symbolize_figure_formula: whether to symbolize the formula in figure format
+            The parameters only useful for "ast":
+                ord2token: whether to transfer the variables (mathord) and constants (textord) to special tokens.
+                var_numbering: whether to use number suffix to denote different variables
+                return_type: 'list' or 'ast'
+            More parameters can be found in the definition in SIF.tokenization.formula
+        For figure_params:
+            figure_instance：whether to return instance of figures in tokens
+        For text_params:
+            See definition in SIF.tokenization.text
     errors:
         warn
         raise
@@ -191,9 +213,16 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = No
     [['已知'], ['说法', '中', '正确']]
     """
     try:
-        if safe is True:
-            flag, sif_item = is_sif(item, check_formula)
-            item = sif_item if flag is not True else item
+        if safe_mode == 2:
+            _, item = is_sif(item, check_formula=True)
+        elif safe_mode == 1:
+            _, item = is_sif(item, check_formula=False)
+        elif safe_mode == 0:
+            pass  # do nothing
+        else:
+            raise KeyError(
+                "Unknown safe_mode %s, use only 0 or 1 or 2." % safe_mode
+            )
 
         ret = seg(item, figures, symbol)
 
diff --git a/tests/test_sif/test_sif.py b/tests/test_sif/test_sif.py
index 0f1febce..53ec2663 100644
--- a/tests/test_sif/test_sif.py
+++ b/tests/test_sif/test_sif.py
@@ -57,3 +57,17 @@ def test_sci4sif(figure0, figure1, figure0_base64, figure1_base64):
             "figure_params": {"figure_instance": True}
         }
     ))
+    repr(sif4sci(
+        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", safe_mode=0
+    ))
+    repr(sif4sci(
+        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", safe_mode=1
+    ))
+    repr(sif4sci(
+        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", safe_mode=2
+    ))
+
+    with pytest.raises(KeyError):
+        repr(sif4sci(
+            r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", safe_mode=3
+        ))

From ed4e3a1ca96295157bd3fc2d44957214effb1e29 Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Sun, 5 Sep 2021 15:48:41 +0800
Subject: [PATCH 037/149] update sif.py api

---
 EduNLP/SIF/sif.py | 32 ++++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index af4fa63a..ae2166f4 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -14,7 +14,8 @@ def is_sif(item):
     r"""
     Parameters
     ----------
-    item
+    item(str):
+        the text of question
 
     Returns
     -------
@@ -46,11 +47,13 @@ def to_sif(item):
     r"""
     Parameters
     ----------
-    item
+    item(str):
+        the text of question
 
     Returns
     -------
-    item
+    item(str):
+        the question's text after sif correction
 
     Examples
     --------
@@ -73,11 +76,24 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = No
 
     Parameters
     ----------
-    item
-    figures
-    safe
-    symbol
-    tokenization
+    item(str):
+        the text of question
+    figures(dict):
+        {"FigureID": Base64 encoding of the figure}
+
+    safe(bool):
+        default is True
+
+    symbol(str):
+        set the parameter to_symbolize:
+            "t": text
+            "f": formula
+            "g": figure
+            "m": question mark
+
+    tokenization(bool):
+        True: tokenize the item
+
     tokenization_params:
         method: which tokenizer to be used, "linear" or "ast"
         The parameters only useful for "linear":

From 259b7ca198421e748732cd749865c74e83d1a910 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 5 Sep 2021 21:21:00 +0800
Subject: [PATCH 038/149] update documents

---
 docs/source/api/ModelZoo.rst | 2 +-
 docs/source/api/i2v.rst      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/api/ModelZoo.rst b/docs/source/api/ModelZoo.rst
index ffdc764d..4a624cb9 100644
--- a/docs/source/api/ModelZoo.rst
+++ b/docs/source/api/ModelZoo.rst
@@ -1,5 +1,5 @@
 EduNLP.ModelZoo
-==============
+==================
 
 rnn
 -----------
diff --git a/docs/source/api/i2v.rst b/docs/source/api/i2v.rst
index 8dff365f..29d40879 100644
--- a/docs/source/api/i2v.rst
+++ b/docs/source/api/i2v.rst
@@ -18,7 +18,7 @@ EduNLP.I2V.W2V
    :imported-members:
 
 EduNLP.I2V.get_pretrained_i2v
--------------------
+---------------------------------------
 .. automodule:: EduNLP.I2V.i2v.get_pretrained_i2v
    :members:
    :imported-members:

From d02608ee59e8f3e6d12410b590ed0dbbf8aa8a25 Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Sun, 5 Sep 2021 22:21:52 +0800
Subject: [PATCH 039/149] [DOC] update i2v,pretrained,sif,tokenizer,vector api
 documents

---
 EduNLP/I2V/i2v.py             | 76 +++++++++++++++++++++++++++--------
 EduNLP/Pretrain/gensim_vec.py | 66 +++++++++++++++++-------------
 EduNLP/SIF/segment/segment.py | 12 +++---
 EduNLP/SIF/sif.py             | 33 ++++++++-------
 EduNLP/Tokenizer/tokenizer.py | 14 ++++---
 EduNLP/Vector/t2v.py          | 23 +++++++----
 6 files changed, 147 insertions(+), 77 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 16594867..3152bb7a 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -26,6 +26,8 @@ class I2V(object):
     tokenizer_kwargs: dict
         the parameters passed to tokenizer
     pretrained_t2v: bool
+        True: use pretrained t2v model
+        False: use your own t2v model
     kwargs:
         the parameters passed to t2v
 
@@ -58,6 +60,8 @@ def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrai
         tokenizer_kwargs: dict
             the parameters passed to tokenizer
         pretrained_t2v: bool
+            True: use pretrained t2v model
+            False: use your own t2v model
         kwargs:
             the parameters passed to t2v
 
@@ -82,7 +86,7 @@ def __call__(self, items, *args, **kwargs):
         return self.infer_vector(items, *args, **kwargs)
 
     def tokenize(self, items, indexing=True, padding=False, key=lambda x: x, *args, **kwargs) -> list:
-        """tokenize item"""
+        # """tokenize item"""
         return self.tokenizer(items, key=key, *args, **kwargs)
 
     def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=lambda x: x, *args,
@@ -121,14 +125,30 @@ def vector_size(self):
 
 class D2V(I2V):
     """
+    Parameters
+    ----------
+    tokenizer: str
+        the tokenizer name
+    t2v: str
+        the name of token2vector model
+    args:
+        the parameters passed to t2v
+    tokenizer_kwargs: dict
+        the parameters passed to tokenizer
+    pretrained_t2v: bool
+        True: use pretrained t2v model
+        False: use your own t2v model
+    kwargs:
+        the parameters passed to t2v
+
     Examples
     --------
     >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
     ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
     ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
-    >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
-    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
-    >>> i2v(item) # doctest: +ELLIPSIS
+    >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+    >>> i2v(item)
     ([array([ ...dtype=float32)], None)
 
     Returns
@@ -142,16 +162,21 @@ def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=
         Parameters
         ----------
         items:str
-        tokenize
-        indexing
-        padding
-        key
-        args
-        kwargs
+            the text of question
+        tokenize:bool
+            True: tokenize the item
+        indexing:bool
+        padding:bool
+        key: lambda function
+            the parameter passed to tokenizer, select the text to be processed
+        args:
+            the parameters passed to t2v
+        kwargs:
+            the parameters passed to t2v
 
         Returns
         -------
-        vector
+        vector:list
         '''
         tokens = self.tokenize(items, return_token=True, key=key) if tokenize is True else items
         tokens = [token for token in tokens]
@@ -164,12 +189,27 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
 
 class W2V(I2V):
     """
+    Parameters
+    ----------
+    tokenizer: str
+        the tokenizer name
+    t2v: str
+        the name of token2vector model
+    args:
+        the parameters passed to t2v
+    tokenizer_kwargs: dict
+        the parameters passed to tokenizer
+    pretrained_t2v: bool
+        True: use pretrained t2v model
+        False: use your own t2v model
+    kwargs:
+        the parameters passed to t2v
 
     Examples
     --------
-    >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") # doctest: +ELLIPSIS
+    >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v")
     >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
-    >>> item_vector # doctest: +ELLIPSIS
+    >>> item_vector
     array([[...]], dtype=float32)
 
     Returns
@@ -205,8 +245,10 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR):
 
     Parameters
     ----------
-    name
-    model_dir
+    name: str
+        the name of item2vector model
+    model_dir:str
+        the path of model, default: MODEL_DIR = '~/.EduNLP/model'
 
     Returns
     -------
@@ -217,8 +259,8 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR):
     >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
     ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
     ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
-    >>> i2v = get_pretrained_i2v("test_d2v", "examples/test_model/data/d2v") # doctest: +ELLIPSIS
-    >>> print(i2v(item)) # doctest: +ELLIPSIS
+    >>> i2v = get_pretrained_i2v("test_d2v", "examples/test_model/data/d2v")
+    >>> print(i2v(item))
     ([array([ ...dtype=float32)], None)
     """
     if name not in MODELS:
diff --git a/EduNLP/Pretrain/gensim_vec.py b/EduNLP/Pretrain/gensim_vec.py
index 8acd41fe..cd0823b7 100644
--- a/EduNLP/Pretrain/gensim_vec.py
+++ b/EduNLP/Pretrain/gensim_vec.py
@@ -19,14 +19,20 @@ class GensimWordTokenizer(object):
 
     Parameters
     ----------
-    symbol:
-        gm
-        fgm
-        gmas
-        fgmas
-    general:
-        True when item isn't in standard format, and want to tokenize formulas(except formulas in figure) linearly.
-        False when use 'ast' mothed to tokenize formulas instead of 'linear'.
+    symbol: str
+        select the methods to symbolize:
+            "t": text,
+            "f": formula,
+            "g": figure,
+            "m": question mark,
+            "a": tag,
+            "s": sep,
+        e.g.: gm, fgm, gmas, fgmas
+    general: bool
+
+        True: when item isn't in standard format, and want to tokenize formulas(except formulas in figure) linearly.
+
+        False: when use 'ast' mothed to tokenize formulas instead of 'linear'.
 
     Returns
     ----------
@@ -77,14 +83,22 @@ class GensimSegTokenizer(object):  # pragma: no cover
 
     Parameters
     ----------
-    symbol:
-        gms
-        fgm
+    symbol:str
+        select the methods to symbolize:
+            "t": text,
+            "f": formula,
+            "g": figure,
+            "m": question mark,
+            "a": tag,
+            "s": sep,
+        e.g. gms, fgm
+
     depth: int or None
-        0: only separate at \\SIFSep
-        1: only separate at \\SIFTag
-        2: separate at \\SIFTag and \\SIFSep
-        otherwise, separate all segments
+
+        0: only separate at \\SIFSep ;
+        1: only separate at \\SIFTag ;
+        2: separate at \\SIFTag and \\SIFSep ;
+        otherwise, separate all segments ;
 
     Returns
     ----------
@@ -95,7 +109,7 @@ class GensimSegTokenizer(object):  # pragma: no cover
     >>> tokenizer = GensimSegTokenizer(symbol="gms", depth=None)
     >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
     ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-    >>> print(token_item[:10]) # doctest: +ELLIPSIS
+    >>> print(token_item[:10])
     [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
     >>> tokenizer = GensimSegTokenizer(symbol="fgm", depth=None)
     >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
@@ -156,20 +170,18 @@ def train_vector(items, w2v_prefix, embedding_dim=None, method="sg", binary=None
     Parameters
     ----------
     items：str
+        the text of question
     w2v_prefix
     embedding_dim:int
         vector_size
     method:str
-        sg
-        cbow
-        fasttext
-        d2v
-        bow
-        tfidf
-    binary:model format
-        True:bin
+        the method of training,
+        e.g.: sg, cbow, fasttext, d2v, bow, tfidf
+    binary: model format
+        True:bin;
         False:kv
-    train_params
+    train_params: dict
+        the training parameters passed to model
 
     Returns
     ----------
@@ -180,12 +192,12 @@ def train_vector(items, w2v_prefix, embedding_dim=None, method="sg", binary=None
     >>> tokenizer = GensimSegTokenizer(symbol="gms", depth=None)
     >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
     ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-    >>> print(token_item[:10]) # doctest: +ELLIPSIS
+    >>> print(token_item[:10])
     [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
     >>> tokenizer = GensimSegTokenizer(symbol="fgm", depth=None)
     >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
     ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-    >>> print(token_item[:10]) # doctest: +ELLIPSIS
+    >>> print(token_item[:10])
     [['公式'], ['[FORMULA]'], ['如图'], ['[FIGURE]'], ['[FORMULA]'],...['最大值'], ['[MARK]']]
     """
     monitor = MonitorCallback(["word", "I", "less"])
diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index 656f5f69..5b9c59a0 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -197,6 +197,8 @@ def symbolize(self, to_symbolize="fgm"):
             "f": formula
             "g": figure
             "m": question mark
+            "a": tag
+            "s": sep 
 
         Returns
         -------
@@ -315,18 +317,18 @@ def seg(item, figures=None, symbol=None):
     ... }
     >>> from EduNLP.utils import dict2str4sif
     >>> test_item_1_str = dict2str4sif(test_item_1)
-    >>> test_item_1_str  # doctest: +ELLIPSIS
+    >>> test_item_1_str
     '$\\SIFTag{stem_begin}$...$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0...$\\SIFTag{options_end}$'
     >>> s1 = seg(test_item_1_str, symbol="tfgm")
-    >>> s1  # doctest: +ELLIPSIS
+    >>> s1
     ['\\SIFTag{stem_begin}'...'\\SIFTag{stem_end}', '\\SIFTag{options_begin}', '\\SIFTag{list_0}', ...]
     >>> with s1.filter(keep="a"):
-    ...     s1  # doctest: +ELLIPSIS
+    ...     s1
     [...'\\SIFTag{list_0}', '\\SIFTag{list_1}', '\\SIFTag{list_2}', '\\SIFTag{list_3}', '\\SIFTag{options_end}']
-    >>> s1.tag_segments  # doctest: +ELLIPSIS
+    >>> s1.tag_segments
     ['\\SIFTag{stem_begin}', '\\SIFTag{stem_end}', '\\SIFTag{options_begin}', ... '\\SIFTag{options_end}']
     >>> test_item_1_str_2 = dict2str4sif(test_item_1, tag_mode="head", add_list_no_tag=False)
-    >>> seg(test_item_1_str_2, symbol="tfgmas")  # doctest: +ELLIPSIS
+    >>> seg(test_item_1_str_2, symbol="tfgmas")
     ['[TAG]', ... '[TAG]', '[TEXT]', '[SEP]', '[TEXT]', '[SEP]', '[FORMULA]', '[SEP]', '[TEXT]']
     >>> s2 = seg(test_item_1_str_2, symbol="fgm")
     >>> s2.tag_segments
diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index ae2166f4..013ce87b 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -14,7 +14,7 @@ def is_sif(item):
     r"""
     Parameters
     ----------
-    item(str):
+    item:str
         the text of question
 
     Returns
@@ -47,12 +47,12 @@ def to_sif(item):
     r"""
     Parameters
     ----------
-    item(str):
+    items:str
         the text of question
 
     Returns
     -------
-    item(str):
+    item:str
         the question's text after sif correction
 
     Examples
@@ -76,36 +76,39 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = No
 
     Parameters
     ----------
-    item(str):
+    item:str
         the text of question
-    figures(dict):
+    figures:dict
         {"FigureID": Base64 encoding of the figure}
 
-    safe(bool):
-        default is True
+    safe:bool
+        Check whether the text conforms to the sif format
 
-    symbol(str):
-        set the parameter to_symbolize:
+    symbol:str
+        select the methods to symbolize:
             "t": text
             "f": formula
             "g": figure
             "m": question mark
+            "a": tag
+            "s": sep
 
-    tokenization(bool):
+    tokenization:bool
         True: tokenize the item
 
     tokenization_params:
         method: which tokenizer to be used, "linear" or "ast"
-        The parameters only useful for "linear":
+
+        The parameters only useful for "linear": None
 
         The parameters only useful for "ast":
             ord2token: whether to transfer the variables (mathord) and constants (textord) to special tokens.
             var_numbering: whether to use number suffix to denote different variables
     errors:
-        warn
-        raise
-        coerce
-        strict
+        warn,
+        raise,
+        coerce,
+        strict,
         ignore
 
     Returns
diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py
index 646fc083..bd51c19d 100644
--- a/EduNLP/Tokenizer/tokenizer.py
+++ b/EduNLP/Tokenizer/tokenizer.py
@@ -15,7 +15,6 @@ def __call__(self, *args, **kwargs):
 
 class PureTextTokenizer(Tokenizer):
     r"""
-
     Examples
     --------
     >>> tokenizer = PureTextTokenizer()
@@ -26,7 +25,7 @@ class PureTextTokenizer(Tokenizer):
     ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
     >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
     >>> tokens = tokenizer(items)
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    >>> next(tokens)
     ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
     '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
     '\\quad', 'A', '\\cap', 'B', '=']
@@ -35,7 +34,7 @@ class PureTextTokenizer(Tokenizer):
     ... "options": ["1", "2"]
     ... }]
     >>> tokens = tokenizer(items, key=lambda x: x["stem"])
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    >>> next(tokens)
     ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
     '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
     '\\quad', 'A', '\\cap', 'B', '=']
@@ -121,8 +120,11 @@ def get_tokenizer(name, *args, **kwargs):
     Parameters
     ----------
     name: str
-    args
-    kwargs
+        the name of tokenizer, e.g. text, pure_text.
+    args:
+        the parameters passed to tokenizer
+    kwargs:
+        the parameters passed to tokenizer
 
     Returns
     -------
@@ -133,7 +135,7 @@ def get_tokenizer(name, *args, **kwargs):
     >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
     >>> tokenizer = get_tokenizer("text")
     >>> tokens = tokenizer(items)
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    >>> next(tokens)
     ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
     '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
     '\\quad', 'A', '\\cap', 'B', '=']
diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index d54b034a..9adfe5e5 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -21,6 +21,12 @@
 
 class T2V(object):
     """
+    Parameters
+    ----------
+    model: str
+        select the model type
+        e.g.: d2v, rnn, lstm, gru, elmo, etc.
+
     Examples
     --------
     >>> item = [{'ques_content':'有公式$\\FormFigureID{wrong1?}$和公式$\\FormFigureBase64{wrong2?}$，\
@@ -70,13 +76,16 @@ def get_pretrained_t2v(name, model_dir=MODEL_DIR):
     Parameters
     ----------
     name:str
-        d2v_all_256
-        d2v_sci_256
-        d2v_eng_256
-        d2v_lit_256
-        w2v_eng_300
-        w2v_lit_300
-    model_dif
+        select the pretrained model
+        e.g.:
+        d2v_all_256,
+        d2v_sci_256,
+        d2v_eng_256,
+        d2v_lit_256,
+        w2v_eng_300,
+        w2v_lit_300.
+    model_dir:str
+        the path of model, default: MODEL_DIR = '~/.EduNLP/model'
 
     Returns
     -------

From 82ca552d39d0323a1ea56a9c9828d3f71cec8d9c Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Sun, 5 Sep 2021 22:57:00 +0800
Subject: [PATCH 040/149] [DOC] update i2v,pretrained,sif,tokenizer,vector api
 documents 2.0

---
 EduNLP/SIF/segment/segment.py | 2 +-
 EduNLP/Tokenizer/tokenizer.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index 5b9c59a0..6d6550d0 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -198,7 +198,7 @@ def symbolize(self, to_symbolize="fgm"):
             "g": figure
             "m": question mark
             "a": tag
-            "s": sep 
+            "s": sep
 
         Returns
         -------
diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py
index bd51c19d..45b50f1f 100644
--- a/EduNLP/Tokenizer/tokenizer.py
+++ b/EduNLP/Tokenizer/tokenizer.py
@@ -25,7 +25,7 @@ class PureTextTokenizer(Tokenizer):
     ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
     >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
     >>> tokens = tokenizer(items)
-    >>> next(tokens)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
     ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
     '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
     '\\quad', 'A', '\\cap', 'B', '=']
@@ -34,7 +34,7 @@ class PureTextTokenizer(Tokenizer):
     ... "options": ["1", "2"]
     ... }]
     >>> tokens = tokenizer(items, key=lambda x: x["stem"])
-    >>> next(tokens)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
     ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
     '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
     '\\quad', 'A', '\\cap', 'B', '=']
@@ -135,7 +135,7 @@ def get_tokenizer(name, *args, **kwargs):
     >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
     >>> tokenizer = get_tokenizer("text")
     >>> tokens = tokenizer(items)
-    >>> next(tokens)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
     ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
     '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
     '\\quad', 'A', '\\cap', 'B', '=']

From 3483c3ac8e0a3197a30af380f74c8cd653c4ba97 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 6 Sep 2021 02:02:09 +0800
Subject: [PATCH 041/149] [DOC]Update documents

---
 docs/source/_static/formula.png               | Bin 0 -> 5779 bytes
 docs/source/_static/formulagroup.png          | Bin 0 -> 5481 bytes
 docs/source/tutorial/zh/index.rst             |  12 -
 docs/source/tutorial/zh/parse.rst             | 246 ++++-
 ...3\346\236\204\350\247\243\346\236\220.rst" | 181 +++-
 ...3\346\236\204\350\247\243\346\236\220.rst" |  59 +-
 docs/source/tutorial/zh/seg.rst               | 156 ++-
 ...0\345\210\206\345\210\206\350\247\243.rst" |  24 +-
 ...0\345\210\206\345\210\206\350\247\243.rst" |  10 +-
 docs/source/tutorial/zh/sif.rst               |  31 +-
 ...tTokenizer.ipynb => PureTextTokenizer.rst} |   0
 docs/source/tutorial/zh/tokenize.rst          | 159 ++-
 examples/formula/formula.ipynb                | 417 +-------
 examples/sif/sif.ipynb                        | 973 ++----------------
 14 files changed, 855 insertions(+), 1413 deletions(-)
 create mode 100644 docs/source/_static/formula.png
 create mode 100644 docs/source/_static/formulagroup.png
 rename docs/source/tutorial/zh/tokenization/{PureTextTokenizer.ipynb => PureTextTokenizer.rst} (100%)

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HcmV?d00001

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 546065b0..d10a4e67 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -16,18 +16,6 @@
 示例
 --------
 
-标准项目格式
-^^^^^^^^
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: sif_gallery
-    :glob:
-    
-    Code for beginner to learn how to use SIF4Sci  <../../build/blitz/sif/sif>
-    Code for beginner to learn how to use sif_additon  <../../build/blitz/sif/sif_addition>
-
-
 成分分解
 ^^^^^^^^^^^
 
diff --git a/docs/source/tutorial/zh/parse.rst b/docs/source/tutorial/zh/parse.rst
index 9d6ea22e..19d09d97 100644
--- a/docs/source/tutorial/zh/parse.rst
+++ b/docs/source/tutorial/zh/parse.rst
@@ -24,13 +24,245 @@
 
 4.匹配latex公式，主要检查latex公式的完整性和可解析性，对latex 中出现中文字符发出警告
 
-学习路线图
+公式语法结构解析
 --------------------
 
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-   
-   文本语法结构解析 <parse/文本语法结构解析>
-   公式语法结构解析 <parse/公式语法结构解析>
+本功能主要由EduNLP.Formula模块实现，具有检查传入的公式是否合法，并将合法的公式转换为art树的形式。从实际使用的角度，本模块常作为中间处理过程，调用相应的模型即可自动选择本模块的相关参数，故一般不需要特别关注。
+
+主要内容介绍
++++++++++++++++
+
+1.Formula:对传入的单个公式进行判断，判断传入的公式是否为str形式，如果是则使用ast的方法进行处理，否则进行报错。此外，提供了variable_standardization参数，当此参数为True时，使用变量标准化方法，即同一变量拥有相同的变量编号。
+
+2.FormulaGroup:如果需要传入公式集则可调用此接口，最终将形成ast森林，森林中树的结构同Formula。
+
+Formula
+>>>>>>>>>>>>
+
+Formula 首先在分词功能中对原始文本的公式做切分处理，另外提供 ``公式解析树`` 功能，可以将数学公式的抽象语法分析树用文本或图片的形式表示出来。  
+
+本模块另提供公式变量标准化的功能，如判断几个子公式内的‘x’为同一变量。
+
+初始化
++++++++++
+
+传入参数：item 
+
+item为str 或 List[Dict]类型，具体内容为latex 公式 或 公式经解析后产生的抽象语法分析树。
+
+::
+
+   >>> f=Formula("x^2 + x+1 = y")
+   >>> f
+   <Formula: x^2 + x+1 = y>
+
+查看公式切分后的具体内容
+++++++++++++++++++++++++++++
+
+- 查看公式切分后的结点元素
+
+::
+
+   >>> f.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]
+
+- 查看公式的抽象语法分析树
+
+::
+
+   >>> f.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],'child': [1, 2],'father': None,'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [7, None],'child': None,'father': None,'forest': None}}]
+
+   >>> print('nodes: ',f.ast_graph.nodes)
+   nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]
+   >>> print('edges: ' ,f.ast_graph.edges)
+   edges:  [(0, 1), (0, 2)]
+
+- 将抽象语法分析树用图片表示
+
+::
+
+   >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
+   >>> plt.show()
+
+.. figure:: ../../../_static/formula.png
+
+变量标准化
++++++++++++
+
+此参数使得同一变量拥有相同的变量编号。
+
+如：``x`` 变量的编号为 ``0``， ``y`` 变量的编号为 ``1``。
+
+::
+
+   >>> f.variable_standardization().elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+
+FormulaGroup
+>>>>>>>>>>>>>>>
+
+调用 ``FormulaGroup`` 类解析公式方程组，相关的属性和函数方法同上。
+
+::
+
+   >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
+   >>> fs
+   <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
+   >>> fs.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},
+   {'id': 8, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 9, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},
+   {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 13, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 15, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 16, 'type': 'mathord', 'text': '\\pi', 'role': None}]
+   >>> fs.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],
+      'child': [1, 2],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2],
+      'child': None,
+      'father': 0,
+      'forest': [6, 12]}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [3, None],
+      'child': None,
+      'father': None,
+      'forest': [10, 14]}},
+   {'val': {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 8],
+      'child': [6, 7],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   show more (open the raw output data in a text editor) ...
+   >>> fs.variable_standardization()[0]
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+   >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
+
+.. figure:: ../../../_static/formulagroup.png
+
+文本语法结构解析
+--------------------
+
+本部分主要由EduNLP.SIF.Parse模块实现，主要功能为将文本中的字母、数字等进行提取，将其转换为标准格式。
+
+此模块主要作为 *中间模块* 来对输入的生文本进行解析处理，用户一般不直接调用此模块。
+
+主要流程介绍
++++++++++++++++
+
+1.按照以下顺序，先后对传入的文本进行判断类型
+
+* is_chinese：用于匹配中文字符 [\u4e00-\u9fa5]
+ 
+* is_alphabet：匹配公式之外的英文字母，将匹配到的只对两个汉字之间的字母做修正（使用$$包裹起来），其余匹配到的情况视为不合 latex 语法录入的公式
+ 
+* is_number：匹配公式之外的数字，只对两个汉字之间的数字做修正（使用$$包裹起来），其余匹配到的情况视为不合 latex 语法录入的公式
+ 
+2.匹配 latex 公式
+
+* latex 中出现中文字符，打印且只打印一次 warning
+ 
+* 使用_is_formula_legal函数，检查latex公式的完整性和可解析性，对于不合法公式报错
+
+输入
+>>>>>>>
+
+类型：str  
+
+内容：题目文本 （text）
+
+::
+
+   >>> text1 = '生产某种零件的A工厂25名工人的日加工零件数_   _'
+   >>> text2 = 'X的分布列为(   )'
+   >>> text3 = '① AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．AC的中点为D'
+   >>> text4 = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
+
+进行解析
+>>>>>>>>>>>>>>>>>>>>
+
+::
+
+   >>> text_parser1 = Parser(text1)
+   >>> text_parser2 = Parser(text2)
+   >>> text_parser3 = Parser(text3)
+   >>> text_parser4 = Parser(text4)
+
+相关描述参数
+>>>>>>>>>>>>
+
+- 尝试转换为标准形式
+
+::
+
+   >>> text_parser1.description_list()
+   >>> print('text_parser1.text:',text_parser1.text)
+   text_parser1.text: 生产某种零件的$A$工厂$25$名工人的日加工零件数$\SIFBlank$
+   >>> text_parser2.description_list()
+   >>> print('text_parser2.text:',text_parser2.text)
+   text_parser2.text: $X$的分布列为$\SIFChoice$
+
+- 判断是否有语法问题
+
+::
+
+   >>> text_parser3.description_list()
+   >>> print('text_parser3.error_flag: ',text_parser3.error_flag)
+   text_parser3.error_flag:  1
+   >>> text_parser4.description_list()
+   >>> print('text_parser4.fomula_illegal_flag: ',text_parser4.fomula_illegal_flag)
+   text_parser4.fomula_illegal_flag:  1
 
diff --git "a/docs/source/tutorial/zh/parse/\345\205\254\345\274\217\350\257\255\346\263\225\347\273\223\346\236\204\350\247\243\346\236\220.rst" "b/docs/source/tutorial/zh/parse/\345\205\254\345\274\217\350\257\255\346\263\225\347\273\223\346\236\204\350\247\243\346\236\220.rst"
index 1a7717fb..94d8517c 100644
--- "a/docs/source/tutorial/zh/parse/\345\205\254\345\274\217\350\257\255\346\263\225\347\273\223\346\236\204\350\247\243\346\236\220.rst"
+++ "b/docs/source/tutorial/zh/parse/\345\205\254\345\274\217\350\257\255\346\263\225\347\273\223\346\236\204\350\247\243\346\236\220.rst"
@@ -10,52 +10,159 @@
 
 2.FormulaGroup:如果需要传入公式集则可调用此接口，最终将形成ast森林，森林中树的结构同Formula。
 
+Formula
+>>>>>>>>>>>>
 
-Examples：
+Formula 首先在分词功能中对原始文本的公式做切分处理，另外提供 ``公式解析树`` 功能，可以将数学公式的抽象语法分析树用文本或图片的形式表示出来。  
+
+本模块另提供公式变量标准化的功能，如判断几个子公式内的‘x’为同一变量。
+
+初始化
++++++++++
+
+传入参数：item 
+
+item为str 或 List[Dict]类型，具体内容为latex 公式 或 公式经解析后产生的抽象语法分析树。
 
 ::
 
- >>> text = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
- >>> text_parser = Parser(text)
- >>> text_parser.description_list()
- >>> text_parser.fomula_illegal_flag
- >>> 1
+   >>> f=Formula("x^2 + x+1 = y")
+   >>> f
+   <Formula: x^2 + x+1 = y>
+
+查看公式切分后的具体内容
+++++++++++++++++++++++++++++
+
+- 查看公式切分后的结点元素
 
 ::
 
- >>> f = Formula("x")
- >>> f
- <Formula: x>
- >>> f.ast
- [{'val': {'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, 'structure': {'bro': [None, None], 'child': None, 'father': None, 'forest': None}}]
- >>> f.elements
- [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}]
- >>> f.variable_standardization(inplace=True)
- <Formula: x>
- >>> f.elements
- [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
+   >>> f.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]
+
+- 查看公式的抽象语法分析树
 
 ::
 
- >>> fg = FormulaGroup(["x + y", "y + x", "z + x"])
- >>> fg
- <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
- >>> fg = FormulaGroup(["x + y", Formula("y + x"), "z + x"])
- >>> fg
- <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
- >>> fg = FormulaGroup(["x", Formula("y"), "x"])
- >>> fg.elements
- [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None},\
- {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
- >>> fg = FormulaGroup(["x", Formula("y"), "x"], variable_standardization=True)
- >>> fg.elements
- [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
-
-详细示范
-+++++++++++++++
+   >>> f.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],'child': [1, 2],'father': None,'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [7, None],'child': None,'father': None,'forest': None}}]
+
+   >>> print('nodes: ',f.ast_graph.nodes)
+   nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]
+   >>> print('edges: ' ,f.ast_graph.edges)
+   edges:  [(0, 1), (0, 2)]
+
+- 将抽象语法分析树用图片表示
+
+::
+
+   >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
+   >>> plt.show()
+
+.. figure:: ../../../_static/formula.png
+
+变量标准化
++++++++++++
+
+此参数使得同一变量拥有相同的变量编号。
+
+如：``x`` 变量的编号为 ``0``， ``y`` 变量的编号为 ``1``。
+
+::
+
+   >>> f.variable_standardization().elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+
+FormulaGroup
+>>>>>>>>>>>>>>>
+
+调用 ``FormulaGroup`` 类解析公式方程组，相关的属性和函数方法同上。
+
+::
 
-.. toctree::
-   :titlesonly:
+   >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
+   >>> fs
+   <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
+   >>> fs.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},
+   {'id': 8, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 9, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},
+   {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 13, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 15, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 16, 'type': 'mathord', 'text': '\\pi', 'role': None}]
+   >>> fs.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],
+      'child': [1, 2],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2],
+      'child': None,
+      'father': 0,
+      'forest': [6, 12]}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [3, None],
+      'child': None,
+      'father': None,
+      'forest': [10, 14]}},
+   {'val': {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 8],
+      'child': [6, 7],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   show more (open the raw output data in a text editor) ...
+   >>> fs.variable_standardization()[0]
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+   >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
 
-   树型处理效果 <../../../build/blitz/formula/tree.ipynb>
-   公式解析效果案例 <../../../build/blitz/formula/formula.ipynb>
+.. figure:: ../../../_static/formulagroup.png
\ No newline at end of file
diff --git "a/docs/source/tutorial/zh/parse/\346\226\207\346\234\254\350\257\255\346\263\225\347\273\223\346\236\204\350\247\243\346\236\220.rst" "b/docs/source/tutorial/zh/parse/\346\226\207\346\234\254\350\257\255\346\263\225\347\273\223\346\236\204\350\247\243\346\236\220.rst"
index f2f442a0..aaa54b64 100644
--- "a/docs/source/tutorial/zh/parse/\346\226\207\346\234\254\350\257\255\346\263\225\347\273\223\346\236\204\350\247\243\346\236\220.rst"
+++ "b/docs/source/tutorial/zh/parse/\346\226\207\346\234\254\350\257\255\346\263\225\347\273\223\346\236\204\350\247\243\346\236\220.rst"
@@ -2,7 +2,9 @@
 --------------------
 
 本部分主要由EduNLP.SIF.Parse模块实现，主要功能为将文本中的字母、数字等进行提取，将其转换为标准格式。
- 
+
+此模块主要作为 *中间模块* 来对输入的生文本进行解析处理，用户一般不直接调用此模块。
+
 主要流程介绍
 +++++++++++++++
 
@@ -20,20 +22,51 @@
  
 * 使用_is_formula_legal函数，检查latex公式的完整性和可解析性，对于不合法公式报错
 
-Examples：
+输入
+>>>>>>>
+
+类型：str  
+
+内容：题目文本 （text）
 
 ::
 
- >>> text = '生产某种零件的A工厂25名工人的日加工零件数_   _'
- >>> text_parser = Parser(text)
- >>> text_parser.description_list()
- >>> text_parser.text
- >>> '生产某种零件的$A$工厂$25$名工人的日加工零件数$\\SIFBlank$'
+   >>> text1 = '生产某种零件的A工厂25名工人的日加工零件数_   _'
+   >>> text2 = 'X的分布列为(   )'
+   >>> text3 = '① AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．AC的中点为D'
+   >>> text4 = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
 
-详细示范
-+++++++++++++++
+进行解析
+>>>>>>>>>>>>>>>>>>>>
+
+::
+
+   >>> text_parser1 = Parser(text1)
+   >>> text_parser2 = Parser(text2)
+   >>> text_parser3 = Parser(text3)
+   >>> text_parser4 = Parser(text4)
+
+相关描述参数
+>>>>>>>>>>>>
+
+- 尝试转换为标准形式
+
+::
+
+   >>> text_parser1.description_list()
+   >>> print('text_parser1.text:',text_parser1.text)
+   text_parser1.text: 生产某种零件的$A$工厂$25$名工人的日加工零件数$\SIFBlank$
+   >>> text_parser2.description_list()
+   >>> print('text_parser2.text:',text_parser2.text)
+   text_parser2.text: $X$的分布列为$\SIFChoice$
+
+- 判断是否有语法问题
+
+::
 
-.. toctree::
-   :titlesonly:
-   
-   文本语法结构解析的案例 <../../../build/blitz/parse/parse.ipynb>
+   >>> text_parser3.description_list()
+   >>> print('text_parser3.error_flag: ',text_parser3.error_flag)
+   text_parser3.error_flag:  1
+   >>> text_parser4.description_list()
+   >>> print('text_parser4.fomula_illegal_flag: ',text_parser4.fomula_illegal_flag)
+   text_parser4.fomula_illegal_flag:  1
diff --git a/docs/source/tutorial/zh/seg.rst b/docs/source/tutorial/zh/seg.rst
index e1e1c0db..c029da96 100644
--- a/docs/source/tutorial/zh/seg.rst
+++ b/docs/source/tutorial/zh/seg.rst
@@ -14,13 +14,155 @@
 
 2.将输入的item按照元素类型进行切分、分组。
 
-学习路线图
---------------------
+语义成分分解
+------------
+
+由于选择题是以字典的形式给出，故需要将其在保留数据类型关系的情况下转换为文本格式。dict2str4sif函数就是实现此功能的一个模块，该模块可以将选择题形式的item转换为字符格式，并将题干和选项、各选项之间分割开来。
+
+
+基础使用方法
+++++++++++++++++++
+
+::
+
+ >>> item = {
+ ...     "stem": r"若复数$z=1+2 i+i^{3}$，则$|z|=$",
+ ...     "options": ['0', '1', r'$\sqrt{2}$', '2'],
+ ... }
+ >>> dict2str4sif(item) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+
+可选的的额外参数/接口
+++++++++++++++++++++++
+
+1.add_list_no_tag：当此参数为True较False时区别在于是否需要将选项部分的标签计数
+
+::
+
+ >>> dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+ 
+ >>> dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2$\\SIFTag{options_end}$'
+
+2.tag_mode:此参数为选择标签所在位置，delimiter为头尾都加标签，head为仅头部加标签，tail为仅尾部加标签
+
+::
+
+ >>> dict2str4sif(item, tag_mode="head") # doctest: +ELLIPSIS
+ '$\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{options}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2'
+ 
+ >>> dict2str4sif(item, tag_mode="tail") # doctest: +ELLIPSIS
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options}$'
+
+3.key_as_tag:当其为False时则不区分切分标签的类型，而是仅在选项之间加入$\SIFSep$
+
+::
+
+ >>> dict2str4sif(item, key_as_tag=False)
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
+
+结构成分分解
+------------
+
+对切片后的item中的各个元素进行分词，提供深度选项，可以按照需求选择所有地方切分或者在部分标签处切分（比如\SIFSep、\SIFTag处）；对标签添加的位置也可以进行选择，可以在头尾处添加或仅在头或尾处添加。
+
+具有两种模式，一种是linear模式，用于对文本进行处理（使用jieba库进行分词）；一种是ast模式，用于对公式进行解析。
+
+基础分解流程：
+
+- 使用正则匹配方法匹配出各个组成成分
+
+- 对特殊结构的成分进行处理，如将base64编码的图片转为numpy形式
+
+- 将当前元素分类放入各个元素组中
+
+- 按照需求输入相应的参数得到筛选后的结果
+
+基础使用方法
+++++++++++++++++++
+
+::
+
+ >>> test_item = r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$"
+ >>> seg(test_item)
+ >>> ['如图所示，则', '\\bigtriangleup ABC', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+
+可选的的额外参数/接口
+++++++++++++++++++++++
+
+1.describe：可以统计出各种类型元素的数量
+
+::
+
+ >>> s.describe()
+ {'t': 3, 'f': 1, 'g': 1, 'm': 1}
+
+2.filter：可以选择性的筛除某种或几种类型的元素
+
+此接口可传入keep参数来选择需要保留的元素类型，也可直接传入特殊字符来筛除特定元素类型
+
+各字母所代表的元素类型：
+
+-   "t": text
+-   "f": formula
+-   "g": figure
+-   "m": question mark
+-   "a": tag
+-   "s": sep tag
+
+::
+
+ >>> with s.filter("f"):
+ ...     s
+ ['如图所示，则', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+ >>> with s.filter(keep="t"):
+ ...     s
+ ['如图所示，则', '的面积是', '。']
+
+3.symbol:选择性的将部分类型的数据转换为特殊符号遮掩起来
+
+symbol所代表的元素类型：
+
+-   "t": text
+-   "f": formula
+-   "g": figure
+-   "m": question mark
+
+::
+
+ >>> seg(test_item, symbol="fgm")
+ ['如图所示，则', '[FORMULA]', '的面积是', '[MARK]', '。', '[FIGURE]']
+ >>> seg(test_item, symbol="tfgm")
+ ['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']
+
+此外，当前还提供了sif4sci函数，其可以很方便的将item转换为结构成分分解后的结果
+
+::
+
+ >>> segments = sif4sci(item["stem"], figures=figures, tokenization=False)
+ >>> segments
+ ['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\SIFChoice', \FigureID{1}]
+
+- 调用此函数时，可以按照需求选择性的输出某一类型的数据
+
+::
+
+ >>> segments.formula_segments
+ ['ABC',
+ 'BC',
+ 'AB',
+ 'AC',
+ '\\bigtriangleup ABC',
+ 'I',
+ 'II',
+ 'III',
+ 'I,II,III',
+ 'p_1,p_2,p_3']
 
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
+- 与seg函数类似，sif4sci也提供了标记化切分选项通过修改 ``symbol`` 参数来将不同的成分转化成特定标记，方便您的研究
 
-   语义成分分解 <seg/语义成分分解>
-   结构成分分解 <seg/结构成分分解>
+::
 
+ >>> sif4sci(item["stem"], figures=figures, tokenization=False, symbol="tfgm")
+ ['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']
diff --git "a/docs/source/tutorial/zh/seg/\347\273\223\346\236\204\346\210\220\345\210\206\345\210\206\350\247\243.rst" "b/docs/source/tutorial/zh/seg/\347\273\223\346\236\204\346\210\220\345\210\206\345\210\206\350\247\243.rst"
index 13ae96ca..bffe1b64 100644
--- "a/docs/source/tutorial/zh/seg/\347\273\223\346\236\204\346\210\220\345\210\206\345\210\206\350\247\243.rst"
+++ "b/docs/source/tutorial/zh/seg/\347\273\223\346\236\204\346\210\220\345\210\206\345\210\206\350\247\243.rst"
@@ -26,6 +26,16 @@
 
 2.filter：可以选择性的筛除某种或几种类型的元素
 
+此接口可传入keep参数来选择需要保留的元素类型，也可直接传入特殊字符来筛除特定元素类型
+
+各字母所代表的元素类型：
+   "t": text
+   "f": formula
+   "g": figure
+   "m": question mark
+   "a": tag
+   "s": sep tag
+
 ::
 
  >>> with s.filter("f"):
@@ -37,17 +47,15 @@
 
 3.symbol:选择性的将部分类型的数据转换为特殊符号遮掩起来
 
+symbol所代表的元素类型：
+   "t": text
+   "f": formula
+   "g": figure
+   "m": question mark
+
 ::
 
  >>> seg(test_item, symbol="fgm")
  ['如图所示，则', '[FORMULA]', '的面积是', '[MARK]', '。', '[FIGURE]']
  >>> seg(test_item, symbol="tfgm")
  ['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']
-
-详细示范
-+++++++++++
-
-.. toctree::
-   :titlesonly:
-
-   结构成分分解的案例  <../../../build/blitz/seg/seg.ipynb>
diff --git "a/docs/source/tutorial/zh/seg/\350\257\255\344\271\211\346\210\220\345\210\206\345\210\206\350\247\243.rst" "b/docs/source/tutorial/zh/seg/\350\257\255\344\271\211\346\210\220\345\210\206\345\210\206\350\247\243.rst"
index 0950dd87..8c709a89 100644
--- "a/docs/source/tutorial/zh/seg/\350\257\255\344\271\211\346\210\220\345\210\206\345\210\206\350\247\243.rst"
+++ "b/docs/source/tutorial/zh/seg/\350\257\255\344\271\211\346\210\220\345\210\206\345\210\206\350\247\243.rst"
@@ -44,12 +44,4 @@
 ::
 
  >>> dict2str4sif(item, key_as_tag=False)
- '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
-
-详细示范
-++++++++++++++++++++++
-
-.. toctree::
-   :titlesonly:
-
-   语义成分分解的案例  <../../../build/blitz/utils/data.ipynb>
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
\ No newline at end of file
diff --git a/docs/source/tutorial/zh/sif.rst b/docs/source/tutorial/zh/sif.rst
index 0bb9f2ae..038fa1e3 100644
--- a/docs/source/tutorial/zh/sif.rst
+++ b/docs/source/tutorial/zh/sif.rst
@@ -87,6 +87,36 @@ version: 0.2
 
     例如：``则$a$的取值范围是（\u3000\u3000）``
 
+判断是否为sif格式和转换为sif格式的函数
+--------------------------------------------
+
+is_sif
++++++++++++
+
+::
+
+    >>> text1 = '若$x,y$满足约束条件' 
+    >>> text2 = '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' 
+    >>> text3 = '则$z=x+7 y$的最大值$\\SIFUnderline$'
+    >>> text4 = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
+    >>> is_sif(text1)
+    True
+    >>> is_sif(text2)
+    True
+    >>> is_sif(text3)
+    True
+    >>> is_sif(text4)
+    False
+
+to_sif
++++++++++++
+
+::
+
+    >>> text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
+    >>> to_sif(text)
+    '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'
+
 
 Change Log
 ----------------
@@ -106,4 +136,3 @@ Change Log
 1. 注明 ``$$`` 之中不能出现换行符。 
 
 2. 添加文本标注格式说明。 
-
diff --git a/docs/source/tutorial/zh/tokenization/PureTextTokenizer.ipynb b/docs/source/tutorial/zh/tokenization/PureTextTokenizer.rst
similarity index 100%
rename from docs/source/tutorial/zh/tokenization/PureTextTokenizer.ipynb
rename to docs/source/tutorial/zh/tokenization/PureTextTokenizer.rst
diff --git a/docs/source/tutorial/zh/tokenize.rst b/docs/source/tutorial/zh/tokenize.rst
index ce719757..d9c9eeb5 100644
--- a/docs/source/tutorial/zh/tokenize.rst
+++ b/docs/source/tutorial/zh/tokenize.rst
@@ -16,13 +16,156 @@
 
 具有两种模式，一种是linear模式，用于对文本进行处理（使用jieba库进行分词）；一种是ast模式，用于对公式进行解析。
 
-学习路线图
---------------------
+分词
+-------
 
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
+词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和"单字解析"。
+
+::
+
+   - 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。
+   
+   - 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。
+    
+
+词解析分为两个主要步骤：
+
+1. 分词：  
+
+   - 词组解析：使用分词工具切分并提取题目文本中的词。本项目目前支持的分词工具有：`jieba`
+
+   - 单字解析：按字符划分。
+
+2. 筛选：过滤指定的停用词。   
+
+   本项目默认使用的停用词表：`[stopwords] <https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt>`_  
+   你也可以使用自己的停用词表，具体使用方法见下面的示例。
+
+Examples：
+
+::
+    
+   >>> text = "三角函数是基本初等函数之一"
+   >>> tokenize(text, granularity="word")
+   ['三角函数', '初等', '函数']
    
-   分词 <tokenize/分词>
-   分句 <tokenize/分句>
-   令牌化 <tokenize/令牌化>
+   >>> tokenize(text, granularity="char")
+   ['三', '角', '函', '数', '基', '初', '函', '数']
+    
+分句
+-------
+
+将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）（待实现）。
+
+令牌化
+-------
+即综合解析，将带公式的句子切分为若干标记的过程。每个标记为一个“令牌”（token）。
+
+此功能对应的实现函数为tokenize，将已经经过结构成分分解后的item传入其中即可得到所需结果
+
+    
+::
+
+   >>> items = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
+   >>> tokenize(SegmentList(items))
+   ['如图所示', '三角形', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]
+   >>> tokenize(SegmentList(items),formula_params={"method": "ast"})
+   ['如图所示', '三角形', <Formula: ABC>, '面积', '\\\\SIFBlank', \\FigureID{1}]
+
+
+
+我们提供了多种已经封装好的令牌化器供用户便捷调用，通过查看 ``./EduNLP/Tokenizer/tokenizer.py`` 及 ``./EduNLP/Pretrain/gensim_vec.py`` 可以查看更多令牌化器，下面是一个完整的令牌化器列表
+
+- TextTokenizer
+
+- PureTextTokenizer
+
+- GensimSegTokenizer
+
+- GensimWordTokenizer
+
+
+TextTokenizer
++++++++++++++++++++++
+
+即文本令牌解析器，在默认情况下对传入的item中的图片、标签、分隔符、题目空缺符等部分则转换成特殊字符进行保护，从而对文本、公式进行令牌化操作。此外，此令牌解析器对文本、公式均采用线性的分析方法，并提供的key参数用于对传入的item进行预处理，待未来根据需求进行开发。
+
+::
+
+   >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+   >>> tokenizer = TextTokenizer()
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+   >>> items = [{
+   ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+   ... "options": ["1", "2"]
+   ... }]
+   >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+
+PureTextTokenizer
++++++++++++++++++++++
+
+即纯净型文本令牌解析器，在默认情况下对传入的item中的图片、标签、分隔符、题目空缺符等部分则转换成特殊字符进行保护，并对特殊公式(例如：$\\FormFigureID{...}$， $\\FormFigureBase64{...}$)进行筛除，从而对文本、纯文本公式进行令牌化操作。此外，此令牌解析器对文本、公式均采用线性的分析方法，并提供的key参数用于对传入的item进行预处理，待未来根据需求进行开发。
+
+
+::
+
+   >>> tokenizer = PureTextTokenizer()
+   >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)[:10]
+   ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
+   >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+   >>> items = [{
+   ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+   ... "options": ["1", "2"]
+   ... }]
+   >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+
+GensimSegTokenizer
+++++++++++++++++++++
+
+此令牌解析器在默认情况下对传入的item中的图片、分隔符、题目空缺符等部分则转换成特殊字符进行保护，从而对文本、公式、标签进行令牌化操作。此外，从令牌化方法而言，此令牌解析器对文本均采用线性的分析方法，而对公式采用抽象语法树的分析方法。
+
+与GensimWordTokenizer相比，GensimSegTokenizer解析器主要区别是：
+
+* 提供了切分深度的选项，即可以在sep标签或者tag标签处进行切割
+* 默认在item组分（如text、formula）的头部插入开始标签
+
+GensimWordTokenizer
++++++++++++++++++++++++
+
+此令牌解析器在默认情况下对传入的item中的图片、题目空缺符等部分转换成特殊字符进行保护，从而对文本、公式、标签、分隔符进行令牌化操作。此外，从令牌化方法而言，此令牌解析器对文本均采用线性的分析方法，而对公式采用抽象语法树的分析方法，提供了general参数可供使用者选择：当general为true的时候则代表着传入的item并非标准格式，此时对公式也使用线性的分析方法；当general为false时则代表使用抽象语法树的方法对公式进行解析。
+
+Examples
+----------
+        
+::
+
+   >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+   >>> tokenizer = GensimWordTokenizer(symbol="fgmas", general=False)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', '[FORMULA]', '约束条件', '公式', '[FORMULA]', '[SEP]', '[FORMULA]']
diff --git a/examples/formula/formula.ipynb b/examples/formula/formula.ipynb
index f748a90a..a9626563 100644
--- a/examples/formula/formula.ipynb
+++ b/examples/formula/formula.ipynb
@@ -3,13 +3,13 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# Formula\n",
-    "\n",
-    "## 概述\n",
-    "\n",
-    "Formula 首先在分词功能中对原始文本的公式做切分处理，另外提供 [公式解析树] 功能，可以将数学公式的抽象语法分析树用文本或图片的形式表示出来。  \n",
-    "\n",
-    "本模块另提供公式变量标准化的功能，如判断几个子公式内的‘x’为同一变量。"
+    "# Formula\r\n",
+    "\r\n",
+    "## 概述\r\n",
+    "\r\n",
+    "Formula 首先在分词功能中对原始文本的公式做切分处理，另外提供多种功能使之能够适应多种用户需求，例如 [公式解析树] 功能，可以将数学公式的抽象语法分析树用文本或图片的形式表示出来；又如[公式变量标准化]的功能，能判断几个子公式内的‘x’为同一变量。\r\n",
+    "\r\n",
+    "由于本部分常作为中间模块，故仅展示基本调用方法，如需更进一步学习模块相关参数请参见对应文档。"
    ],
    "metadata": {}
   },
@@ -17,9 +17,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from EduNLP.Formula import Formula\n",
-    "from EduNLP.Formula import FormulaGroup\n",
+    "import matplotlib.pyplot as plt\r\n",
+    "from EduNLP.Formula import Formula\r\n",
+    "from EduNLP.Formula import FormulaGroup\r\n",
     "from EduNLP.Formula.viz import ForestPlotter"
    ],
    "outputs": [],
@@ -41,7 +41,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "source": [
-    "f = Formula(\"x^2 + x+1 = y\")\n",
+    "f = Formula(\"x^2 + x+1 = y\")\r\n",
     "f "
    ],
    "outputs": [
@@ -60,188 +60,6 @@
     "collapsed": true
    }
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 查看公式切分后的结点元素:"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "source": [
-    "f.elements"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[{'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       " {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
-       " {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
-       " {'id': 3, 'type': 'bin', 'text': '+', 'role': None},\n",
-       " {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},\n",
-       " {'id': 5, 'type': 'bin', 'text': '+', 'role': None},\n",
-       " {'id': 6, 'type': 'textord', 'text': '1', 'role': None},\n",
-       " {'id': 7, 'type': 'rel', 'text': '=', 'role': None},\n",
-       " {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 3
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 查看公式的抽象语法分析树："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "f.ast "
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[{'val': {'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       "  'structure': {'bro': [None, 3],\n",
-       "   'child': [1, 2],\n",
-       "   'father': None,\n",
-       "   'forest': None}},\n",
-       " {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
-       "  'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},\n",
-       " {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
-       "  'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},\n",
-       " {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},\n",
-       "  'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},\n",
-       " {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},\n",
-       "  'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},\n",
-       " {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},\n",
-       "  'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},\n",
-       " {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},\n",
-       "  'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},\n",
-       " {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},\n",
-       "  'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},\n",
-       " {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},\n",
-       "  'structure': {'bro': [7, None],\n",
-       "   'child': None,\n",
-       "   'father': None,\n",
-       "   'forest': None}}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 4
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "print('nodes: ',f.ast_graph.nodes)\n",
-    "print('edges: ' ,f.ast_graph.edges)\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
-      "edges:  [(0, 1), (0, 2)]\n"
-     ]
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "source": [
-    "ForestPlotter().export(\n",
-    "    f.ast_graph, root_list=[node[\"val\"][\"id\"] for node in f.ast if node[\"structure\"][\"father\"] is None],\n",
-    ")\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 变量标准化\n",
-    "\n",
-    "下面这个例子中，`var` 为变量编号。同一变量拥有相同的变量编号。  \n",
-    "如：`x` 变量的编号为 `0`， `y` 变量的编号为 `1`。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "f.variable_standardization().elements"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[{'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       " {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},\n",
-       " {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
-       " {'id': 3, 'type': 'bin', 'text': '+', 'role': None},\n",
-       " {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},\n",
-       " {'id': 5, 'type': 'bin', 'text': '+', 'role': None},\n",
-       " {'id': 6, 'type': 'textord', 'text': '1', 'role': None},\n",
-       " {'id': 7, 'type': 'rel', 'text': '=', 'role': None},\n",
-       " {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 20
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -255,11 +73,11 @@
    "cell_type": "code",
    "execution_count": 21,
    "source": [
-    "fs = FormulaGroup([\n",
-    "    \"x^2 = y\",\n",
-    "    \"x^3 = y^2\",\n",
-    "    \"x + y = \\pi\"\n",
-    "])\n",
+    "fs = FormulaGroup([\r\n",
+    "    \"x^2 = y\",\r\n",
+    "    \"x^3 = y^2\",\r\n",
+    "    \"x + y = \\pi\"\r\n",
+    "])\r\n",
     "fs"
    ],
    "outputs": [
@@ -280,209 +98,6 @@
      "name": "#%%\n"
     }
    }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "source": [
-    "fs.elements"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[{'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       " {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
-       " {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
-       " {'id': 3, 'type': 'rel', 'text': '=', 'role': None},\n",
-       " {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},\n",
-       " {'id': 5, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       " {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
-       " {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},\n",
-       " {'id': 8, 'type': 'rel', 'text': '=', 'role': None},\n",
-       " {'id': 9, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       " {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},\n",
-       " {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
-       " {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},\n",
-       " {'id': 13, 'type': 'bin', 'text': '+', 'role': None},\n",
-       " {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},\n",
-       " {'id': 15, 'type': 'rel', 'text': '=', 'role': None},\n",
-       " {'id': 16, 'type': 'mathord', 'text': '\\\\pi', 'role': None}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 22
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "source": [
-    "fs.ast"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[{'val': {'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       "  'structure': {'bro': [None, 3],\n",
-       "   'child': [1, 2],\n",
-       "   'father': None,\n",
-       "   'forest': None}},\n",
-       " {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
-       "  'structure': {'bro': [None, 2],\n",
-       "   'child': None,\n",
-       "   'father': 0,\n",
-       "   'forest': [6, 12]}},\n",
-       " {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
-       "  'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},\n",
-       " {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},\n",
-       "  'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},\n",
-       " {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},\n",
-       "  'structure': {'bro': [3, None],\n",
-       "   'child': None,\n",
-       "   'father': None,\n",
-       "   'forest': [10, 14]}},\n",
-       " {'val': {'id': 5, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       "  'structure': {'bro': [None, 8],\n",
-       "   'child': [6, 7],\n",
-       "   'father': None,\n",
-       "   'forest': None}},\n",
-       " {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},\n",
-       "  'structure': {'bro': [None, 7],\n",
-       "   'child': None,\n",
-       "   'father': 5,\n",
-       "   'forest': [1, 12]}},\n",
-       " {'val': {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},\n",
-       "  'structure': {'bro': [6, None], 'child': None, 'father': 5, 'forest': None}},\n",
-       " {'val': {'id': 8, 'type': 'rel', 'text': '=', 'role': None},\n",
-       "  'structure': {'bro': [5, 9], 'child': None, 'father': None, 'forest': None}},\n",
-       " {'val': {'id': 9, 'type': 'supsub', 'text': '\\\\supsub', 'role': None},\n",
-       "  'structure': {'bro': [8, None],\n",
-       "   'child': [10, 11],\n",
-       "   'father': None,\n",
-       "   'forest': None}},\n",
-       " {'val': {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},\n",
-       "  'structure': {'bro': [None, 11],\n",
-       "   'child': None,\n",
-       "   'father': 9,\n",
-       "   'forest': [4, 14]}},\n",
-       " {'val': {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},\n",
-       "  'structure': {'bro': [10, None],\n",
-       "   'child': None,\n",
-       "   'father': 9,\n",
-       "   'forest': None}},\n",
-       " {'val': {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},\n",
-       "  'structure': {'bro': [None, 13],\n",
-       "   'child': None,\n",
-       "   'father': None,\n",
-       "   'forest': [1, 6]}},\n",
-       " {'val': {'id': 13, 'type': 'bin', 'text': '+', 'role': None},\n",
-       "  'structure': {'bro': [12, 14],\n",
-       "   'child': None,\n",
-       "   'father': None,\n",
-       "   'forest': None}},\n",
-       " {'val': {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},\n",
-       "  'structure': {'bro': [13, 15],\n",
-       "   'child': None,\n",
-       "   'father': None,\n",
-       "   'forest': [4, 10]}},\n",
-       " {'val': {'id': 15, 'type': 'rel', 'text': '=', 'role': None},\n",
-       "  'structure': {'bro': [14, 16],\n",
-       "   'child': None,\n",
-       "   'father': None,\n",
-       "   'forest': None}},\n",
-       " {'val': {'id': 16, 'type': 'mathord', 'text': '\\\\pi', 'role': None},\n",
-       "  'structure': {'bro': [15, None],\n",
-       "   'child': None,\n",
-       "   'father': None,\n",
-       "   'forest': None}}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 23
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "source": [
-    "ForestPlotter().export(\n",
-    "    fs.ast_graph, root_list=[node[\"val\"][\"id\"] for node in fs.ast if node[\"structure\"][\"father\"] is None],\n",
-    ")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[Text(22.32, 181.2, 'id: 0\\ntype: supsub\\ntext: \\\\supsub\\nrole: None'),\n",
-       " Text(11.16, 108.72, 'id: 1\\ntype: mathord\\ntext: x\\nrole: base'),\n",
-       " Text(33.480000000000004, 108.72, 'id: 2\\ntype: textord\\ntext: 2\\nrole: sup'),\n",
-       " Text(55.8, 181.2, 'id: 3\\ntype: rel\\ntext: =\\nrole: None'),\n",
-       " Text(78.12, 181.2, 'id: 4\\ntype: mathord\\ntext: y\\nrole: None'),\n",
-       " Text(111.6, 181.2, 'id: 5\\ntype: supsub\\ntext: \\\\supsub\\nrole: None'),\n",
-       " Text(100.44, 108.72, 'id: 6\\ntype: mathord\\ntext: x\\nrole: base'),\n",
-       " Text(122.76, 108.72, 'id: 7\\ntype: textord\\ntext: 3\\nrole: sup'),\n",
-       " Text(145.08, 181.2, 'id: 8\\ntype: rel\\ntext: =\\nrole: None'),\n",
-       " Text(178.56, 181.2, 'id: 9\\ntype: supsub\\ntext: \\\\supsub\\nrole: None'),\n",
-       " Text(167.4, 108.72, 'id: 10\\ntype: mathord\\ntext: y\\nrole: base'),\n",
-       " Text(189.72, 108.72, 'id: 11\\ntype: textord\\ntext: 2\\nrole: sup'),\n",
-       " Text(212.04, 181.2, 'id: 12\\ntype: mathord\\ntext: x\\nrole: None'),\n",
-       " Text(234.36, 181.2, 'id: 13\\ntype: bin\\ntext: +\\nrole: None'),\n",
-       " Text(256.68, 181.2, 'id: 14\\ntype: mathord\\ntext: y\\nrole: None'),\n",
-       " Text(279.0, 181.2, 'id: 15\\ntype: rel\\ntext: =\\nrole: None'),\n",
-       " Text(301.32, 181.2, 'id: 16\\ntype: mathord\\ntext: \\\\pi\\nrole: None')]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 25
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAU1klEQVR4nO3dWW+c133H8d95ZuNwJyVZFLVYtixLju3Ure0UKeJe1AHaAgZStEDQi170BfS2yAvoReEG6HLRl5CroldxgCBwbbR20sULHDu2KTm0ZUkWqZWSOFxmPb2YkTgcDWd7znPmmWe+H8CwuJ3nP3+e+fHZ5oyx1goA4Ecw7AIAYJwQugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgEaELAB4RugDgk7XW2X8mnV2XZF3+Z9LZ9WHV07ztKB7bII8xaT0Os/049CKqeeF67vmYy1HVHJfnnrOctNbKFWOMffxHbzgbT5K+fv01WWvNMOpp3nYUj63ddrpJWo/DbD8OvYhqXrieez7mclQ1x+W554q30wuVzVuSpJ1LH0mSSje+0uavf+Fr8wfWUdm8rc2Pfu58XEm69z//FnrcsHXsrL6v4rULQ62h+M1KLH7XO19+oO0Lvxra9quFDRU+fVulm5e0tfKu83HLG9dU+PTtSGq2tqZ7//vvzsfduvBLlW5ecj5u8dqFfc/FOEn72lDp+pfaWX1fQX5GteKWqtv3lJlf8rX5A+tITS/KVkrOx63ubCo1teig4nB1BJOzTh5fmBokK1vc8lpDuzpSM4e1s/p/w9v+9IJSUwvKHjmt8p1vnI+bml6ULRcjqbm0vqrMoZPOxzWpdKj5edC4lbvrCnJTA48bJc8X0vZOZaQmZ1W+u+53823qqG7eVJCbdD5ubee+qlt3ZKsVR2MPVkeQm1J18/ZQa7BWMpncEGrYX0eQyys17fsPYdOc2C2otHZRWyvvyqSzzset3r/laC4/OratFFW+fcX5uEFu0sH8fHTc9OJxVTZvhhw3Gt72dCef+s6+j7OPPansY0/62vyBdQS5KU0//33n4+aOPa3csadDjxu2jiA3pczi8aHWMHHiGU2ceMZrDe3qCHJTmn7u1aFtX5LmvvvDSMfNHDoR2dgTJ5+LZNwwOdBp3NzSUwOPGyXne7ou9l5dn4vpp6ZO2x7ksZXvrrcdc/fyx7K1at/jhamllcs+u+px1Ns+SJiafM2LbtvZu05xq6ft9yuKmpuNynMvLOd7ujur76myeFwygco3vlTQOL8y/dyrSs0e0fbFXymzsKzq9j1V7l2XLW7LZPPKHDrx8FC8ePUzBblJZ3uKXWv6/L+Umj2i0vpvlTl8avBxVt5RtbCh1Mwh1XYLSs0cUrWwIVvalmrV+uGZMcounVXx6ufKnXg2usfUR59dcNXjSLYdcS98zYtu26nt3FfhN28pNb2orU/+QxOPf1u54+2PMOJS8yg+98JyHrqpyXnJBDKpvaHTc489/HdmfknlO9+otluQrRQlWU2cel7Fq59JQSCTytS/0eGtbN1qCibnpFql66FZ13Hyswrys/UPalXVdjYVZHIK5h6TalVJRiadc3A+L359dtXjKLYddS98zYtu22k8CElSdvmcah0uqsWl5lF87oXlPHSnnnlFO6vvK3v8vGy1ovzpF/Z9PXv0jLJHz0hSfa+jVlVmYTnS847damr9OKpx8k+++PDfYc83xa3Prnocxbaj7oWvedFtO1PP/OHI1TyKz72wIrmQlj/zUv3/XRqRnjsaxebb6rUmX+O4ELc+D7M3w+6Fr8fucjujVnOcnnthRH4hrbZbUHVnU5JUvHahp1uooripuVNdcR673+0ltceDbHOYvZCi60e3cWvF7UjGHdQwxm3+3fdyYdGnSC6kFSemFWQnVd26o9zyOVW37ys9v6TMkdMqrl1U+dZlpRsvjKhsXFNq5rDKNy8pu/SUZAJVN29pa+VdTZ3/npe60rNHtHvpIwX5WeWOnXU6tqxV+fYV5ZbOKjW9EPljSWqPB9nmMHvRrbbU5Jw2P/ipZl76gYI+72Hu1ufCr3+u3Mnn++5zVPM4qnnR7XdfunlJxcufyGTzmnnhT/oaO0rO93RTk/MyQUqpmUNSrVb/q9u4QFGfXFa14pZspaTKxrX6D9maFASytarKN75yXVLXuuolVOt1OB47s3BMpbUvnAVut+0ltceDbHOYvehWm62W92pxOK4JUsounxvoD1tU8ziqedHtd//gomncRHIh7YF2J6wnTjyriabbNXYufbT/HM2Zl12X1FNd7W6ydjF2rVxUbtntiyTGsceDbnNYveiltkFfKNHLY3Y9bph5HNW86DZueuawUmcXlFlYHmj8qHh5GXDrObO77/5E26vvSfJ7UrxTHVGNG2Ryykf4xO6ljiT02FUdSehFlD0+aOyw83gYvUjPHY1d4EoRvgx488OfyWQnlMrX11gorV3U5PlXlFk4Vr9B+fInMiZQrbyr6v2byhw+peKVTzX9O3/c5t7D4dSR9Mfnqo441BCXOqKqIcrHNmo1x+H3HEZkoZtePC5b3pVkZCtFpRePS6a+bKVJZ6VUWjJGRkaZI6elWlWpmUN7N6rHvI6kP75RqyEudYzifBu1muPwew4jstBtPox7cH9d89d8HeZFVUfSH9+o1RCXOkZxvo1azXH4PYfBe6QBgE9O3/snBu9Z5bIe3iON90gbRg1RzD3eI8393B/4OWOb7peLmjHmY0l/ba390BhzXtIb1tp4Lno5oowxP5Z0x1r7942Pv5b0R9ba1eFW5p8x5l8lXbTW/kvj45uSvm2tXRtuZRhn3k4vGGNSkp6SdLHxqVVJJ4wxw3pLgaQ6J2ml6eOVxufGEb1A7Pg8p3tK0m1rbUGSrLVlSV+rHsRw57yk5neivND43DiiF4gdn6F7TvufAGp8zJ6HI8aYrOp/3JpPJYxlj40x05IWJV1u+vRY9gLx4jt0V1o+x+GeW09KumKtbV69elx7/LSkL6zd96L+ce0FYsRn6LYe6kkc7rlGj/fQC8QSe7rJ0q7H30iaMsbM+y9nqNr14itJy8aYiSHUA0iKyZ6uMY3X8CGsR3ps6/cEXtT4/XFr14uy6sHrbkFfoE9eQtcYMytpTtLV5s9ba29Jqkga/ioUydBu704azyMKeoFY8rWne071m9TbrVTMeTYHGkcL7Y4mpDHrsTEmUP1C2sU2Xx6rXiB+fIZuuzCQuI3HlcOSjKSbbb42bj0+KWnDWtvujbjGrReIGZ+h2+5QT+Jwz5VzklZs+9d1j1uPmW+ILV+he9Bhr8ThniudevyFpDONl2KPg67zjYu3GBb2dJPjwB5ba7clXZd02mdBQ9SpF3ck7Upa8loR0BB56Db2rs6q/UUNSfpSLHzjQqe9O2m8jijoBWLLx57uKUm3rLVb7b7IwjfOdLpYKY3XBSR6gdjyEbqdTi08wCmGEA5Y6KbVWPTYGDOjRxe6aTUWvUA8+Qjdbod6Eod7YZ3RowvdtBqXHrdb6KbVuPQCMcSebjLQ4z30ArHGnm4y9NLjaxqPhW966cWDhW/yHuoB9onVni73Tg6sa48bL5oYhwtIvfSiovpdM1y8hXeRhm5joZtZ1ZcXPJC19rbqC98cjbKeBOtl704ajyMKeoFYi3pP95y6X9R4YBz2wpxrHB10u0XqgUT3uLHQTad7wpsluheIr6hD97y6n1p4gIsbgznc+H+7hW5arSjZe3cnVX/7+XYL3bRivmEofOzp9rIHJnG4N6jzki4csNBNq6Tv3fV6akFivmFIfIQue7rR6qfHX0h60hiTjrCeYeqnFxfExVsMgY/TC+x5RKvnHo/Bwjf99OLBwjfHIq0IaBFZ6DYWunlKvV3UkOq38Bxn4Zu+9XMKR0r2EUW/vUj66RbEUJR7uo9LunnQQjetGgvfXBL3Tvarn0NqKdlHFP32Isl/gBBTUYZuv08AKdmB4FyPC920SmTQNC10c6WPH2O+wbsoQ7ef87kPJDIQInRG0mVrbamPn0lq0Dytg9/89CDMN3jHnu5o4w/bnkF6wXyDd3Hb0+XCRn/6vXAkSWuS8saYhQjqGaZBevGVpGMsfAOfot7THWgvjHsne9b30UTjRRQXlbw/boP0goVv4F0koWuMmZM0oy4L3bRi4Zu+DXI0ISXzFMOgveAUA7yKak/3nPq/qPFAEgPBuaaFbvo9by4lLGiaFrrhDxBiL8rQHSQMpIQFQoSONP5/a4CfTVrQnFJ9oZvCAD/LfINXUYXuoId6UvICISrnJK30uNBNq6QFTZg/8sw3eMWe7ugK84ftC0lPJGjhmzC9YOEbeBXHPV1uG+vNIHeHSJKstTuS1iU94bSi4QnTiw2x8A08ch66jYVuzqj3hW5aPVj4ZsJdVYkU5mhCStZhNb3AyIhiT/e0pBuNZQT7xsI3PQtzNCEl6zQOvcDIiCJ0Bz7Ua8KeRweN5S9Pqr+Fblol4jROY6GbefW30E0r5hu8iSp0wxzqSex5dDPIQjetkhI0/bz56UGYb/AmitANe6gnJWQvLEIujiaSEjSuesF8gxdOQ9cYMynpWbl5EnzLGDMdvqpkadza9JLCH02sSZowxnwrfFXD0Xgl2osKP9++krRkjDkduiigC9d7uq9J+gNJPww5zp9L+j1JfxG6ouSZlfS3Ct/j70nKS/rnsAUN0VFJfyPpL0OO86qkjKR/DF0R0IXr0H1L0o6kvws5zo8lbUv6z9AVJYy19p6ka5L+KeRQv1T9Qty7oYsaEmvtmqSbkv4h5FBvqn4h7p3QRQFdmMFeRQoAGETUb8EOAGhC6AKARx0XPAkyuXVbKUWyoLhJZWu2WnJ790Q6e71WLi65HDNqkfY4nb0uSS7Hj7LH9ALjoOM5XWOMffxHb0Sy4a9ff02ux/769ddkrR2p1aKi7rEkp32Ossf0AuMg1J5mZbO+fvbOpY8kSdXChgqfvh26qHZjlzfWVPjNW07GHjWP9OL2VW2thL/poHXcyuZt3X3nJ6HHjcpB82336mfa+nzwGw8OGre8cU3bF/87VM1Aq1DrqZauf6md1fcV5GdUK24pNb2g1JSbN5ltHTuzcEyl62GWGhhdrb0o37kqE4Q/M9M6bnrmkLLHzjqoOBoHzTdbKSmYnHU+bmZhWZV7Nxw+AsDJhbS90xO13YJKa4Ou6Nh57N2rn6pWHGjhsoTY60V6YVmV+7dka1Wn41prFf+1vB+dbyadVW37vvNxK5u3Vb55KeS4wH6h9nQnn/rOI5+b+27YF0q1H3vixLOaOPGsk7FHTWsvsrkpZQ+fcj6uJOXPvBx63KhENd86jTv78p+FHh9oxi1jAOBR19At313ve9Dy3fWHFyWa7V7+eN8hcbex23293efabWvU0OeDt9vLz/TSh17Gj1MvkExdTy/srL6nyuJxyQQq3/hSwdSCKnfXNf3cq0rNHtH2yjuqFjaUmjmk2m5BqZlDqhY2ZEvbUq2q8u0rkjHKLp1V8ernyjWdIug2dvHqZ6oW7qi0/ltlFpYlY1S5u6bSxLRstSKTyqhW2lF6fvRvlaTP0fdh1HqBZOoauqnJeckEMqm9b03PPfbw30F+VkG+ceW4VlVtZ1NBJqdg7jGpVpVkZNI5mXS277FTUwuqFu4oc+iEbKUsEwQKJmYa33dUQX5GxW8+7/MhxxN97q3WMH3oZfw49QLJ1DV0p555RTur7yt7/LxstaL86Rf2fb3141b5J198+O/c0v63Pes69hO/2608ZY+c7vo9o4A+10XZh57Gj1EvkEw9XUjLn3lJQWZi3wRtPc9V2y2ourPZdwGtY3cat1bc1uavf6Facavv7YyCfnoRZuxO49pqRYVP3lRl8/ZA23EhyvnWOv44zzcMx8C3jO2svqfixLSC7KSqW3eUWz6n6vZ9peeXlJqc0+YHP9XMSz9QkMk5Gzczv6T09IIUpAYte6QMq8cmlXHy4guXOtUsa1W+fUW5pbNKTff34hzmG3wb+JmVmpyXCVJKzRySarX6Cxca6zjYarn+TQO8V2CncWu7BRWvXZBqYd6DcHQMpcflXSlI7Y0fE51qziwcU2nti74Dt9u44zbf4MfAe7pTz7zy8N/tzp0NetN6t3HnX/mrgcYdRcPq8dT57w00bpQ61VwrF5Vbftr5uNJ4zTf4Ea9jSGAAQSYX61fSAc0GDt3Wm8Nd3yzOzed1UfZ51HpML5AEfZ1e2PzwZzLZCaXysyrfXVdp7aImz7+izMIxVTdvaWvlXVXv31B2+bzKty7LZHIKspOaPPv7occvfPKmJk49L1spqbq1ofKtK5p6/lUFmYmBHnicRdnnUesxvUDS9LWnm148riA3JcnIVopKLx6XWlalCiamVdu6q1pxSyaVqV+gcDB+en5JpRtfaffKp43vTu4bakbZ51HrMb1A0vS1p9t832T+zEv7vjb9/PdDF9Np/ImTzz389+7ljzX9wp/KJPRWnij7PGo9phdImo5v18N7pEWP9wXbQy8wDjqGLgDALW4ZAwCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8IjQBQCPCF0A8Oj/AWdihSPDqhKLAAAAAElFTkSuQmCC"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "source": [
-    "for ft in fs.variable_standardization():\n",
-    "    print(ft.elements)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "[{'id': 0, 'type': 'supsub', 'text': '\\\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]\n",
-      "[{'id': 5, 'type': 'supsub', 'text': '\\\\supsub', 'role': None}, {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'}, {'id': 8, 'type': 'rel', 'text': '=', 'role': None}, {'id': 9, 'type': 'supsub', 'text': '\\\\supsub', 'role': None}, {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base', 'var': 1}, {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'}]\n",
-      "[{'id': 12, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, {'id': 13, 'type': 'bin', 'text': '+', 'role': None}, {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, {'id': 15, 'type': 'rel', 'text': '=', 'role': None}, {'id': 16, 'type': 'mathord', 'text': '\\\\pi', 'role': None}]\n"
-     ]
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
   }
  ],
  "metadata": {
diff --git a/examples/sif/sif.ipynb b/examples/sif/sif.ipynb
index 2076e126..3ba5b7d3 100644
--- a/examples/sif/sif.ipynb
+++ b/examples/sif/sif.ipynb
@@ -3,1003 +3,156 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# SIF4Sci 使用示例\n",
-    "\n",
-    "## 概述\n",
-    "\n",
-    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
-   ],
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
-    "\n",
-    "![Figure](../../asset/_static/item.png)"
+    "# 标准项目格式\r\n",
+    "\r\n",
+    "version: 0.2\r\n",
+    "\r\n",
+    "为了后续研究和使用的方便，我们需要一个统一的试题语法标准。\r\n",
+    "\r\n",
+    "## 语法规则\r\n",
+    "1. 题目文本中只允许出现中文字符、中英文标点和换行符。\r\n",
+    "2. 使用 \\$\\SIFBlank\\$ 替换横线，对于选择题中的括号使用 \\$\\SIFChoice\\$ 替换。\r\n",
+    "3. 图片 ID 以公式的形式嵌入文本中：`$\\FigureID{ uuid }$` 或用 base64 编码表示，特别的，内容为公式的图片用`$\\FormFigureID{ uuid }$`表示。\r\n",
+    "4. 文本标注格式：统一用 `$\\textf{item,CHAR_EN}$` 表示，目前定义的有：b-加粗，i-斜体，u-下划线，w-下划波浪线，d-加点，t-标题。标注可以混用，按字母顺序排序，例如：$\\textf{EduNLP, biu}$ 表示 <u>***EduNLP***</u>\r\n",
+    "5. 其余诸如，英文字母、罗马字符、数字等数学符号一律需要使用 latex 格式表示，即嵌在 `$$` 之中。\r\n",
+    "6. 分子式的录入标准暂且参考 [INCHI](https://zh.wikipedia.org/wiki/%E5%9B%BD%E9%99%85%E5%8C%96%E5%90%88%E7%89%A9%E6%A0%87%E8%AF%86)\r\n",
+    "7. 目前对 latex 内部语法没有要求。\r\n",
+    "\r\n",
+    "## 示例\r\n",
+    "\r\n",
+    "标准形式:\r\n",
+    "\r\n",
+    "1. `若$x,y$满足约束条件$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，则$z=x+7 y$的最大值$\\\\SIFUnderline$'`\r\n",
+    "\r\n",
+    "2. `已知函数$f(x)=|3 x+1|-2|x|$画出$y=f(x)$的图像求不等式$f(x)>f(x+1)$的解集$\\\\PictureID{3bf2ddf4-8af1-11eb-b750-b46bfc50aa29}$$\\\\PictureID{59b8bd14-8af1-11eb-93a5-b46bfc50aa29}$$\\\\PictureID{63118b3a-8b75-11eb-a5c0-b46bfc50aa29}$$\\\\PictureID{6a006179-8b76-11eb-b386-b46bfc50aa29}$$\\\\PictureID{088f15eb-8b7c-11eb-a86f-b46bfc50aa29}$`\r\n",
+    "\r\n",
+    "非标准形式：\r\n",
+    "\r\n",
+    "1. 字母、数字和数学符号连续混合出现：\r\n",
+    "    例如：\r\n",
+    "    `完成下面的2x2列联表，`\r\n",
+    "    `（单位：m3）`\r\n",
+    "    `则输出的n=`\r\n",
+    "    \r\n",
+    "2. 特殊的数学符号没有用 latex 公式表示：\r\n",
+    "    例如：\r\n",
+    "    `命题中真命题的序号是 ①`\r\n",
+    "    `AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．若D为AC的中点`\r\n",
+    "    \r\n",
+    "3. 出现以 unicode 编码写成的字符\r\n",
+    "    例如：`则$a$的取值范围是（\\u3000\\u3000）`\r\n"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
+    "## 此外提供了判断是否为sif格式和转换为sif格式的函数"
    ],
    "metadata": {}
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "item = {\n",
-    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\n",
-    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\n",
-    "}\n",
-    "item[\"stem\"]"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 5
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
   {
    "cell_type": "markdown",
    "source": [
-    "- 加载图片：`$\\\\FigureID{1}$`"
+    "## 导入函数"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "source": [
-    "from PIL import Image\n",
-    "img = Image.open(\"../../asset/_static/item_figure.png\")\n",
-    "figures = {\"1\": img}\n",
-    "img"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "execution_count": 6
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 导入模块"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "source": [
-    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
+    "from EduNLP.SIF import is_sif, to_sif,sif4sci"
    ],
    "outputs": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "## 验证题目格式"
+    "## is_sif"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "source": [
-    "is_sif(item['stem'])"
+    "text = '若$x,y$满足约束条件' \\\r\n",
+    "    '$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，' \\\r\n",
+    "'则$z=x+7 y$的最大值$\\\\SIFUnderline$'\r\n",
+    "    \r\n",
+    "is_sif(text)\r\n"
    ],
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "True"
       ]
      },
      "metadata": {},
-     "execution_count": 7
+     "output_type": "display_data"
     }
    ],
    "metadata": {}
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
     "is_sif(text)"
    ],
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "False"
       ]
      },
      "metadata": {},
-     "execution_count": 8
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
-    "to_sif(text)\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 9
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 题目切分及令牌化\n",
-    "\n",
-    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 题目切分"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 基本切分\n",
-    "分离文本、公式、图片和特殊符号。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\n",
-    "segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 12
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 文本部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "segments.text_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
-       " '的斜边',\n",
-       " ', 直角边',\n",
-       " ', ',\n",
-       " '.',\n",
-       " '的三边所围成的区域记为',\n",
-       " ',黑色部分记为',\n",
-       " ', 其余部分记为',\n",
-       " '.在整个图形中随机取一点，此点取自',\n",
-       " '的概率分别记为',\n",
-       " ',则']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 13
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 公式部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "source": [
-    "segments.formula_segments\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I,II,III',\n",
-       " 'p_1,p_2,p_3']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 15
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 图片部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "source": [
-    "segments.figure_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[\\FigureID{1}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 16
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "source": [
-    "segments.figure_segments[0].figure"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "execution_count": 17
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 特殊符号"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "source": [
-    "segments.ques_mark_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\SIFChoice']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 19
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 标记化切分  \n",
-    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    " - symbol:\n",
-    "    - \"t\": text\n",
-    "    - \"f\": formula\n",
-    "    - \"g\": figure\n",
-    "    - \"m\": question mark"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 11
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 令牌化\n",
-    "\n",
-    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
-    "\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
-   ],
-   "outputs": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 文本解析结果"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "tokens.text_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图',\n",
-       " '古希腊',\n",
-       " '数学家',\n",
-       " '希波',\n",
-       " '克拉底',\n",
-       " '研究',\n",
-       " '几何图形',\n",
-       " '此图',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '直径',\n",
-       " '直角三角形',\n",
-       " '斜边',\n",
-       " '直角',\n",
-       " '三边',\n",
-       " '围成',\n",
-       " '区域',\n",
-       " '记',\n",
-       " '黑色',\n",
-       " '记',\n",
-       " '其余部分',\n",
-       " '记',\n",
-       " '图形',\n",
-       " '中',\n",
-       " '随机',\n",
-       " '取',\n",
-       " '一点',\n",
-       " '此点',\n",
-       " '取自',\n",
-       " '概率',\n",
-       " '记']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 12
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 公式解析结果"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "tokens.formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 13
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 自定义参数，得到定制化解析结果"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "source": [
-    "sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\": {\n",
-    "            \"method\": \"linear\",\n",
-    "        }\n",
-    "    }\n",
-    ").formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 37
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
-    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
-    "> 因此，ast 可以看做是公式的语法结构表征。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "source": [
-    "sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\":{\n",
-    "            \"method\": \"ast\",\n",
-    "        }\n",
-    "    }\n",
-    ").formula_tokens\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[<Formula: ABC>,\n",
-       " <Formula: BC>,\n",
-       " <Formula: AB>,\n",
-       " <Formula: AC>,\n",
-       " <Formula: \\bigtriangleup ABC>,\n",
-       " <Formula: I>,\n",
-       " <Formula: II>,\n",
-       " <Formula: III>,\n",
-       " <Formula: I,II,III>,\n",
-       " <Formula: p_1,p_2,p_3>]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 39
+     "output_type": "display_data"
     }
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 语法树展示："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "source": [
-    "f = sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\":{\n",
-    "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"ast\",\n",
-    "            \"ord2token\": True,\n",
-    "            \"var_numbering\": True,\n",
-    "        }\n",
-    "    }\n",
-    ").formula_tokens\n",
-    "f\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 109
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "source": [
-    "for i in range(0, len(f)):\n",
-    "    ForestPlotter().export(\n",
-    "        f[i], root_list=[node for node in f[i]],\n",
-    "    )\n",
-    "# plt.show()\n"
-   ],
-   "outputs": [],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
-    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+    "## to_sif"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 6,
    "source": [
-    "sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\":{\n",
-    "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"list\",\n",
-    "            \"ord2token\": True,\n",
-    "        }\n",
-    "    }\n",
-    ").formula_tokens"
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "to_sif(text)"
    ],
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "['mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub']"
+       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
       ]
      },
      "metadata": {},
-     "execution_count": 40
+     "output_type": "display_data"
     }
    ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
-   ],
    "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "source": [
-    "sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\":{\n",
-    "            \"method\": \"ast\",\n",
-    "            \"ord2token\": True,\n",
-    "            \"return_type\": \"list\",\n",
-    "            \"var_numbering\": True\n",
-    "        }\n",
-    "    }\n",
-    ").formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_0',\n",
-       " 'mathord_2',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " ',',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " ',',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 44
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 综合训练\n",
-    "\n",
-    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\n",
-    "        symbol=\"fgm\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 96
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
   }
  ],
  "metadata": {
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.8.5 64-bit"
+   "display_name": "Python 3.6.3 64-bit"
   },
   "language_info": {
    "name": "python",
-   "version": "3.8.5",
+   "version": "3.6.3",
    "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
@@ -1010,7 +163,7 @@
    "file_extension": ".py"
   },
   "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+   "hash": "6f23ddf1f0697a8f0c43dd2435bdb82528077c79e9967f824fba6a3b52b05faf"
   }
  },
  "nbformat": 4,

From 62d34f26463af3c76c1bd1522ba8f00ddf89b062 Mon Sep 17 00:00:00 2001
From: Rui Lv <lvrui2018@mail.ustc.edu.cn>
Date: Mon, 6 Sep 2021 14:26:55 +0800
Subject: [PATCH 042/149] fix merge conflict

---
 examples/formula/formula.ipynb               | 42 ----------
 examples/pretrain/gensim/w2v_stem_text.ipynb |  8 --
 examples/sif/sif.ipynb                       |  8 --
 examples/utils/data.ipynb                    | 84 --------------------
 4 files changed, 142 deletions(-)

diff --git a/examples/formula/formula.ipynb b/examples/formula/formula.ipynb
index 47ed347f..f748a90a 100644
--- a/examples/formula/formula.ipynb
+++ b/examples/formula/formula.ipynb
@@ -22,43 +22,6 @@
     "from EduNLP.Formula import FormulaGroup\n",
     "from EduNLP.Formula.viz import ForestPlotter"
    ],
-<<<<<<< HEAD
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!\n",
-      "  warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n",
-      "/home/lvrui/.local/lib/python3.8/site-packages/EduNLP/Formula/viz/__init__.py:5: UserWarning: Do not use this package\n",
-      "  warnings.warn(\"Do not use this package\")\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 公式语法结构分析\n",
-    "\n",
-    "### 初始化实例\n",
-    "\n",
-    "- item 类型：`str or List[Dict]` \n",
-    "- item 内容：latex 公式 或 公式经解析后产生的抽象语法分析树(abstracted syntax tree) "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "source": [
-    "f = Formula(\"x^2 + x+1 = y\")\n",
-    "f "
-   ],
-   "outputs": [
-    {
-=======
    "outputs": [],
    "metadata": {}
   },
@@ -83,7 +46,6 @@
    ],
    "outputs": [
     {
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
      "output_type": "execute_result",
      "data": {
       "text/plain": [
@@ -136,11 +98,7 @@
   {
    "cell_type": "markdown",
    "source": [
-<<<<<<< HEAD
-    "- 查看公式的抽象语法分析树：  "
-=======
     "- 查看公式的抽象语法分析树："
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
    ],
    "metadata": {}
   },
diff --git a/examples/pretrain/gensim/w2v_stem_text.ipynb b/examples/pretrain/gensim/w2v_stem_text.ipynb
index c76aebdf..88922789 100644
--- a/examples/pretrain/gensim/w2v_stem_text.ipynb
+++ b/examples/pretrain/gensim/w2v_stem_text.ipynb
@@ -63,11 +63,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "source": [
-<<<<<<< HEAD
-    "# 100 dimension with skipgram method\n",
-=======
     "# 100 dimension with skipgram method\r\n",
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
     "train_vector(sif_items, \"../../../data/w2v/gensim_luna_stem_t_\", 100)"
    ],
    "outputs": [
@@ -105,11 +101,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "source": [
-<<<<<<< HEAD
-    "# 50 dimension with cbow method\n",
-=======
     "# 50 dimension with cbow method\r\n",
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
     "train_vector(sif_items, \"../../../data/w2v/gensim_luna_stem_t_\", 50, method=\"cbow\")"
    ],
    "outputs": [
diff --git a/examples/sif/sif.ipynb b/examples/sif/sif.ipynb
index c647bd92..41546dd9 100644
--- a/examples/sif/sif.ipynb
+++ b/examples/sif/sif.ipynb
@@ -7,11 +7,7 @@
     "\n",
     "## 概述\n",
     "\n",
-<<<<<<< HEAD
     "SIF4Sci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
-=======
-    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
    ],
    "metadata": {
     "collapsed": true,
@@ -23,11 +19,7 @@
   {
    "cell_type": "markdown",
    "source": [
-<<<<<<< HEAD
     "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIF4Sci 的使用方法。    \n",
-=======
-    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
     "\n",
     "![Figure](../../asset/_static/item.png)"
    ],
diff --git a/examples/utils/data.ipynb b/examples/utils/data.ipynb
index 115c685a..ded161cf 100644
--- a/examples/utils/data.ipynb
+++ b/examples/utils/data.ipynb
@@ -8,7 +8,6 @@
    "metadata": {}
   },
   {
-<<<<<<< HEAD
    "cell_type": "markdown",
    "source": [
     "## 导入模块"
@@ -16,8 +15,6 @@
    "metadata": {}
   },
   {
-=======
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
    "cell_type": "code",
    "execution_count": 1,
    "source": [
@@ -28,20 +25,14 @@
      "output_type": "stream",
      "name": "stderr",
      "text": [
-<<<<<<< HEAD
       "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!\n",
       "  warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n"
-=======
-      "/home/lvrui/.local/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
      ]
     }
    ],
    "metadata": {}
   },
   {
-<<<<<<< HEAD
    "cell_type": "markdown",
    "source": [
     "## 测试数据"
@@ -56,15 +47,6 @@
     "    \"stem\": r\"若复数$z=1+2 i+i^{3}$，则$|z|=$\",\n",
     "    \"options\": ['0', '1', r'$\\sqrt{2}$', '2'],\n",
     "    }\n",
-=======
-   "cell_type": "code",
-   "execution_count": 3,
-   "source": [
-    "item = {\r\n",
-    "    \"stem\": r\"若复数$z=1+2 i+i^{3}$，则$|z|=$\",\r\n",
-    "    \"options\": ['0', '1', r'$\\sqrt{2}$', '2'],\r\n",
-    "    }\r\n",
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
     "item"
    ],
    "outputs": [
@@ -83,7 +65,6 @@
    "metadata": {}
   },
   {
-<<<<<<< HEAD
    "cell_type": "markdown",
    "source": [
     "## 区分题目成分"
@@ -106,45 +87,22 @@
     "            #  keys=[\"options\"],\n",
     "             tag_mode=\"head\"\n",
     "             )\n"
-=======
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "# 给题目各个部分加标签\r\n",
-    "dict2str4sif(item)"
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-<<<<<<< HEAD
        "'$\\\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{options}$0$\\\\SIFSep$1$\\\\SIFSep$$\\\\sqrt{2}$$\\\\SIFSep$2'"
       ]
      },
      "metadata": {},
      "execution_count": 15
-=======
-       "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options_end}$'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 4
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
     }
    ],
    "metadata": {}
   },
   {
-<<<<<<< HEAD
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-=======
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
    "cell_type": "code",
    "execution_count": 5,
    "source": [
@@ -166,102 +124,60 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
    "execution_count": 6,
    "source": [
     "dict2str4sif(item, add_list_no_tag=False)"
-=======
-   "execution_count": 7,
-   "source": [
-    "dict2str4sif(item, tag_mode=\"head\")"
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-<<<<<<< HEAD
        "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$0$\\\\SIFSep$1$\\\\SIFSep$$\\\\sqrt{2}$$\\\\SIFSep$2$\\\\SIFTag{options_end}$'"
       ]
      },
      "metadata": {},
      "execution_count": 6
-=======
-       "'$\\\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{options}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 7
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
    "execution_count": 7,
    "source": [
     "dict2str4sif(item, tag_mode=\"head\")"
-=======
-   "execution_count": 9,
-   "source": [
-    "dict2str4sif(item, tag_mode=\"tail\")"
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-<<<<<<< HEAD
        "'$\\\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{options}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2'"
       ]
      },
      "metadata": {},
      "execution_count": 7
-=======
-       "'若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options}$'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 9
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
    "execution_count": 9,
    "source": [
     "dict2str4sif(item, tag_mode=\"tail\")"
-=======
-   "execution_count": 10,
-   "source": [
-    "dict2str4sif(item, add_list_no_tag=False)"
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-<<<<<<< HEAD
        "'若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem}$$\\\\SIFTag{list_0}$0$\\\\SIFTag{list_1}$1$\\\\SIFTag{list_2}$$\\\\sqrt{2}$$\\\\SIFTag{list_3}$2$\\\\SIFTag{options}$'"
       ]
      },
      "metadata": {},
      "execution_count": 9
-=======
-       "'$\\\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\\\SIFTag{stem_end}$$\\\\SIFTag{options_begin}$0$\\\\SIFSep$1$\\\\SIFSep$$\\\\sqrt{2}$$\\\\SIFSep$2$\\\\SIFTag{options_end}$'"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 10
->>>>>>> 5ab4854b328241d38f8e43d9b618a07fed473c84
     }
    ],
    "metadata": {}

From a00e15b464c5661a37edc197071fb69aae2f720c Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 6 Sep 2021 16:44:22 +0800
Subject: [PATCH 043/149] [DOC]Update documents

---
 docs/source/conf.py                           |    3 +
 docs/source/index.rst                         |    4 +-
 docs/source/tutorial/zh/index.rst             |  111 +-
 docs/source/tutorial/zh/parse.rst             |   23 +
 docs/source/tutorial/zh/pretrain.rst          |  121 +-
 docs/source/tutorial/zh/seg.rst               |   20 +-
 docs/source/tutorial/zh/sif.rst               |    6 +
 docs/source/tutorial/zh/tokenize.rst          |    5 +-
 docs/source/tutorial/zh/vectorization.rst     |  143 ++-
 ...5\347\273\203\346\250\241\345\236\213.rst" |    2 +-
 examples/sif/sif4sci.ipynb                    | 1018 +++++++++++++++++
 11 files changed, 1336 insertions(+), 120 deletions(-)
 create mode 100644 examples/sif/sif4sci.ipynb

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 9d6a118b..995e97c8 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -74,6 +74,9 @@ def copy_tree(src, tar):
     'build/blitz/pretrain/seg_token/d2v': '_static/d2v.png',
     'build/blitz/pretrain/seg_token/d2v_d1': '_static/d2v_d1.png',
     'build/blitz/pretrain/seg_token/d2v_d2': '_static/d2v_d2.png',
+    'build/blitz/tokenizer/tokenizier': '_static/tokenizer.png',
+    'build/blitz/sif/sif4sci': '_static/tokenizer.png',
+    'build/blitz/vectorization/get_pretrained_i2v': '_static/i2v.png',
 }
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 16107eae..337dce47 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -151,11 +151,11 @@ If this repository is helpful for you, please cite our work
 
    tutorial/zh/index
    tutorial/zh/sif
-   tutorial/zh/seg
    tutorial/zh/parse
+   tutorial/zh/seg
    tutorial/zh/tokenize
-   tutorial/zh/vectorization
    tutorial/zh/pretrain
+   tutorial/zh/vectorization
 
 
 .. toctree::
diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index d10a4e67..11a5cfc4 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -9,62 +9,15 @@
 
 *  `令牌化 <tokenize.rst>`_ 
 
+*  `预训练 <pretrain.rst>`_ 
+
 *  `向量化 <vectorization.rst>`_ 
 
-*  `预训练 <pretrain.rst>`_ 
 
 示例
 --------
 
-成分分解
-^^^^^^^^^^^
-
-语义成分分解
-####################
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: dict2str4sif_gallery
-    :glob:
-    
-    Code for beginner to learn how to use dict2str4sif  <../../build/blitz/utils/data.ipynb>
-
-
-结构成分分解
-####################
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: seg_gallery
-    :glob:
-    
-    Code for beginner to learn how to use seg  <../../build/blitz/seg/seg.ipynb>
-
-
-语法解析
-^^^^^^^^^^^
-
-文本语法结构解析
-####################
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: parse_gallery
-    :glob:
-    
-    Code for beginner to learn how to use parse  <../../build/blitz/parse/parse.ipynb>
-
-
-公式语法结构解析
-####################
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: formula_gallery
-    :glob:
-    
-    Code for beginner to learn how to use Formula  <../../build/blitz/formula/formula.ipynb>
-
+为使您快速了解此项目的功能，此部分仅展示常用的函数接口使用方法（如得到令牌化序列、试题对应的向量等），对于其中间函数模块（如parse、formula、segment等）以及更细分的接口方法不做展示，如需深入学习，请查看相关部分的文档。
 
 令牌化
 ^^^^^^^^^^^
@@ -74,7 +27,9 @@
     :name: tokenizer_gallery
     :glob:
     
-    Code for beginner to learn how to use Tokenizer  <../../build/blitz/tokenizer/tokenizer.ipynb>
+    直接使用tokenize模块来进行令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>
+    调用已经封装好的令牌化容器进行令牌化  <../../build/blitz/tokenizer/tokenizier.ipynb>
+    调用sif4sci来进行令牌化 <../../build/blitz/sif/sif4sci.ipynb>
 
 
 向量化
@@ -85,54 +40,6 @@
     :name: vectorization_gallery
     :glob:
     
-    Code for beginner to learn how to use i2v  <../../build/blitz/vectorization/i2v.ipynb>
-
-
-预训练
-^^^^^^^^^^^
-
-获得数据集
-####################
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: rst1-gallery
-    :glob:
-
-    prepare_dataset  <../../build/blitz/pretrain/prepare_dataset.ipynb>
-
-
-gensim模型d2v例子
-####################
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: rst2-gallery
-    :glob:
-
-    d2v_general  <../../build/blitz/pretrain/gensim/d2v_general.ipynb>
-    d2v_bow_tfidf  <../../build/blitz/pretrain/gensim/d2v_bow_tfidf.ipynb>
-    d2v_stem_tf  <../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
-
-
-gensim模型w2v例子
-####################
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: rst3-gallery
-    :glob:
-
-    w2v_stem_text  <../../build/blitz/pretrain/gensim/w2v_stem_text.ipynb>
-    w2v_stem_tf  <../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
-
-
-seg_token例子
-####################
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: rst4-gallery
-    :glob:
-
-    d2v.ipynb  <../../build/blitz/pretrain/seg_token/d2v.ipynb>
+    获得数据集  <../../build/blitz/pretrain/prepare_dataset.ipynb>
+    调用已有模型得到对应的向量  <../../build/blitz/vectorization/i2v.ipynb>
+    直接利用提供的预训练模型得到对应向量  <../../build/blitz/vectorization/get_pretrained_i2v.ipynb>
diff --git a/docs/source/tutorial/zh/parse.rst b/docs/source/tutorial/zh/parse.rst
index 19d09d97..3758dcb5 100644
--- a/docs/source/tutorial/zh/parse.rst
+++ b/docs/source/tutorial/zh/parse.rst
@@ -43,6 +43,15 @@ Formula 首先在分词功能中对原始文本的公式做切分处理，另外
 
 本模块另提供公式变量标准化的功能，如判断几个子公式内的‘x’为同一变量。
 
+调用库
++++++++++
+
+::
+
+   import matplotlib.pyplot as plt
+   from EduNLP.Formula import Formula
+   from EduNLP.Formula.viz import ForestPlotter
+
 初始化
 +++++++++
 
@@ -110,8 +119,10 @@ item为str 或 List[Dict]类型，具体内容为latex 公式 或 公式经解
    >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
    >>> plt.show()
 
+
 .. figure:: ../../../_static/formula.png
 
+
 变量标准化
 +++++++++++
 
@@ -139,6 +150,10 @@ FormulaGroup
 
 ::
 
+   import matplotlib.pyplot as plt
+   from EduNLP.Formula import Formula
+   from EduNLP.Formula import FormulaGroup
+   from EduNLP.Formula.viz import ForestPlotter
    >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
    >>> fs
    <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
@@ -193,6 +208,7 @@ FormulaGroup
 
 .. figure:: ../../../_static/formulagroup.png
 
+
 文本语法结构解析
 --------------------
 
@@ -217,6 +233,13 @@ FormulaGroup
  
 * 使用_is_formula_legal函数，检查latex公式的完整性和可解析性，对于不合法公式报错
 
+调用库
+>>>>>>>>>>>>
+
+::
+
+   from EduNLP.SIF.Parser import Parser
+
 输入
 >>>>>>>
 
diff --git a/docs/source/tutorial/zh/pretrain.rst b/docs/source/tutorial/zh/pretrain.rst
index 477717a4..c7c5e697 100644
--- a/docs/source/tutorial/zh/pretrain.rst
+++ b/docs/source/tutorial/zh/pretrain.rst
@@ -8,13 +8,124 @@
 * 如何加载预训练模型
 * 公开的预训练模型
 
-学习路线图
-------------------
+导入模块
+----------
+
+::
+
+   from EduNLP.I2V import get_pretrained_i2v
+   from EduNLP.Vector import get_pretrained_t2v
+
+
+装载模型
+--------
+
+将所得到的模型传入I2V模块即可装载模型
+ 
+Examples：
+
+::
+
+   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+
+训练模型
+------------
+
+如需训练模型则可直接train_vector函数接口，来使使训练模型更加方便。模块调用gensim库中的相关训练模型，目前提供了"sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf"的训练方法，并提供了embedding_dim参数，使之可以按照需求确定向量的维度。
+
+基本步骤
+##################
+
+1.确定模型的类型，选择适合的Tokenizer（GensimWordTokenizer、 GensimSegTokenizer），使之令牌化；
+
+2.调用train_vector函数，即可得到所需的预训练模型。
+
+Examples：
+
+::
+
+   >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+   
+   # 10 dimension with fasstext method
+   train_vector(sif_items, "../../../data/w2v/gensim_luna_stem_tf_", 10, method="d2v")
+
+
+公开模型一览
+------------
+
+版本说明
+##################
+
+一级版本
+
+* 公开版本1（luna_pub）：高考
+* 公开版本2（ luna_pub_large）：高考 + 地区试题
+
+二级版本：
+
+* 小科（Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry）
+* 大科（理科science、文科literal、全科all）
+
+三级版本：【待完成】
+
+* 不使用第三方初始化词表
+* 使用第三方初始化词表 
+
+模型训练数据说明
+##################
+
+* 当前【词向量w2v】【句向量d2v】模型所用的数据均为 【高中学段】 的题目
+* 测试数据：`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
+
+当前提供以下模型，更多分学科、分题型模型正在训练中，敬请期待
+    "d2v_all_256"(全科)，"d2v_sci_256"(理科)，"d2v_eng_256"（英语），"d2v_lit_256"(文科)
+
+
+模型训练案例
+------------
+
+获得数据集
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   prepare_dataset  <../../build/blitz/pretrain/prepare_dataset.ipynb>
+
+gensim模型d2v例子
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v_bow_tfidf  <../../build/blitz/pretrain/gensim/d2v_bow_tfidf.ipynb>
+   d2v_general  <../../build/blitz/pretrain/gensim/d2v_general.ipynb>
+   d2v_stem_tf  <../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
+
+gensim模型w2v例子
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   w2v_stem_text  <../../build/blitz/pretrain/gensim/w2v_stem_text.ipynb>
+   w2v_stem_tf  <../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
+
+seg_token例子
+####################
 
 .. toctree::
    :maxdepth: 1
    :titlesonly:
 
-   训练模型 <pretrain/start.rst>
-   装载模型 <pretrain/loading.rst>
-   公开模型一览 <pretrain/pub.rst>
+   d2v.ipynb  <../../build/blitz/pretrain/seg_token/d2v.ipynb>
+   d2v_d1  <../../build/blitz/pretrain/seg_token/d2v_d1.ipynb>
+   d2v_d2  <../../build/blitz/pretrain/seg_token/d2v_d2.ipynb>
\ No newline at end of file
diff --git a/docs/source/tutorial/zh/seg.rst b/docs/source/tutorial/zh/seg.rst
index c029da96..f65a2b41 100644
--- a/docs/source/tutorial/zh/seg.rst
+++ b/docs/source/tutorial/zh/seg.rst
@@ -19,6 +19,12 @@
 
 由于选择题是以字典的形式给出，故需要将其在保留数据类型关系的情况下转换为文本格式。dict2str4sif函数就是实现此功能的一个模块，该模块可以将选择题形式的item转换为字符格式，并将题干和选项、各选项之间分割开来。
 
+导入库
++++++++++
+
+::
+
+ from EduNLP.utils import dict2str4sif
 
 基础使用方法
 ++++++++++++++++++
@@ -67,7 +73,11 @@
 
 对切片后的item中的各个元素进行分词，提供深度选项，可以按照需求选择所有地方切分或者在部分标签处切分（比如\SIFSep、\SIFTag处）；对标签添加的位置也可以进行选择，可以在头尾处添加或仅在头或尾处添加。
 
-具有两种模式，一种是linear模式，用于对文本进行处理（使用jieba库进行分词）；一种是ast模式，用于对公式进行解析。
+具有两种模式:
+
+* linear模式，用于对文本进行处理（使用jieba库进行分词）；
+
+* ast模式，用于对公式进行解析。
 
 基础分解流程：
 
@@ -79,6 +89,14 @@
 
 - 按照需求输入相应的参数得到筛选后的结果
 
+导入库
++++++++++
+
+::
+
+ from EduNLP.SIF.segment import seg
+ from EduNLP.SIF import sif4sci
+
 基础使用方法
 ++++++++++++++++++
 
diff --git a/docs/source/tutorial/zh/sif.rst b/docs/source/tutorial/zh/sif.rst
index 038fa1e3..0d34eb91 100644
--- a/docs/source/tutorial/zh/sif.rst
+++ b/docs/source/tutorial/zh/sif.rst
@@ -90,6 +90,12 @@ version: 0.2
 判断是否为sif格式和转换为sif格式的函数
 --------------------------------------------
 
+调用库
+++++++++
+::
+
+    from EduNLP.SIF import is_sif, to_sif
+
 is_sif
 +++++++++++
 
diff --git a/docs/source/tutorial/zh/tokenize.rst b/docs/source/tutorial/zh/tokenize.rst
index d9c9eeb5..9bac75b7 100644
--- a/docs/source/tutorial/zh/tokenize.rst
+++ b/docs/source/tutorial/zh/tokenize.rst
@@ -44,7 +44,8 @@
 Examples：
 
 ::
-    
+
+   from EduNLP.SIF.tokenization.text import tokenize 
    >>> text = "三角函数是基本初等函数之一"
    >>> tokenize(text, granularity="word")
    ['三角函数', '初等', '函数']
@@ -65,7 +66,7 @@ Examples：
 
     
 ::
-
+   from EduNLP.Tokenizer import get_tokenizer
    >>> items = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
    >>> tokenize(SegmentList(items))
    ['如图所示', '三角形', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]
diff --git a/docs/source/tutorial/zh/vectorization.rst b/docs/source/tutorial/zh/vectorization.rst
index 89175ba6..8c57cac7 100644
--- a/docs/source/tutorial/zh/vectorization.rst
+++ b/docs/source/tutorial/zh/vectorization.rst
@@ -3,6 +3,10 @@
 
 此部分提供了简便的接口，可以直接将传入的items经过转化得到向量。当前提供了是否使用预训练模型的选项，可根据需要进行选择，如不使用预训练模型则可直接调用D2V函数，使用预训练模型则调用get_pretrained_i2v函数。
 
+- 不使用预训练模型
+
+- 使用预训练模型
+
 总体流程
 ---------------------------
 
@@ -14,13 +18,138 @@
 
 4.使用已有或者使用提供的预训练模型，将令牌化后的item转换为向量。
 
-学习路线图
----------------------------
 
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
+不使用预训练模型:直接调用已有模型
+------------------------------------
+
+使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。
+
+* 优点：可以使用自己的模型，另可调整训练参数，灵活性强。
+
+导入模块
+++++++++++
+
+::
+
+   from EduNLP.I2V import D2V,W2V,get_pretrained_i2v
+   from EduNLP.Vector import T2V,get_pretrained_t2v
+
+提供的模型类型
+++++++++++++++++++++
+
+- W2V
+
+- D2V
+
+- T2V
+
+W2V
+<<<<<<<<<
+
+此模型方法直接使用gensim库中的相关模型方法，将传入的word转换为vector，当前提供一下四种方法：
+
+ - FastText
+
+ - Word2Vec
+
+ - KeyedVectors
+
+::
+
+   >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") # doctest: +ELLIPSIS
+   >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
+   >>> item_vector # doctest: +ELLIPSIS
+   array([[...]], dtype=float32)
+
+D2V
+<<<<<<<<<<<<
+
+此模型方法可以将item转换为vector，是一个综合性的处理方法，当前提供以下方法：
+
+- d2v：调用gensim库中的Doc2Vec，来使item转换为vector
+
+- BowLoader：调用gensim库中的corpora模块将doc转化为bow
+
+- TfidfLoader：调用gensim库中的TfidfModel模块将doc转化为bow
+
+::
+
+   >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$"}
+   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+   >>> i2v(item)
+   ([array([ 4.76559885e-02, -1.60574958e-01,  1.94614579e-03,  2.40295693e-01,
+   2.24517003e-01, -3.24351490e-02,  4.35789041e-02, -1.65670961e-02,...
+
+T2V
+<<<<<<<<<<
+
+使用自己提供的任一预训练模型（给出模型存放路径即可）将一组题目的切分序列表征为向量。
+
+- 优点：模型及其参数可自主调整，灵活性强。
+
+输入
+^^^^^^^^^^
+
+类型：list  
+内容：一个题组中每个题目切分序列的组合。
+> 使用 ``GensimWordTokenizer`` 模型即可将题目文本（`str` 类型）转换成 tokens。
+
+::
+
+   >>> token_items=['公式','[FORMULA]','公式','[FORMULA]','如图','[FIGURE]','x',',','y','约束条件','[SEP]','z','=','x','+','7','y','最大值','[MARK]']
+   >>> path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> t2v = T2V('d2v',filepath=path)
+   >>> t2v(token_items)
+   [array([ 0.0256574 ,  0.06061139, -0.00121044, -0.0167674 , -0.0111706 ,
+   0.05325712, -0.02097339, -0.01613594,  0.02904145,  0.0185046 ,...
+
+处理的具体流程
+++++++++++++++++++++
+
+1.调用get_tokenizer函数，得到经过分词后的结果；
+
+2.根据使用的模型，选择提供的模型类型，进行向量化处理。
+
+
+使用预训练模型：直接调用get_pretrained_i2v
+---------------------------------------------
+
+使用 EduNLP 项目组给定的预训练模型将给定的题目文本转成向量。
+
+* 优点：简单方便。
+
+* 缺点：只能使用项目中给定的模型，局限性较大。
+
+* 调用此函数即可获得相应的预训练模型，目前提供以下的预训练模型：d2v_all_256、d2v_sci_256、d2v_eng_256、d2v_lit_256
+
+模型选择与使用
+##################
+
+根据题目所属学科选择预训练模型：
+
++--------------------+------------------------+
+|    预训练模型名称  | 模型训练数据的所属学科 |
++====================+========================+
+|    d2v_all_256     |        全学科          |
++--------------------+------------------------+
+|    d2v_sci_256     |         理科           |
++--------------------+------------------------+
+|    d2v_lit_256     |         文科           |
++--------------------+------------------------+
+|    d2v_eng_256     |         英语           |
++--------------------+------------------------+
+
+处理的具体流程
+##################
+
+1.下载相应的预处理模型
+
+2.将所得到的模型传入D2V，使用D2V进行处理
+
+Examples：
 
-   不使用预训练模型 <vectorization/不使用预训练模型.rst>
-   使用预训练模型 <vectorization/使用预训练模型.rst>
+::
 
+  >>> i2v = get_pretrained_i2v("d2v_sci_256")
+  >>> i2v(item)
diff --git "a/docs/source/tutorial/zh/vectorization/\344\270\215\344\275\277\347\224\250\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213.rst" "b/docs/source/tutorial/zh/vectorization/\344\270\215\344\275\277\347\224\250\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213.rst"
index 5a26588f..04f21712 100644
--- "a/docs/source/tutorial/zh/vectorization/\344\270\215\344\275\277\347\224\250\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213.rst"
+++ "b/docs/source/tutorial/zh/vectorization/\344\270\215\344\275\277\347\224\250\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213.rst"
@@ -11,7 +11,7 @@
 
 1.调用get_tokenizer函数，得到经过分词后的结果；
 
-2.调用T2V模块，根据需要选择是否使用预训练的t2v模型
+2.调用D2V或W2V等模块，根据需要选择是否使用预训练的t2v模型
 
 Examples：
 
diff --git a/examples/sif/sif4sci.ipynb b/examples/sif/sif4sci.ipynb
new file mode 100644
index 00000000..2076e126
--- /dev/null
+++ b/examples/sif/sif4sci.ipynb
@@ -0,0 +1,1018 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# SIF4Sci 使用示例\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
+    "\n",
+    "![Figure](../../asset/_static/item.png)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "item = {\n",
+    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\n",
+    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\n",
+    "}\n",
+    "item[\"stem\"]"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 加载图片：`$\\\\FigureID{1}$`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "from PIL import Image\n",
+    "img = Image.open(\"../../asset/_static/item_figure.png\")\n",
+    "figures = {\"1\": img}\n",
+    "img"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入模块"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 验证题目格式"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "is_sif(item['stem'])"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "is_sif(text)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "to_sif(text)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 题目切分及令牌化\n",
+    "\n",
+    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 题目切分"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 基本切分\n",
+    "分离文本、公式、图片和特殊符号。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\n",
+    "segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "segments.text_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
+       " '的斜边',\n",
+       " ', 直角边',\n",
+       " ', ',\n",
+       " '.',\n",
+       " '的三边所围成的区域记为',\n",
+       " ',黑色部分记为',\n",
+       " ', 其余部分记为',\n",
+       " '.在整个图形中随机取一点，此点取自',\n",
+       " '的概率分别记为',\n",
+       " ',则']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 公式部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "source": [
+    "segments.formula_segments\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I,II,III',\n",
+       " 'p_1,p_2,p_3']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 图片部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "segments.figure_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[\\FigureID{1}]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "segments.figure_segments[0].figure"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 特殊符号"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "segments.ques_mark_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\SIFChoice']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 标记化切分  \n",
+    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    " - symbol:\n",
+    "    - \"t\": text\n",
+    "    - \"f\": formula\n",
+    "    - \"g\": figure\n",
+    "    - \"m\": question mark"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 令牌化\n",
+    "\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
+    "\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "tokens.text_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " '斜边',\n",
+       " '直角',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " '概率',\n",
+       " '记']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 公式解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "tokens.formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 自定义参数，得到定制化解析结果"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "source": [
+    "sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\": {\n",
+    "            \"method\": \"linear\",\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "source": [
+    "sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\":{\n",
+    "            \"method\": \"ast\",\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<Formula: ABC>,\n",
+       " <Formula: BC>,\n",
+       " <Formula: AB>,\n",
+       " <Formula: AC>,\n",
+       " <Formula: \\bigtriangleup ABC>,\n",
+       " <Formula: I>,\n",
+       " <Formula: II>,\n",
+       " <Formula: III>,\n",
+       " <Formula: I,II,III>,\n",
+       " <Formula: p_1,p_2,p_3>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 语法树展示："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "source": [
+    "f = sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\":{\n",
+    "            \"method\": \"ast\",\n",
+    "            \"return_type\": \"ast\",\n",
+    "            \"ord2token\": True,\n",
+    "            \"var_numbering\": True,\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens\n",
+    "f\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 109
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "source": [
+    "for i in range(0, len(f)):\n",
+    "    ForestPlotter().export(\n",
+    "        f[i], root_list=[node for node in f[i]],\n",
+    "    )\n",
+    "# plt.show()\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "source": [
+    "sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\":{\n",
+    "            \"method\": \"ast\",\n",
+    "            \"return_type\": \"list\",\n",
+    "            \"ord2token\": True,\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "source": [
+    "sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\":{\n",
+    "            \"method\": \"ast\",\n",
+    "            \"ord2token\": True,\n",
+    "            \"return_type\": \"list\",\n",
+    "            \"var_numbering\": True\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_0',\n",
+       " 'mathord_2',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 综合训练\n",
+    "\n",
+    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\n",
+    "        symbol=\"fgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 96
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file

From 9e2bfffcc7750b38304082785a49b29f6bcc425d Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 6 Sep 2021 17:33:36 +0800
Subject: [PATCH 044/149] Create sif.ipynb

---
 examples/sif/sif.ipynb | 975 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 911 insertions(+), 64 deletions(-)

diff --git a/examples/sif/sif.ipynb b/examples/sif/sif.ipynb
index 3ba5b7d3..25affe58 100644
--- a/examples/sif/sif.ipynb
+++ b/examples/sif/sif.ipynb
@@ -3,156 +3,1003 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# 标准项目格式\r\n",
-    "\r\n",
-    "version: 0.2\r\n",
-    "\r\n",
-    "为了后续研究和使用的方便，我们需要一个统一的试题语法标准。\r\n",
-    "\r\n",
-    "## 语法规则\r\n",
-    "1. 题目文本中只允许出现中文字符、中英文标点和换行符。\r\n",
-    "2. 使用 \\$\\SIFBlank\\$ 替换横线，对于选择题中的括号使用 \\$\\SIFChoice\\$ 替换。\r\n",
-    "3. 图片 ID 以公式的形式嵌入文本中：`$\\FigureID{ uuid }$` 或用 base64 编码表示，特别的，内容为公式的图片用`$\\FormFigureID{ uuid }$`表示。\r\n",
-    "4. 文本标注格式：统一用 `$\\textf{item,CHAR_EN}$` 表示，目前定义的有：b-加粗，i-斜体，u-下划线，w-下划波浪线，d-加点，t-标题。标注可以混用，按字母顺序排序，例如：$\\textf{EduNLP, biu}$ 表示 <u>***EduNLP***</u>\r\n",
-    "5. 其余诸如，英文字母、罗马字符、数字等数学符号一律需要使用 latex 格式表示，即嵌在 `$$` 之中。\r\n",
-    "6. 分子式的录入标准暂且参考 [INCHI](https://zh.wikipedia.org/wiki/%E5%9B%BD%E9%99%85%E5%8C%96%E5%90%88%E7%89%A9%E6%A0%87%E8%AF%86)\r\n",
-    "7. 目前对 latex 内部语法没有要求。\r\n",
-    "\r\n",
-    "## 示例\r\n",
-    "\r\n",
-    "标准形式:\r\n",
-    "\r\n",
-    "1. `若$x,y$满足约束条件$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，则$z=x+7 y$的最大值$\\\\SIFUnderline$'`\r\n",
-    "\r\n",
-    "2. `已知函数$f(x)=|3 x+1|-2|x|$画出$y=f(x)$的图像求不等式$f(x)>f(x+1)$的解集$\\\\PictureID{3bf2ddf4-8af1-11eb-b750-b46bfc50aa29}$$\\\\PictureID{59b8bd14-8af1-11eb-93a5-b46bfc50aa29}$$\\\\PictureID{63118b3a-8b75-11eb-a5c0-b46bfc50aa29}$$\\\\PictureID{6a006179-8b76-11eb-b386-b46bfc50aa29}$$\\\\PictureID{088f15eb-8b7c-11eb-a86f-b46bfc50aa29}$`\r\n",
-    "\r\n",
-    "非标准形式：\r\n",
-    "\r\n",
-    "1. 字母、数字和数学符号连续混合出现：\r\n",
-    "    例如：\r\n",
-    "    `完成下面的2x2列联表，`\r\n",
-    "    `（单位：m3）`\r\n",
-    "    `则输出的n=`\r\n",
-    "    \r\n",
-    "2. 特殊的数学符号没有用 latex 公式表示：\r\n",
-    "    例如：\r\n",
-    "    `命题中真命题的序号是 ①`\r\n",
-    "    `AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．若D为AC的中点`\r\n",
-    "    \r\n",
-    "3. 出现以 unicode 编码写成的字符\r\n",
-    "    例如：`则$a$的取值范围是（\\u3000\\u3000）`\r\n"
+    "# SIF4Sci 使用示例\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "SIF4Sci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIF4Sci 的使用方法。    \n",
+    "\n",
+    "![Figure](../../asset/_static/item.png)"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "## 此外提供了判断是否为sif格式和转换为sif格式的函数"
+    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "item = {\n",
+    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\n",
+    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\n",
+    "}\n",
+    "item[\"stem\"]"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "markdown",
    "source": [
-    "## 导入函数"
+    "- 加载图片：`$\\\\FigureID{1}$`"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
+   "source": [
+    "from PIL import Image\n",
+    "img = Image.open(\"../../asset/_static/item_figure.png\")\n",
+    "figures = {\"1\": img}\n",
+    "img"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 导入模块"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "source": [
-    "from EduNLP.SIF import is_sif, to_sif,sif4sci"
+    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
    ],
    "outputs": [],
-   "metadata": {}
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   },
   {
    "cell_type": "markdown",
    "source": [
-    "## is_sif"
+    "## 验证题目格式"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "source": [
-    "text = '若$x,y$满足约束条件' \\\r\n",
-    "    '$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，' \\\r\n",
-    "'则$z=x+7 y$的最大值$\\\\SIFUnderline$'\r\n",
-    "    \r\n",
-    "is_sif(text)\r\n"
+    "is_sif(item['stem'])"
    ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "True"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "execution_count": 7
     }
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
     "is_sif(text)"
    ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "False"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "execution_count": 8
     }
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "to_sif(text)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 题目切分及令牌化\n",
+    "\n",
+    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 题目切分"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
   {
    "cell_type": "markdown",
    "source": [
-    "## to_sif"
+    "#### 基本切分\n",
+    "分离文本、公式、图片和特殊符号。"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
-    "to_sif(text)"
+    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\n",
+    "segments"
    ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "segments.text_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
+       " '的斜边',\n",
+       " ', 直角边',\n",
+       " ', ',\n",
+       " '.',\n",
+       " '的三边所围成的区域记为',\n",
+       " ',黑色部分记为',\n",
+       " ', 其余部分记为',\n",
+       " '.在整个图形中随机取一点，此点取自',\n",
+       " '的概率分别记为',\n",
+       " ',则']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 公式部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "source": [
+    "segments.formula_segments\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I,II,III',\n",
+       " 'p_1,p_2,p_3']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 图片部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "segments.figure_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[\\FigureID{1}]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "segments.figure_segments[0].figure"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 特殊符号"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "segments.ques_mark_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\SIFChoice']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 标记化切分  \n",
+    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    " - symbol:\n",
+    "    - \"t\": text\n",
+    "    - \"f\": formula\n",
+    "    - \"g\": figure\n",
+    "    - \"m\": question mark"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 令牌化\n",
+    "\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
+    "\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "tokens.text_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " '斜边',\n",
+       " '直角',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " '概率',\n",
+       " '记']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 公式解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "tokens.formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 自定义参数，得到定制化解析结果"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "source": [
+    "sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\": {\n",
+    "            \"method\": \"linear\",\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "source": [
+    "sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\":{\n",
+    "            \"method\": \"ast\",\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<Formula: ABC>,\n",
+       " <Formula: BC>,\n",
+       " <Formula: AB>,\n",
+       " <Formula: AC>,\n",
+       " <Formula: \\bigtriangleup ABC>,\n",
+       " <Formula: I>,\n",
+       " <Formula: II>,\n",
+       " <Formula: III>,\n",
+       " <Formula: I,II,III>,\n",
+       " <Formula: p_1,p_2,p_3>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 语法树展示："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "source": [
+    "f = sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\":{\n",
+    "            \"method\": \"ast\",\n",
+    "            \"return_type\": \"ast\",\n",
+    "            \"ord2token\": True,\n",
+    "            \"var_numbering\": True,\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens\n",
+    "f\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 109
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "source": [
+    "for i in range(0, len(f)):\n",
+    "    ForestPlotter().export(\n",
+    "        f[i], root_list=[node for node in f[i]],\n",
+    "    )\n",
+    "# plt.show()\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "source": [
+    "sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\":{\n",
+    "            \"method\": \"ast\",\n",
+    "            \"return_type\": \"list\",\n",
+    "            \"ord2token\": True,\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "execution_count": 40
     }
    ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
    "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "source": [
+    "sif4sci(\n",
+    "    item[\"stem\"],\n",
+    "    figures=figures,\n",
+    "    tokenization=True,\n",
+    "    tokenization_params={\n",
+    "        \"formula_params\":{\n",
+    "            \"method\": \"ast\",\n",
+    "            \"ord2token\": True,\n",
+    "            \"return_type\": \"list\",\n",
+    "            \"var_numbering\": True\n",
+    "        }\n",
+    "    }\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_0',\n",
+       " 'mathord_2',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 综合训练\n",
+    "\n",
+    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\n",
+    "        symbol=\"fgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 96
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.6.3 64-bit"
+   "display_name": "Python 3.8.5 64-bit"
   },
   "language_info": {
    "name": "python",
-   "version": "3.6.3",
+   "version": "3.8.5",
    "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
@@ -163,9 +1010,9 @@
    "file_extension": ".py"
   },
   "interpreter": {
-   "hash": "6f23ddf1f0697a8f0c43dd2435bdb82528077c79e9967f824fba6a3b52b05faf"
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}

From 7cdac61acc7a665a71f39a9f7d6e53630838f514 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 6 Sep 2021 21:22:17 +0800
Subject: [PATCH 045/149] Create i2v.py

---
 EduNLP/I2V/i2v.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 3152bb7a..a20558c5 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -39,7 +39,7 @@ class I2V(object):
     >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
     >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
     >>> i2v(item) # doctest: +ELLIPSIS
-    ([array([ ...dtype=float32)], None)
+    [array([...], dtype=float32)]
 
     Returns
     -------
@@ -209,7 +209,7 @@ class W2V(I2V):
     --------
     >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v")
     >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
-    >>> item_vector
+    >>> item_vector # doctest: +ELLIPSIS
     array([[...]], dtype=float32)
 
     Returns

From db4023097b4655028f851c975c5b482a033c3f34 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 6 Sep 2021 21:39:41 +0800
Subject: [PATCH 046/149] Create i2v.py

---
 EduNLP/I2V/i2v.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index a20558c5..2e708eac 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -39,7 +39,7 @@ class I2V(object):
     >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
     >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
     >>> i2v(item) # doctest: +ELLIPSIS
-    [array([...], dtype=float32)]
+    ([array([...], dtype=float32)], None)
 
     Returns
     -------
@@ -210,7 +210,7 @@ class W2V(I2V):
     >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v")
     >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
     >>> item_vector # doctest: +ELLIPSIS
-    array([[...]], dtype=float32)
+    [array([...], dtype=float32)]
 
     Returns
     -------

From 9000793f1ebc28f94499d85110bd7fd81ce29a4b Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Tue, 7 Sep 2021 10:28:03 +0800
Subject: [PATCH 047/149] Create parse.rst

---
 docs/source/tutorial/zh/parse.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/tutorial/zh/parse.rst b/docs/source/tutorial/zh/parse.rst
index 3758dcb5..6c1dfe6a 100644
--- a/docs/source/tutorial/zh/parse.rst
+++ b/docs/source/tutorial/zh/parse.rst
@@ -120,7 +120,7 @@ item为str 或 List[Dict]类型，具体内容为latex 公式 或 公式经解
    >>> plt.show()
 
 
-.. figure:: ../../../_static/formula.png
+.. figure:: ../../_static/formula.png
 
 
 变量标准化
@@ -206,7 +206,7 @@ FormulaGroup
    [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
    >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
 
-.. figure:: ../../../_static/formulagroup.png
+.. figure:: ../../_static/formulagroup.png
 
 
 文本语法结构解析

From afd70e794ad92570e055130a376f51d8f93ccdd6 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Tue, 7 Sep 2021 17:07:45 +0800
Subject: [PATCH 048/149] Create i2v.py

---
 EduNLP/I2V/i2v.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 2e708eac..6f4b0d4e 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -125,6 +125,7 @@ def vector_size(self):
 
 class D2V(I2V):
     """
+
     Parameters
     ----------
     tokenizer: str
@@ -189,6 +190,7 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
 
 class W2V(I2V):
     """
+
     Parameters
     ----------
     tokenizer: str

From 05a8ef5438e401d8fc9471f9be71e747cae903a0 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Tue, 7 Sep 2021 17:20:04 +0800
Subject: [PATCH 049/149] Create tokenize.rst

---
 docs/source/tutorial/zh/tokenize.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/tutorial/zh/tokenize.rst b/docs/source/tutorial/zh/tokenize.rst
index 9bac75b7..fa54a870 100644
--- a/docs/source/tutorial/zh/tokenize.rst
+++ b/docs/source/tutorial/zh/tokenize.rst
@@ -62,10 +62,10 @@ Examples：
 -------
 即综合解析，将带公式的句子切分为若干标记的过程。每个标记为一个“令牌”（token）。
 
-此功能对应的实现函数为tokenize，将已经经过结构成分分解后的item传入其中即可得到所需结果
+此功能对应的实现函数为tokenize，将已经经过结构成分分解后的item传入其中即可得到所需结果。
 
-    
 ::
+
    from EduNLP.Tokenizer import get_tokenizer
    >>> items = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
    >>> tokenize(SegmentList(items))
@@ -75,7 +75,7 @@ Examples：
 
 
 
-我们提供了多种已经封装好的令牌化器供用户便捷调用，通过查看 ``./EduNLP/Tokenizer/tokenizer.py`` 及 ``./EduNLP/Pretrain/gensim_vec.py`` 可以查看更多令牌化器，下面是一个完整的令牌化器列表
+我们提供了多种已经封装好的令牌化器供用户便捷调用，通过查看 ``./EduNLP/Tokenizer/tokenizer.py`` 及 ``./EduNLP/Pretrain/gensim_vec.py`` 可以查看更多令牌化器，下面是一个完整的令牌化器列表:
 
 - TextTokenizer
 

From 9c80f998dcee8a3f3443ea1de392037733a2500a Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 8 Sep 2021 09:47:14 +0800
Subject: [PATCH 050/149] [DOC]Update documents

---
 EduNLP/Formula/Formula.py  |  3 ++-
 EduNLP/I2V/i2v.py          |  2 +-
 EduNLP/ModelZoo/rnn/rnn.py | 30 ++++++++++++++----------------
 EduNLP/Vector/t2v.py       |  1 +
 4 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index f7d7a542..84ac0a75 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -261,7 +261,8 @@ def link_formulas(*formula: Formula, link_vars=True, **kwargs):
 
     Parameters
     ----------
-    formula:the parsed abstracted syntax tree
+    formula
+        the parsed abstracted syntax tree
     link_vars
     kwargs
     """
diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 6f4b0d4e..98378dcd 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -12,7 +12,7 @@
 
 class I2V(object):
     """
-    It just a api, so you shouldn't use it directly. \
+    It just a api, so you shouldn't use it directly. 
     If you want to get vector from item, you can use other model like D2V and W2V.
 
     Parameters
diff --git a/EduNLP/ModelZoo/rnn/rnn.py b/EduNLP/ModelZoo/rnn/rnn.py
index c3359fa9..97585170 100644
--- a/EduNLP/ModelZoo/rnn/rnn.py
+++ b/EduNLP/ModelZoo/rnn/rnn.py
@@ -9,6 +9,20 @@
 
 class LM(nn.Module):
     """
+
+    Parameters
+    ----------
+    rnn_type：str
+        Legal types including RNN, LSTM, GRU,ELMO
+    vocab_size: int
+    embedding_dim: int
+    hidden_size: int
+    num_layers
+    bidirectional
+    embedding
+    model_params
+    kwargs
+
     Examples
     --------
     >>> import torch
@@ -30,22 +44,6 @@ class LM(nn.Module):
 
     def __init__(self, rnn_type: str, vocab_size: int, embedding_dim: int, hidden_size: int, num_layers=1,
                  bidirectional=False, embedding=None, model_params=None, **kwargs):
-        """
-
-        Parameters
-        ----------
-        rnn_type：str
-            Legal types including RNN, LSTM, GRU,ELMO
-        vocab_size: int
-        embedding_dim: int
-        hidden_size: int
-        num_layers
-        bidirectional
-        embedding
-        model_params
-        kwargs
-
-        """
         super(LM, self).__init__()
         rnn_type = rnn_type.upper()
         self.embedding = torch.nn.Embedding(vocab_size, embedding_dim) if embedding is None else embedding
diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index 9adfe5e5..b2133587 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -21,6 +21,7 @@
 
 class T2V(object):
     """
+
     Parameters
     ----------
     model: str

From 2a97a2693a50948e1a03afa0514d3d07eae70b7b Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 8 Sep 2021 11:03:28 +0800
Subject: [PATCH 051/149] [DOC]Update document

---
 EduNLP/I2V/i2v.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 98378dcd..d2dcc531 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -12,11 +12,11 @@
 
 class I2V(object):
     """
-    It just a api, so you shouldn't use it directly. 
+    It just a api, so you shouldn't use it directly. \
     If you want to get vector from item, you can use other model like D2V and W2V.
 
     Parameters
-    ----------
+    -----------
     tokenizer: str
         the tokenizer name
     t2v: str
@@ -50,7 +50,7 @@ def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrai
         """
 
         Parameters
-        ----------
+        -----------
         tokenizer: str
             the tokenizer name
         t2v: str
@@ -127,7 +127,7 @@ class D2V(I2V):
     """
 
     Parameters
-    ----------
+    -----------
     tokenizer: str
         the tokenizer name
     t2v: str
@@ -143,7 +143,7 @@ class D2V(I2V):
         the parameters passed to t2v
 
     Examples
-    --------
+    ---------
     >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
     ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
     ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
@@ -161,7 +161,7 @@ def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=
         '''
 
         Parameters
-        ----------
+        -----------
         items:str
             the text of question
         tokenize:bool
@@ -176,7 +176,7 @@ def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=
             the parameters passed to t2v
 
         Returns
-        -------
+        --------
         vector:list
         '''
         tokens = self.tokenize(items, return_token=True, key=key) if tokenize is True else items
@@ -192,7 +192,7 @@ class W2V(I2V):
     """
 
     Parameters
-    ----------
+    -----------
     tokenizer: str
         the tokenizer name
     t2v: str
@@ -208,14 +208,14 @@ class W2V(I2V):
         the parameters passed to t2v
 
     Examples
-    --------
+    ---------
     >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v")
     >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
     >>> item_vector # doctest: +ELLIPSIS
     [array([...], dtype=float32)]
 
     Returns
-    -------
+    --------
     i2v model: W2V
 
     """
@@ -246,18 +246,18 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR):
     """
 
     Parameters
-    ----------
+    -----------
     name: str
         the name of item2vector model
     model_dir:str
         the path of model, default: MODEL_DIR = '~/.EduNLP/model'
 
     Returns
-    -------
+    --------
     i2v model: I2V
 
     Examples
-    --------
+    ---------
     >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
     ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
     ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}

From 73129724ebc223d2575159c1fcc6f0fc5b6cfa73 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 8 Sep 2021 14:58:23 +0800
Subject: [PATCH 052/149] update doc

---
 EduNLP/I2V/i2v.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index d2dcc531..d893cda2 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -26,8 +26,11 @@ class I2V(object):
     tokenizer_kwargs: dict
         the parameters passed to tokenizer
     pretrained_t2v: bool
+
         True: use pretrained t2v model
+
         False: use your own t2v model
+
     kwargs:
         the parameters passed to t2v
 
@@ -126,6 +129,8 @@ def vector_size(self):
 class D2V(I2V):
     """
 
+    Bases: I2V
+
     Parameters
     -----------
     tokenizer: str
@@ -191,6 +196,8 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
 class W2V(I2V):
     """
 
+    Bases: I2V
+
     Parameters
     -----------
     tokenizer: str

From 20a1838453086baef92b0514f0c1025476bc5fa3 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 18:21:59 +0800
Subject: [PATCH 053/149] [DOC]Update documents

---
 docs/source/api/i2v.rst | 20 +-------------------
 1 file changed, 1 insertion(+), 19 deletions(-)

diff --git a/docs/source/api/i2v.rst b/docs/source/api/i2v.rst
index 29d40879..d8306edb 100644
--- a/docs/source/api/i2v.rst
+++ b/docs/source/api/i2v.rst
@@ -1,24 +1,6 @@
 EduNLP.I2V
 ============
 
-.. automodule:: EduNLP.I2V.i2v.I2V
-   :members:
-   :imported-members:
-
-EduNLP.I2V.D2V
--------------------
-.. automodule:: EduNLP.I2V.i2v.D2V
-   :members:
-   :imported-members:
-
-EduNLP.I2V.W2V
--------------------
-.. automodule:: EduNLP.I2V.i2v.W2V
-   :members:
-   :imported-members:
-
-EduNLP.I2V.get_pretrained_i2v
----------------------------------------
-.. automodule:: EduNLP.I2V.i2v.get_pretrained_i2v
+.. automodule:: EduNLP.I2V.i2v
    :members:
    :imported-members:

From ddf37537a8313d790d066912d8d98eda5e7793a4 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 18:25:02 +0800
Subject: [PATCH 054/149] [DOC]Update i2v.py

---
 EduNLP/I2V/i2v.py | 57 ++++++++++++++++-------------------------------
 1 file changed, 19 insertions(+), 38 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index d893cda2..8e1fa3b4 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -11,46 +11,11 @@
 
 
 class I2V(object):
-    """
-    It just a api, so you shouldn't use it directly. \
-    If you want to get vector from item, you can use other model like D2V and W2V.
-
-    Parameters
-    -----------
-    tokenizer: str
-        the tokenizer name
-    t2v: str
-        the name of token2vector model
-    args:
-        the parameters passed to t2v
-    tokenizer_kwargs: dict
-        the parameters passed to tokenizer
-    pretrained_t2v: bool
-
-        True: use pretrained t2v model
-
-        False: use your own t2v model
-
-    kwargs:
-        the parameters passed to t2v
-
-    Examples
-    --------
-    >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
-    ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
-    ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
-    >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
-    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
-    >>> i2v(item) # doctest: +ELLIPSIS
-    ([array([...], dtype=float32)], None)
-
-    Returns
-    -------
-    i2v model: I2V
-    """
 
     def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrained_t2v=False, **kwargs):
         """
+        It just a api, so you shouldn't use it directly. \
+        If you want to get vector from item, you can use other model like D2V and W2V.
 
         Parameters
         -----------
@@ -63,12 +28,28 @@ def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrai
         tokenizer_kwargs: dict
             the parameters passed to tokenizer
         pretrained_t2v: bool
+
             True: use pretrained t2v model
+
             False: use your own t2v model
+
         kwargs:
             the parameters passed to t2v
 
-        """
+        Examples
+        --------
+        >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
+        ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
+        ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
+        >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
+        >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
+        >>> i2v(item) # doctest: +ELLIPSIS
+        ([array([...], dtype=float32)], None)
+
+        Returns
+        -------
+        i2v model: I2V
+    """
         self.tokenizer: Tokenizer = get_tokenizer(tokenizer, **tokenizer_kwargs if tokenizer_kwargs is not None else {})
         if pretrained_t2v:
             logger.info("Use pretrained t2v model %s" % t2v)

From 6d24488571b2a46ac6ab2668c07eaec4797b4552 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 18:50:34 +0800
Subject: [PATCH 055/149] [DOC]Update documents

---
 EduNLP/Formula/Formula.py     | 85 +++++++++++++++++------------------
 EduNLP/Tokenizer/tokenizer.py | 74 +++++++++++++++---------------
 2 files changed, 77 insertions(+), 82 deletions(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index 84ac0a75..e9e1f308 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -14,27 +14,6 @@
 
 
 class Formula(object):
-    """
-    Examples
-    --------
-    >>> f = Formula("x")
-    >>> f
-    <Formula: x>
-    >>> f.ast
-    [{'val': {'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, \
-'structure': {'bro': [None, None], 'child': None, 'father': None, 'forest': None}}]
-    >>> f.elements
-    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}]
-    >>> f.variable_standardization(inplace=True)
-    <Formula: x>
-    >>> f.elements
-    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
-
-    Returns
-    --------
-    the parsed abstracted syntax tree
-    """
-
     def __init__(self, formula: (str, List[Dict]), variable_standardization=False, const_mathord=None,
                  init=True, *args, **kwargs):
         """
@@ -48,6 +27,25 @@ def __init__(self, formula: (str, List[Dict]), variable_standardization=False, c
         init
         args
         kwargs
+
+        Examples
+        --------
+        >>> f = Formula("x")
+        >>> f
+        <Formula: x>
+        >>> f.ast
+        [{'val': {'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, \
+    'structure': {'bro': [None, None], 'child': None, 'father': None, 'forest': None}}]
+        >>> f.elements
+        [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}]
+        >>> f.variable_standardization(inplace=True)
+        <Formula: x>
+        >>> f.elements
+        [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
+
+        Returns
+        --------
+        the parsed abstracted syntax tree
         """
         self._formula = formula
         self._ast = None
@@ -130,30 +128,6 @@ def resetable(self):
 
 
 class FormulaGroup(object):
-    """
-    Examples
-    ---------
-    >>> fg = FormulaGroup(["x + y", "y + x", "z + x"])
-    >>> fg
-    <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
-    >>> fg = FormulaGroup(["x + y", Formula("y + x"), "z + x"])
-    >>> fg
-    <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
-    >>> fg = FormulaGroup(["x", Formula("y"), "x"])
-    >>> fg.elements
-    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None},\
- {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
-    >>> fg = FormulaGroup(["x", Formula("y"), "x"], variable_standardization=True)
-    >>> fg.elements
-    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, \
-{'id': 1, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, \
-{'id': 2, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
-
-    Returns
-    --------
-    the parsed abstracted syntax forest
-    """
-
     def __init__(self,
                  formula_list: (list, List[str], List[Formula]),
                  variable_standardization=False,
@@ -170,6 +144,27 @@ def __init__(self,
         const_mathord
         detach
 
+        Examples
+        ---------
+        >>> fg = FormulaGroup(["x + y", "y + x", "z + x"])
+        >>> fg
+        <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
+        >>> fg = FormulaGroup(["x + y", Formula("y + x"), "z + x"])
+        >>> fg
+        <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
+        >>> fg = FormulaGroup(["x", Formula("y"), "x"])
+        >>> fg.elements
+        [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None},\
+    {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
+        >>> fg = FormulaGroup(["x", Formula("y"), "x"], variable_standardization=True)
+        >>> fg.elements
+        [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, \
+    {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, \
+    {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
+
+        Returns
+        --------
+        the parsed abstracted syntax forest
         """
         forest = []
         self._formulas = []
diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py
index 45b50f1f..b105ecb8 100644
--- a/EduNLP/Tokenizer/tokenizer.py
+++ b/EduNLP/Tokenizer/tokenizer.py
@@ -14,33 +14,34 @@ def __call__(self, *args, **kwargs):
 
 
 class PureTextTokenizer(Tokenizer):
-    r"""
-    Examples
-    --------
-    >>> tokenizer = PureTextTokenizer()
-    >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
-    >>> tokens = tokenizer(items)
-    >>> next(tokens)[:10]
-    ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
-    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
-    >>> tokens = tokenizer(items)
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-    '\\quad', 'A', '\\cap', 'B', '=']
-    >>> items = [{
-    ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
-    ... "options": ["1", "2"]
-    ... }]
-    >>> tokens = tokenizer(items, key=lambda x: x["stem"])
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-    '\\quad', 'A', '\\cap', 'B', '=']
-    """
 
     def __init__(self, *args, **kwargs):
+        r"""
+
+        Examples
+        --------
+        >>> tokenizer = PureTextTokenizer()
+        >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
+        >>> tokens = tokenizer(items)
+        >>> next(tokens)[:10]
+        ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
+        >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+        >>> tokens = tokenizer(items)
+        >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+        ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+        '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+        '\\quad', 'A', '\\cap', 'B', '=']
+        >>> items = [{
+        ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+        ... "options": ["1", "2"]
+        ... }]
+        >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+        >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+        ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+        '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+        '\\quad', 'A', '\\cap', 'B', '=']
+        """
         self.tokenization_params = {
             "formula_params": {
                 "method": "linear",
@@ -69,19 +70,18 @@ def __call__(self, items: Iterable, key=lambda x: x, *args, **kwargs):
 
 
 class TextTokenizer(Tokenizer):
-    r"""
-
-    Examples
-    ----------
-    >>> tokenizer = TextTokenizer()
-    >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
-    >>> tokens = tokenizer(items)
-    >>> next(tokens)[:10]
-    ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
-    """
-
     def __init__(self, *args, **kwargs):
+        r"""
+
+        Examples
+        ----------
+        >>> tokenizer = TextTokenizer()
+        >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
+        >>> tokens = tokenizer(items)
+        >>> next(tokens)[:10]
+        ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+        """
         self.tokenization_params = {
             "formula_params": {
                 "method": "linear",

From 5093098e0a97bd0bfff5340fe3a08b5c609f33f9 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 18:56:29 +0800
Subject: [PATCH 056/149] Update formula

---
 EduNLP/Formula/Formula.py | 126 +++++++++++++++++++-------------------
 1 file changed, 63 insertions(+), 63 deletions(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index e9e1f308..73cfd09a 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -14,39 +14,39 @@
 
 
 class Formula(object):
+    """
+
+    Parameters
+    ----------
+    formula: str or List[Dict]
+        latex formula string or the parsed abstracted syntax tree
+    variable_standardization
+    const_mathord
+    init
+    args
+    kwargs
+
+    Examples
+    --------
+    >>> f = Formula("x")
+    >>> f
+    <Formula: x>
+    >>> f.ast
+    [{'val': {'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, \
+'structure': {'bro': [None, None], 'child': None, 'father': None, 'forest': None}}]
+    >>> f.elements
+    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}]
+    >>> f.variable_standardization(inplace=True)
+    <Formula: x>
+    >>> f.elements
+    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
+
+    Returns
+    --------
+    the parsed abstracted syntax tree
+    """
     def __init__(self, formula: (str, List[Dict]), variable_standardization=False, const_mathord=None,
                  init=True, *args, **kwargs):
-        """
-
-        Parameters
-        ----------
-        formula: str or List[Dict]
-            latex formula string or the parsed abstracted syntax tree
-        variable_standardization
-        const_mathord
-        init
-        args
-        kwargs
-
-        Examples
-        --------
-        >>> f = Formula("x")
-        >>> f
-        <Formula: x>
-        >>> f.ast
-        [{'val': {'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, \
-    'structure': {'bro': [None, None], 'child': None, 'father': None, 'forest': None}}]
-        >>> f.elements
-        [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}]
-        >>> f.variable_standardization(inplace=True)
-        <Formula: x>
-        >>> f.elements
-        [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
-
-        Returns
-        --------
-        the parsed abstracted syntax tree
-        """
         self._formula = formula
         self._ast = None
         if init is True:
@@ -128,44 +128,44 @@ def resetable(self):
 
 
 class FormulaGroup(object):
+    """
+
+    Parameters
+    ----------
+    formula: str or List[Dict] or List[Formula]
+        latex formula string or the parsed abstracted syntax tree or a group of parsed abstracted syntax tree
+    variable_standardization
+    const_mathord
+    detach
+
+    Examples
+    ---------
+    >>> fg = FormulaGroup(["x + y", "y + x", "z + x"])
+    >>> fg
+    <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
+    >>> fg = FormulaGroup(["x + y", Formula("y + x"), "z + x"])
+    >>> fg
+    <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
+    >>> fg = FormulaGroup(["x", Formula("y"), "x"])
+    >>> fg.elements
+    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None},\
+{'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
+    >>> fg = FormulaGroup(["x", Formula("y"), "x"], variable_standardization=True)
+    >>> fg.elements
+    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, \
+{'id': 1, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, \
+{'id': 2, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
+
+    Returns
+    --------
+    the parsed abstracted syntax forest
+    """
     def __init__(self,
                  formula_list: (list, List[str], List[Formula]),
                  variable_standardization=False,
                  const_mathord=None,
                  detach=True
                  ):
-        """
-
-        Parameters
-        ----------
-        formula: str or List[Dict] or List[Formula]
-            latex formula string or the parsed abstracted syntax tree or a group of parsed abstracted syntax tree
-        variable_standardization
-        const_mathord
-        detach
-
-        Examples
-        ---------
-        >>> fg = FormulaGroup(["x + y", "y + x", "z + x"])
-        >>> fg
-        <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
-        >>> fg = FormulaGroup(["x + y", Formula("y + x"), "z + x"])
-        >>> fg
-        <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
-        >>> fg = FormulaGroup(["x", Formula("y"), "x"])
-        >>> fg.elements
-        [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None},\
-    {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
-        >>> fg = FormulaGroup(["x", Formula("y"), "x"], variable_standardization=True)
-        >>> fg.elements
-        [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, \
-    {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, \
-    {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
-
-        Returns
-        --------
-        the parsed abstracted syntax forest
-        """
         forest = []
         self._formulas = []
         for formula in formula_list:

From f41f31100240c1008fba515b7020515e5574ef87 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 19:22:14 +0800
Subject: [PATCH 057/149] [DOC]Update documents

---
 EduNLP/ModelZoo/utils/masker.py | 48 ++++++++++++++++-----------------
 EduNLP/ModelZoo/utils/padder.py | 26 +++++++++---------
 2 files changed, 36 insertions(+), 38 deletions(-)

diff --git a/EduNLP/ModelZoo/utils/masker.py b/EduNLP/ModelZoo/utils/masker.py
index 4e56d738..1dc563f6 100644
--- a/EduNLP/ModelZoo/utils/masker.py
+++ b/EduNLP/ModelZoo/utils/masker.py
@@ -6,31 +6,6 @@
 
 
 class Masker(object):
-    """
-    Examples
-    -------
-    >>> masker = Masker(per=0.5, seed=10)
-    >>> items = [[1, 1, 3, 4, 6], [2], [5, 9, 1, 4]]
-    >>> masked_seq, mask_label = masker(items)
-    >>> masked_seq
-    [[1, 1, 0, 0, 6], [2], [0, 9, 0, 4]]
-    >>> mask_label
-    [[0, 0, 1, 1, 0], [0], [1, 0, 1, 0]]
-    >>> items = [[1, 2, 3], [1, 1, 0], [2, 0, 0]]
-    >>> masked_seq, mask_label = masker(items, [3, 2, 1])
-    >>> masked_seq
-    [[1, 0, 3], [0, 1, 0], [2, 0, 0]]
-    >>> mask_label
-    [[0, 1, 0], [1, 0, 0], [0, 0, 0]]
-    >>> masker = Masker(mask="[MASK]", per=0.5, seed=10)
-    >>> items = [["a", "b", "c"], ["d", "[PAD]", "[PAD]"], ["hello", "world", "[PAD]"]]
-    >>> masked_seq, mask_label = masker(items, length=[3, 1, 2])
-    >>> masked_seq
-    [['a', '[MASK]', 'c'], ['d', '[PAD]', '[PAD]'], ['hello', '[MASK]', '[PAD]']]
-    >>> mask_label
-    [[0, 1, 0], [0, 0, 0], [0, 1, 0]]
-    """
-
     def __init__(self, mask: (int, str, ...) = 0, per=0.2, seed=None):
         """
 
@@ -39,6 +14,29 @@ def __init__(self, mask: (int, str, ...) = 0, per=0.2, seed=None):
         mask: int, str
         per
         seed
+
+        Examples
+        -------
+        >>> masker = Masker(per=0.5, seed=10)
+        >>> items = [[1, 1, 3, 4, 6], [2], [5, 9, 1, 4]]
+        >>> masked_seq, mask_label = masker(items)
+        >>> masked_seq
+        [[1, 1, 0, 0, 6], [2], [0, 9, 0, 4]]
+        >>> mask_label
+        [[0, 0, 1, 1, 0], [0], [1, 0, 1, 0]]
+        >>> items = [[1, 2, 3], [1, 1, 0], [2, 0, 0]]
+        >>> masked_seq, mask_label = masker(items, [3, 2, 1])
+        >>> masked_seq
+        [[1, 0, 3], [0, 1, 0], [2, 0, 0]]
+        >>> mask_label
+        [[0, 1, 0], [1, 0, 0], [0, 0, 0]]
+        >>> masker = Masker(mask="[MASK]", per=0.5, seed=10)
+        >>> items = [["a", "b", "c"], ["d", "[PAD]", "[PAD]"], ["hello", "world", "[PAD]"]]
+        >>> masked_seq, mask_label = masker(items, length=[3, 1, 2])
+        >>> masked_seq
+        [['a', '[MASK]', 'c'], ['d', '[PAD]', '[PAD]'], ['hello', '[MASK]', '[PAD]']]
+        >>> mask_label
+        [[0, 1, 0], [0, 0, 0], [0, 1, 0]]
         """
         self.seed = np.random.default_rng(seed)
         self.per = per
diff --git a/EduNLP/ModelZoo/utils/padder.py b/EduNLP/ModelZoo/utils/padder.py
index 52a662ee..3f8b7693 100644
--- a/EduNLP/ModelZoo/utils/padder.py
+++ b/EduNLP/ModelZoo/utils/padder.py
@@ -5,21 +5,21 @@
 
 
 class PadSequence(object):
-    """Pad the sequence.
-
-    Pad the sequence to the given `length` by inserting `pad_val`. If `clip` is set,
-    sequence that has length larger than `length` will be clipped.
+    def __init__(self, length, pad_val=0, clip=True):
+        """
+        Pad the sequence.
 
-    Parameters
-    ----------
-    length : int
-        The maximum length to pad/clip the sequence
-    pad_val : number
-        The pad value. Default 0
-    clip : bool
-    """
+        Pad the sequence to the given `length` by inserting `pad_val`. If `clip` is set,
+        sequence that has length larger than `length` will be clipped.
 
-    def __init__(self, length, pad_val=0, clip=True):
+        Parameters
+        ----------
+        length : int
+            The maximum length to pad/clip the sequence
+        pad_val : number
+            The pad value. Default 0
+        clip : bool
+        """
         self._length = length
         self._pad_val = pad_val
         self._clip = clip

From b7f4a81184359e83f5de5105a3bc44d190086131 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 19:29:30 +0800
Subject: [PATCH 058/149] [DOC]Update documents

---
 EduNLP/ModelZoo/utils/masker.py | 79 +++++++++++++++------------------
 EduNLP/ModelZoo/utils/padder.py | 40 +++++++----------
 2 files changed, 52 insertions(+), 67 deletions(-)

diff --git a/EduNLP/ModelZoo/utils/masker.py b/EduNLP/ModelZoo/utils/masker.py
index 1dc563f6..18e5369b 100644
--- a/EduNLP/ModelZoo/utils/masker.py
+++ b/EduNLP/ModelZoo/utils/masker.py
@@ -6,56 +6,47 @@
 
 
 class Masker(object):
+    """
+
+    Parameters
+    ----------
+    mask: int, str
+    per
+    seed
+
+    Examples
+    -------
+    >>> masker = Masker(per=0.5, seed=10)
+    >>> items = [[1, 1, 3, 4, 6], [2], [5, 9, 1, 4]]
+    >>> masked_seq, mask_label = masker(items)
+    >>> masked_seq
+    [[1, 1, 0, 0, 6], [2], [0, 9, 0, 4]]
+    >>> mask_label
+    [[0, 0, 1, 1, 0], [0], [1, 0, 1, 0]]
+    >>> items = [[1, 2, 3], [1, 1, 0], [2, 0, 0]]
+    >>> masked_seq, mask_label = masker(items, [3, 2, 1])
+    >>> masked_seq
+    [[1, 0, 3], [0, 1, 0], [2, 0, 0]]
+    >>> mask_label
+    [[0, 1, 0], [1, 0, 0], [0, 0, 0]]
+    >>> masker = Masker(mask="[MASK]", per=0.5, seed=10)
+    >>> items = [["a", "b", "c"], ["d", "[PAD]", "[PAD]"], ["hello", "world", "[PAD]"]]
+    >>> masked_seq, mask_label = masker(items, length=[3, 1, 2])
+    >>> masked_seq
+    [['a', '[MASK]', 'c'], ['d', '[PAD]', '[PAD]'], ['hello', '[MASK]', '[PAD]']]
+    >>> mask_label
+    [[0, 1, 0], [0, 0, 0], [0, 1, 0]]
+
+    Returns
+    ----------
+    list:list of masked_seq and list of masked_list
+    """
     def __init__(self, mask: (int, str, ...) = 0, per=0.2, seed=None):
-        """
-
-        Parameters
-        ----------
-        mask: int, str
-        per
-        seed
-
-        Examples
-        -------
-        >>> masker = Masker(per=0.5, seed=10)
-        >>> items = [[1, 1, 3, 4, 6], [2], [5, 9, 1, 4]]
-        >>> masked_seq, mask_label = masker(items)
-        >>> masked_seq
-        [[1, 1, 0, 0, 6], [2], [0, 9, 0, 4]]
-        >>> mask_label
-        [[0, 0, 1, 1, 0], [0], [1, 0, 1, 0]]
-        >>> items = [[1, 2, 3], [1, 1, 0], [2, 0, 0]]
-        >>> masked_seq, mask_label = masker(items, [3, 2, 1])
-        >>> masked_seq
-        [[1, 0, 3], [0, 1, 0], [2, 0, 0]]
-        >>> mask_label
-        [[0, 1, 0], [1, 0, 0], [0, 0, 0]]
-        >>> masker = Masker(mask="[MASK]", per=0.5, seed=10)
-        >>> items = [["a", "b", "c"], ["d", "[PAD]", "[PAD]"], ["hello", "world", "[PAD]"]]
-        >>> masked_seq, mask_label = masker(items, length=[3, 1, 2])
-        >>> masked_seq
-        [['a', '[MASK]', 'c'], ['d', '[PAD]', '[PAD]'], ['hello', '[MASK]', '[PAD]']]
-        >>> mask_label
-        [[0, 1, 0], [0, 0, 0], [0, 1, 0]]
-        """
         self.seed = np.random.default_rng(seed)
         self.per = per
         self.mask = mask
 
     def __call__(self, seqs, length=None, *args, **kwargs) -> tuple:
-        """
-
-        Parameters
-        ----------
-        seqs:list
-        length
-        args
-        kwargs
-
-        Returns
-        ----------
-        list:list of masked_seq and list of masked_list
-        """
         seqs = deepcopy(seqs)
         masked_list = []
         if length is None:
diff --git a/EduNLP/ModelZoo/utils/padder.py b/EduNLP/ModelZoo/utils/padder.py
index 3f8b7693..443eb374 100644
--- a/EduNLP/ModelZoo/utils/padder.py
+++ b/EduNLP/ModelZoo/utils/padder.py
@@ -5,36 +5,30 @@
 
 
 class PadSequence(object):
-    def __init__(self, length, pad_val=0, clip=True):
-        """
-        Pad the sequence.
+    """
+    Pad the sequence.
+
+    Pad the sequence to the given `length` by inserting `pad_val`. If `clip` is set,
+    sequence that has length larger than `length` will be clipped.
 
-        Pad the sequence to the given `length` by inserting `pad_val`. If `clip` is set,
-        sequence that has length larger than `length` will be clipped.
+    Parameters
+    ----------
+    length : int
+        The maximum length to pad/clip the sequence
+    pad_val : number
+        The pad value. Default 0
+    clip : bool
 
-        Parameters
-        ----------
-        length : int
-            The maximum length to pad/clip the sequence
-        pad_val : number
-            The pad value. Default 0
-        clip : bool
-        """
+    Returns
+    -------
+    ret : list of number
+    """
+    def __init__(self, length, pad_val=0, clip=True):
         self._length = length
         self._pad_val = pad_val
         self._clip = clip
 
     def __call__(self, sample: list):
-        """
-
-        Parameters
-        ----------
-        sample : list of number
-
-        Returns
-        -------
-        ret : list of number
-        """
         sample_length = len(sample)
         if sample_length >= self._length:
             if self._clip and sample_length > self._length:

From ee5d77d3808e5191c95b7ed168eb244b8bed4820 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 19:55:05 +0800
Subject: [PATCH 059/149] [DOC]Update documents

---
 EduNLP/Vector/gensim_vec.py | 57 +++++++++++++++++++++++++++++++------
 1 file changed, 48 insertions(+), 9 deletions(-)

diff --git a/EduNLP/Vector/gensim_vec.py b/EduNLP/Vector/gensim_vec.py
index 13c5600a..fc763794 100644
--- a/EduNLP/Vector/gensim_vec.py
+++ b/EduNLP/Vector/gensim_vec.py
@@ -11,16 +11,18 @@
 
 
 class W2V(Vector):
+    """
+
+    Parameters
+    ----------
+    filepath:
+        path to the pretrained model file
+    method: str
+        fasttext
+        other(Word2Vec)
+    binary
+    """
     def __init__(self, filepath, method=None, binary=None):
-        """
-
-        Parameters
-        ----------
-        filepath:
-            path to the pretrained model file
-        method
-        binary
-        """
         fp = PurePath(filepath)
         self.binary = binary if binary is not None else (True if fp.suffix == ".bin" else False)
         if self.binary is True:
@@ -70,6 +72,22 @@ def infer_tokens(self, items, *args, **kwargs) -> list:
 
 
 class BowLoader(object):
+    """
+    Using doc2bow model, which has a lot of effects.
+
+    Convert document (a list of words) into the bag-of-words format = list of \
+    (token_id, token_count) 2-tuples. Each word is assumed to be a \
+    tokenized and normalized string (either unicode or utf8-encoded). \
+    No further preprocessing is done on the words in document;\
+     apply tokenization, stemming etc. before calling this method.
+
+    If allow_update is set, then also update dictionary in the process: \
+    create ids for new words. At the same time, update document frequencies – \
+    for each word appearing in this document, increase its document frequency (self.dfs) by one.
+
+    If allow_update is not set, this function is const, \
+    aka read-only.
+    """
     def __init__(self, filepath):
         self.dictionary = corpora.Dictionary.load(filepath)
 
@@ -88,6 +106,11 @@ def vector_size(self):
 
 
 class TfidfLoader(object):
+    """
+    This module implements functionality related to the Term Frequency - \
+    Inverse Document Frequency <https://en.wikipedia.org/wiki/Tf%E2%80%93idf> \
+    vector space bag-of-words models.
+    """
     def __init__(self, filepath):
         self.tfidf_model = TfidfModel.load(filepath)
         # 'tfidf' model shold be used based on 'bow' model
@@ -111,6 +134,22 @@ def vector_size(self):
 
 
 class D2V(Vector):
+    """
+    It is a collection which include d2v, bow, tfidf method.
+
+    Parameters
+    -----------
+    filepath
+    method: str
+        d2v
+        bow
+        tfidf
+    item
+
+    Returns
+    ---------
+    D2V
+    """
     def __init__(self, filepath, method="d2v"):
         self._method = method
         self._filepath = filepath

From c36ca763770987c8e024e8d8b9fd6692695c41f8 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 21:20:15 +0800
Subject: [PATCH 060/149] [DOC]update documents

---
 EduNLP/I2V/i2v.py             | 63 +++++++++++++++++------------------
 EduNLP/SIF/segment/segment.py | 30 ++++++++---------
 docs/source/api/sif.rst       |  2 +-
 3 files changed, 47 insertions(+), 48 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 8e1fa3b4..e7b2dd93 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -11,45 +11,44 @@
 
 
 class I2V(object):
+    """
+    It just a api, so you shouldn't use it directly. \
+    If you want to get vector from item, you can use other model like D2V and W2V.
 
-    def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrained_t2v=False, **kwargs):
-        """
-        It just a api, so you shouldn't use it directly. \
-        If you want to get vector from item, you can use other model like D2V and W2V.
-
-        Parameters
-        -----------
-        tokenizer: str
-            the tokenizer name
-        t2v: str
-            the name of token2vector model
-        args:
-            the parameters passed to t2v
-        tokenizer_kwargs: dict
-            the parameters passed to tokenizer
-        pretrained_t2v: bool
+    Parameters
+    -----------
+    tokenizer: str
+        the tokenizer name
+    t2v: str
+        the name of token2vector model
+    args:
+        the parameters passed to t2v
+    tokenizer_kwargs: dict
+        the parameters passed to tokenizer
+    pretrained_t2v: bool
 
-            True: use pretrained t2v model
+        True: use pretrained t2v model
 
-            False: use your own t2v model
+        False: use your own t2v model
 
-        kwargs:
-            the parameters passed to t2v
+    kwargs:
+        the parameters passed to t2v
 
-        Examples
-        --------
-        >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
-        ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
-        ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
-        >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
-        >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
-        >>> i2v(item) # doctest: +ELLIPSIS
-        ([array([...], dtype=float32)], None)
+    Examples
+    --------
+    >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \
+    ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\
+    ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"}
+    >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
+    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
+    >>> i2v(item) # doctest: +ELLIPSIS
+    ([array([...], dtype=float32)], None)
 
-        Returns
-        -------
-        i2v model: I2V
+    Returns
+    -------
+    i2v model: I2V
     """
+    def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrained_t2v=False, **kwargs):
         self.tokenizer: Tokenizer = get_tokenizer(tokenizer, **tokenizer_kwargs if tokenizer_kwargs is not None else {})
         if pretrained_t2v:
             logger.info("Use pretrained t2v model %s" % t2v)
diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index 6d6550d0..f93efbf0 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -80,24 +80,24 @@ class SepSegment(str):
 
 
 class SegmentList(object):
-    def __init__(self, item, figures: dict = None):
-        """
+    """
 
-        Parameters
-        ----------
-        item
-        figures:dict
+    Parameters
+    ----------
+    item
+    figures:dict
 
-        Returns
-        ----------
-        list:tokenizated item
+    Returns
+    ----------
+    list:tokenizated item
 
-        Examples
-        --------
-        >>> test_item = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
-        >>> SegmentList(test_item)
-        ['如图所示，则三角形', 'ABC', '的面积是', '\\\\SIFBlank', '。', \\FigureID{1}]
-        """
+    Examples
+    --------
+    >>> test_item = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
+    >>> SegmentList(test_item)
+    ['如图所示，则三角形', 'ABC', '的面积是', '\\\\SIFBlank', '。', \\FigureID{1}]
+    """
+    def __init__(self, item, figures: dict = None):
         self._segments = []
         self._text_segments = []
         self._formula_segments = []
diff --git a/docs/source/api/sif.rst b/docs/source/api/sif.rst
index 4931807c..95f355fb 100644
--- a/docs/source/api/sif.rst
+++ b/docs/source/api/sif.rst
@@ -17,7 +17,7 @@ Segment
 
 Parser
 --------
-.. automodule:: EduNLP.SIF.parser.parser.Parser
+.. automodule:: EduNLP.SIF.parser.parser
    :members:
    :imported-members:
 

From 8661bd1289c1fd3f099595b0f94ec5f600b74430 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 22:05:21 +0800
Subject: [PATCH 061/149] [DOC]fix pytest

---
 EduNLP/Formula/Formula.py | 5 +++--
 EduNLP/I2V/i2v.py         | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index 73cfd09a..23f709fc 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -148,8 +148,9 @@ class FormulaGroup(object):
     <FormulaGroup: <Formula: x + y>;<Formula: y + x>;<Formula: z + x>>
     >>> fg = FormulaGroup(["x", Formula("y"), "x"])
     >>> fg.elements
-    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None},\
-{'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
+    [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, \
+    {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None}, \
+    {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
     >>> fg = FormulaGroup(["x", Formula("y"), "x"], variable_standardization=True)
     >>> fg.elements
     [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, \
diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 2a732b14..3b58401f 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -42,7 +42,7 @@ class I2V(object):
     >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS
     >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS
     >>> i2v(item) # doctest: +ELLIPSIS
-    ([array([...], dtype=float32)], None)
+    ([array([...dtype=float32)], None)
 
     Returns
     -------

From 7a1a659d8bc47c4f920e9ab3d1777644c81ad1ab Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Fri, 10 Sep 2021 22:17:01 +0800
Subject: [PATCH 062/149] fix pytext

---
 EduNLP/Formula/Formula.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index 23f709fc..6b0cc38d 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -149,8 +149,8 @@ class FormulaGroup(object):
     >>> fg = FormulaGroup(["x", Formula("y"), "x"])
     >>> fg.elements
     [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, \
-    {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None}, \
-    {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
+{'id': 1, 'type': 'mathord', 'text': 'y', 'role': None}, \
+{'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}]
     >>> fg = FormulaGroup(["x", Formula("y"), "x"], variable_standardization=True)
     >>> fg.elements
     [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, \

From d5aa55c3f67bcc9d2eaa49262a039765345fc32e Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 01:12:37 +0800
Subject: [PATCH 063/149] update pic

---
 .../\346\265\201\347\250\213\345\233\276.svg" | 315 ++++++++++++++++++
 docs/source/tutorial/zh/index.rst             |   4 +
 2 files changed, 319 insertions(+)
 create mode 100644 "docs/source/_static/\346\265\201\347\250\213\345\233\276.svg"

diff --git "a/docs/source/_static/\346\265\201\347\250\213\345\233\276.svg" "b/docs/source/_static/\346\265\201\347\250\213\345\233\276.svg"
new file mode 100644
index 00000000..2a599f85
--- /dev/null
+++ "b/docs/source/_static/\346\265\201\347\250\213\345\233\276.svg"
@@ -0,0 +1,315 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- 由 Microsoft Visio, SVG Export 生成 流程图.svg Page-1 -->
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ev="http://www.w3.org/2001/xml-events"
+		xmlns:v="http://schemas.microsoft.com/visio/2003/SVGExtensions/" width="23.8031in" height="11.6929in"
+		viewBox="0 0 1713.83 841.89" xml:space="preserve" color-interpolation-filters="sRGB" class="st11">
+	<v:documentProperties v:langID="2052" v:metric="true" v:viewMarkup="false">
+		<v:userDefs>
+			<v:ud v:nameU="msvNoAutoConnect" v:val="VT0(1):26"/>
+		</v:userDefs>
+	</v:documentProperties>
+
+	<style type="text/css">
+	<![CDATA[
+		.st1 {fill:#ffffff;stroke:#000000;stroke-linecap:round;stroke-linejoin:round;stroke-width:0.75}
+		.st2 {fill:#000000;font-family:宋体;font-size:1.00001em}
+		.st3 {font-size:1em}
+		.st4 {marker-end:url(#mrkr1-41);stroke:#000000;stroke-linecap:round;stroke-linejoin:round;stroke-width:1.5}
+		.st5 {fill:#000000;fill-opacity:1;stroke:#000000;stroke-opacity:1;stroke-width:0.37313432835821}
+		.st6 {fill:none;stroke:none;stroke-linecap:round;stroke-linejoin:round;stroke-width:0.75}
+		.st7 {fill:#ffffff;stroke:#000000;stroke-linecap:round;stroke-linejoin:round;stroke-width:0.24}
+		.st8 {fill:#000000;font-family:宋体;font-size:0.666664em}
+		.st9 {marker-start:url(#mrkr1-108);stroke:#000000;stroke-linecap:round;stroke-linejoin:round;stroke-width:1.5}
+		.st10 {stroke:#000000;stroke-linecap:round;stroke-linejoin:round;stroke-width:1.5}
+		.st11 {fill:none;fill-rule:evenodd;font-size:12px;overflow:visible;stroke-linecap:square;stroke-miterlimit:3}
+	]]>
+	</style>
+
+	<defs id="Markers">
+		<g id="lend1">
+			<path d="M 1 -1 L 0 0 L 1 1 " style="stroke-linecap:round;stroke-linejoin:round;fill:none"/>
+		</g>
+		<marker id="mrkr1-41" class="st5" v:arrowType="1" v:arrowSize="2" orient="auto" markerUnits="strokeWidth"
+				overflow="visible">
+			<use xlink:href="#lend1" transform="scale(-2.68,-2.68) "/>
+		</marker>
+		<marker id="mrkr1-108" class="st5" v:arrowType="1" v:arrowSize="2" orient="auto" markerUnits="strokeWidth"
+				overflow="visible">
+			<use xlink:href="#lend1" transform="scale(2.68) "/>
+		</marker>
+	</defs>
+	<g v:mID="0" v:index="1" v:groupContext="foregroundPage">
+		<title>页-1</title>
+		<v:pageProperties v:drawingScale="0.0393701" v:pageScale="0.0393701" v:drawingUnits="24" v:shadowOffsetX="8.50394"
+				v:shadowOffsetY="-8.50394"/>
+		<v:layer v:name="容器" v:index="0"/>
+		<v:layer v:name="连接线" v:index="1"/>
+		<v:layer v:name="流程图" v:index="2"/>
+		<g id="shape6-1" v:mID="6" v:groupContext="shape" transform="translate(644.315,-566.929)">
+			<title>工作表.6</title>
+			<desc>语义成分分解 使用dic2str4sci模块</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="77.9528" cy="806.457" width="155.91" height="70.8661"/>
+			<rect x="0" y="771.024" width="155.906" height="70.8661" class="st1"/>
+			<text x="41.95" y="802.86" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>语义成分分解<v:newlineChar/><tspan
+						x="8.95" dy="1.2em" class="st3">（</tspan>使用dic2str4sci模块）</text>		</g>
+		<g id="shape7-5" v:mID="7" v:groupContext="shape" transform="translate(658.488,-439.37)">
+			<title>工作表.7</title>
+			<desc>文本结构解析 使用parse模块</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="806.457" width="127.56" height="70.8661"/>
+			<rect x="0" y="771.024" width="127.559" height="70.8661" class="st1"/>
+			<text x="27.78" y="802.86" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>文本结构解析<v:newlineChar/><tspan
+						x="12.78" dy="1.2em" class="st3">（</tspan>使用parse模块）</text>		</g>
+		<g id="shape9-9" v:mID="9" v:groupContext="shape" transform="translate(871.087,-439.37)">
+			<title>工作表.9</title>
+			<desc>结构成分分解 使用segment模块</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="806.457" width="127.56" height="70.8661"/>
+			<rect x="0" y="771.024" width="127.559" height="70.8661" class="st1"/>
+			<text x="27.78" y="802.86" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>结构成分分解<v:newlineChar/><tspan
+						x="6.78" dy="1.2em" class="st3">（</tspan>使用segment模块）</text>		</g>
+		<g id="shape10-13" v:mID="10" v:groupContext="shape" transform="translate(1175.81,-439.37)">
+			<title>工作表.10</title>
+			<desc>令牌化 使用tokenize模块</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="77.9528" cy="806.457" width="155.91" height="70.8661"/>
+			<rect x="0" y="771.024" width="155.906" height="70.8661" class="st1"/>
+			<text x="59.95" y="802.86" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>令牌化<v:newlineChar/><tspan
+						x="17.95" dy="1.2em" class="st3">（</tspan>使用tokenize模块）</text>		</g>
+		<g id="shape11-17" v:mID="11" v:groupContext="shape" transform="translate(1296.28,-680.315)">
+			<title>工作表.11</title>
+			<desc>Formula模块处理</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="806.457" width="127.56" height="70.8661"/>
+			<rect x="0" y="771.024" width="127.559" height="70.8661" class="st1"/>
+			<text x="18.78" y="810.06" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Formula模块处理</text>		</g>
+		<g id="shape12-20" v:mID="12" v:groupContext="shape" transform="translate(1189.98,-566.929)">
+			<title>工作表.12</title>
+			<desc>Text模块处理</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="806.457" width="127.56" height="70.8661"/>
+			<rect x="0" y="771.024" width="127.559" height="70.8661" class="st1"/>
+			<text x="27.78" y="810.06" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Text模块处理</text>		</g>
+		<g id="shape13-23" v:mID="13" v:groupContext="shape" transform="translate(1189.98,-198.425)">
+			<title>工作表.13</title>
+			<desc>向量化 可使用I2V、D2V、W2V、T2V等模块</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="806.457" width="127.56" height="70.8661"/>
+			<rect x="0" y="771.024" width="127.559" height="70.8661" class="st1"/>
+			<text x="45.78" y="795.66" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>向量化<v:newlineChar/><tspan
+						x="9.78" dy="1.2em" class="st3">（</tspan>可使用I2V、D2V、<tspan x="15.78" dy="1.2em" class="st3">W</tspan>2V、T2V等模块）</text>		</g>
+		<g id="shape14-28" v:mID="14" v:groupContext="shape" transform="translate(899.433,-311.811)">
+			<title>工作表.14</title>
+			<desc>预训练模型 使用get_pretrain_i2v模块</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="77.9528" cy="806.457" width="155.91" height="70.8661"/>
+			<rect x="0" y="771.024" width="155.906" height="70.8661" class="st1"/>
+			<text x="47.95" y="795.66" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>预训练模型<v:newlineChar/><tspan
+						x="5.95" dy="1.2em" class="st3">（</tspan>使用get_pretrain_i2v模<tspan x="65.95" dy="1.2em" class="st3">块</tspan>）</text>		</g>
+		<g id="shape17-33" v:mID="17" v:groupContext="shape" transform="translate(757.701,-198.425)">
+			<title>工作表.17</title>
+			<desc>下游模型</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="806.457" width="127.56" height="70.8661"/>
+			<rect x="0" y="771.024" width="127.559" height="70.8661" class="st1"/>
+			<text x="39.78" y="810.06" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>下游模型</text>		</g>
+		<g id="shape19-36" v:mID="19" v:groupContext="shape" transform="translate(1557.07,274.961) rotate(90)">
+			<title>工作表.19</title>
+			<path d="M0 841.89 L56.69 841.89" class="st4"/>
+		</g>
+		<g id="shape20-42" v:mID="20" v:groupContext="shape" transform="translate(786.047,-473.684)">
+			<title>工作表.20</title>
+			<path d="M0 841.89 L85.04 841.89" class="st4"/>
+		</g>
+		<g id="shape21-47" v:mID="21" v:groupContext="shape" v:layerMember="1" transform="translate(998.646,-467.717)">
+			<title>动态连接线</title>
+			<path d="M0 834.8 L177.17 834.8" class="st4"/>
+		</g>
+		<g id="shape22-52" v:mID="22" v:groupContext="shape" v:layerMember="1" transform="translate(927.78,-510.236)">
+			<title>动态连接线.22</title>
+			<path d="M7.09 841.89 L7.09 765.35" class="st4"/>
+		</g>
+		<g id="shape23-57" v:mID="23" v:groupContext="shape" transform="translate(858.866,-592.362)">
+			<title>工作表.23</title>
+			<desc>Seged_item</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
+			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
+			<text x="46" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Seged_item</text>		</g>
+		<g id="shape24-60" v:mID="24" v:groupContext="shape" transform="translate(1002.55,-476.063)">
+			<title>工作表.24</title>
+			<desc>Seged_item</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
+			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
+			<text x="46" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Seged_item</text>		</g>
+		<g id="shape25-63" v:mID="25" v:groupContext="shape" transform="translate(752.567,-453.543)">
+			<title>工作表.25</title>
+			<desc>解析后的item</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
+			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
+			<text x="40" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>解析后的item</text>		</g>
+		<g id="shape26-66" v:mID="26" v:groupContext="shape" transform="translate(559.276,-601.989)">
+			<title>工作表.26</title>
+			<path d="M0 841.89 L85.04 841.89" class="st4"/>
+		</g>
+		<g id="shape27-71" v:mID="27" v:groupContext="shape" transform="translate(438.803,-592.362)">
+			<title>工作表.27</title>
+			<desc>字典形式的item</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
+			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
+			<text x="21.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>字典形式的item</text>		</g>
+		<g id="shape28-74" v:mID="28" v:groupContext="shape" transform="translate(452.976,-464.803)">
+			<title>工作表.28</title>
+			<desc>字符串形式的item</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
+			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
+			<text x="15.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>字符串形式的item</text>		</g>
+		<g id="shape29-77" v:mID="29" v:groupContext="shape" transform="translate(573.449,-475.176)">
+			<title>工作表.29</title>
+			<path d="M0 841.89 L85.04 841.89" class="st4"/>
+		</g>
+		<g id="shape30-82" v:mID="30" v:groupContext="shape" transform="translate(708.094,-528.583)">
+			<title>工作表.30</title>
+			<desc>处理后的字符串形式的item</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
+			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
+			<text x="9.78" y="828.29" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>处理后的字符串形式<tspan
+						x="45.78" dy="1.2em" class="st3">的</tspan>item</text>		</g>
+		<g id="shape32-86" v:mID="32" v:groupContext="shape" transform="translate(1367.15,-464.803)">
+			<title>工作表.32</title>
+			<desc>Token</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
+			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
+			<text x="61" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Token</text>		</g>
+		<g id="shape33-89" v:mID="33" v:groupContext="shape" transform="translate(1335.62,-472.52)">
+			<title>工作表.33</title>
+			<path d="M0 841.89 L85.04 841.89" class="st4"/>
+		</g>
+		<g id="shape34-94" v:mID="34" v:groupContext="shape" v:layerMember="2" transform="translate(1058.17,-562.677)">
+			<title>判定 2</title>
+			<desc>是否为公式且使用ast进行解析</desc>
+			<v:custProps>
+				<v:cp v:nameU="Cost" v:lbl="成本" v:type="7" v:format="@" v:langID="2052"/>
+				<v:cp v:nameU="Duration" v:lbl="持续时间" v:type="2" v:langID="2052"/>
+				<v:cp v:nameU="Resources" v:lbl="资源" v:langID="2052"/>
+			</v:custProps>
+			<v:userDefs>
+				<v:ud v:nameU="visVersion" v:val="VT0(15):26"/>
+			</v:userDefs>
+			<v:textBlock v:margins="rect(2,2,2,2)" v:tabSpace="42.5197"/>
+			<v:textRect cx="39.685" cy="802.205" width="59.53" height="69.4488"/>
+			<path d="M0 802.2 L39.69 762.52 L79.37 802.2 L39.69 841.89 L0 802.2 Z" class="st7"/>
+			<text x="15.69" y="795" class="st8" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>是否为公式且<tspan x="13.68"
+						dy="1.2em" class="st3">使用</tspan>ast进行解<tspan x="35.69" dy="1.2em" class="st3">析</tspan></text>		</g>
+		<g id="shape35-99" v:mID="35" v:groupContext="shape" v:layerMember="2" transform="translate(1214.08,-311.811)">
+			<title>判定 2.35</title>
+			<desc>是否使用预训练模型</desc>
+			<v:custProps>
+				<v:cp v:nameU="Cost" v:lbl="成本" v:type="7" v:format="@" v:langID="2052"/>
+				<v:cp v:nameU="Duration" v:lbl="持续时间" v:type="2" v:langID="2052"/>
+				<v:cp v:nameU="Resources" v:lbl="资源" v:langID="2052"/>
+			</v:custProps>
+			<v:userDefs>
+				<v:ud v:nameU="visVersion" v:val="VT0(15):26"/>
+			</v:userDefs>
+			<v:textBlock v:margins="rect(2,2,2,2)" v:tabSpace="42.5197"/>
+			<v:textRect cx="39.685" cy="802.205" width="59.53" height="69.4488"/>
+			<path d="M0 802.2 L39.69 762.52 L79.37 802.2 L39.69 841.89 L0 802.2 Z" class="st7"/>
+			<text x="15.69" y="799.8" class="st8" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>是否使用预训<tspan x="27.69"
+						dy="1.2em" class="st3">练模型</tspan></text>		</g>
+		<g id="shape37-103" v:mID="37" v:groupContext="shape" transform="translate(1567.78,-419.322) rotate(33.9298)">
+			<title>工作表.37</title>
+			<path d="M0 841.89 L93.95 841.89" class="st9"/>
+		</g>
+		<g id="shape38-109" v:mID="38" v:groupContext="shape" transform="translate(1137.54,-602.362)">
+			<title>工作表.38</title>
+			<path d="M0 841.89 L52.44 841.89" class="st4"/>
+		</g>
+		<g id="shape39-114" v:mID="39" v:groupContext="shape" v:layerMember="1" transform="translate(1097.86,-642.047)">
+			<title>动态连接线.39</title>
+			<path d="M0 841.89 L0 768.19 L198.43 768.19" class="st4"/>
+		</g>
+		<g id="shape49-119" v:mID="49" v:groupContext="shape" transform="translate(2209.04,161.575) rotate(90)">
+			<title>工作表.49</title>
+			<path d="M0 841.89 L170.08 841.89" class="st10"/>
+		</g>
+		<g id="shape51-122" v:mID="51" v:groupContext="shape" transform="translate(1317.54,-604.227)">
+			<title>工作表.51</title>
+			<path d="M0 841.89 L49.61 841.89" class="st4"/>
+		</g>
+		<g id="shape52-127" v:mID="52" v:groupContext="shape" transform="translate(1331.72,-510.236)">
+			<title>工作表.52</title>
+			<path d="M0 841.89 L35.43 841.89" class="st9"/>
+		</g>
+		<g id="shape53-132" v:mID="53" v:groupContext="shape" transform="translate(1069.51,-717.165)">
+			<title>工作表.53</title>
+			<desc>是</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
+			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
+			<text x="70" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>是</text>		</g>
+		<g id="shape54-135" v:mID="54" v:groupContext="shape" transform="translate(1087.59,-603.622)">
+			<title>工作表.54</title>
+			<desc>否</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
+			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
+			<text x="70" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>否</text>		</g>
+		<g id="shape55-138" v:mID="55" v:groupContext="shape" transform="translate(2095.65,402.52) rotate(90)">
+			<title>工作表.55</title>
+			<path d="M0 841.89 L48.19 841.89" class="st4"/>
+		</g>
+		<g id="shape56-143" v:mID="56" v:groupContext="shape" transform="translate(1214.08,-405.276)">
+			<title>工作表.56</title>
+			<desc>Token</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
+			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
+			<text x="48.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Token</text>		</g>
+		<g id="shape57-146" v:mID="57" v:groupContext="shape" transform="translate(2095.65,530.079) rotate(90)">
+			<title>工作表.57</title>
+			<path d="M0 841.89 L42.52 841.89" class="st4"/>
+		</g>
+		<g id="shape58-151" v:mID="58" v:groupContext="shape" transform="translate(1214.08,-290.551)">
+			<title>工作表.58</title>
+			<desc>否</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
+			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
+			<text x="57.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>否</text>		</g>
+		<g id="shape59-154" v:mID="59" v:groupContext="shape" transform="translate(1214.08,1332.28) rotate(180)">
+			<title>工作表.59</title>
+			<path d="M0 841.89 L158.74 841.89" class="st4"/>
+		</g>
+		<g id="shape60-159" v:mID="60" v:groupContext="shape" transform="translate(1070.93,-354.331)">
+			<title>工作表.60</title>
+			<desc>是</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
+			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
+			<text x="57.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>是</text>		</g>
+		<g id="shape61-162" v:mID="61" v:groupContext="shape" v:layerMember="1" transform="translate(977.386,-311.811)">
+			<title>动态连接线.61</title>
+			<path d="M0 841.89 L0 919.84 L212.6 919.84" class="st10"/>
+		</g>
+		<g id="shape62-165" v:mID="62" v:groupContext="shape" transform="translate(977.386,1449.92) rotate(180)">
+			<title>工作表.62</title>
+			<path d="M0 841.89 L92.13 841.89" class="st4"/>
+		</g>
+		<g id="shape63-170" v:mID="63" v:groupContext="shape" transform="translate(934.52,-206.772)">
+			<title>工作表.63</title>
+			<desc>静态词向量</desc>
+			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
+			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
+			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
+			<text x="46" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>静态词向量</text>		</g>
+	</g>
+</svg>
diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 11a5cfc4..9b2a8c72 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -13,6 +13,10 @@
 
 *  `向量化 <vectorization.rst>`_ 
 
+主要流程
+----------
+
+.. figure:: ../../../_static/流程图.svg
 
 示例
 --------

From e32011a86ee27640d8519a642f52dcede1372d5c Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 01:19:15 +0800
Subject: [PATCH 064/149] fix pic

---
 docs/source/tutorial/zh/index.rst | 2 +-
 docs/source/tutorial/zh/parse.rst | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 9b2a8c72..ceeb13fb 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -16,7 +16,7 @@
 主要流程
 ----------
 
-.. figure:: ../../../_static/流程图.svg
+.. figure:: ../../_static/流程图.svg
 
 示例
 --------
diff --git a/docs/source/tutorial/zh/parse.rst b/docs/source/tutorial/zh/parse.rst
index 3758dcb5..6c1dfe6a 100644
--- a/docs/source/tutorial/zh/parse.rst
+++ b/docs/source/tutorial/zh/parse.rst
@@ -120,7 +120,7 @@ item为str 或 List[Dict]类型，具体内容为latex 公式 或 公式经解
    >>> plt.show()
 
 
-.. figure:: ../../../_static/formula.png
+.. figure:: ../../_static/formula.png
 
 
 变量标准化
@@ -206,7 +206,7 @@ FormulaGroup
    [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
    >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
 
-.. figure:: ../../../_static/formulagroup.png
+.. figure:: ../../_static/formulagroup.png
 
 
 文本语法结构解析

From 23a76eeac97afc3f1efdc1ce775927ed16e3539d Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 01:26:37 +0800
Subject: [PATCH 065/149] update pic width

---
 docs/source/tutorial/zh/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index ceeb13fb..c52be2b5 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -17,6 +17,7 @@
 ----------
 
 .. figure:: ../../_static/流程图.svg
+    :width: 1200px
 
 示例
 --------

From 263aebf6a50a4457ac4d06b1b5ee76ff9dab135a Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 01:40:36 +0800
Subject: [PATCH 066/149] new pic

---
 ...276\346\224\276\345\244\247\347\211\210.png" | Bin 0 -> 44449 bytes
 docs/source/tutorial/zh/index.rst               |   3 +--
 2 files changed, 1 insertion(+), 2 deletions(-)
 create mode 100644 "docs/source/_static/\346\265\201\347\250\213\345\233\276\346\224\276\345\244\247\347\211\210.png"

diff --git "a/docs/source/_static/\346\265\201\347\250\213\345\233\276\346\224\276\345\244\247\347\211\210.png" "b/docs/source/_static/\346\265\201\347\250\213\345\233\276\346\224\276\345\244\247\347\211\210.png"
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diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index c52be2b5..a4a09c02 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -16,8 +16,7 @@
 主要流程
 ----------
 
-.. figure:: ../../_static/流程图.svg
-    :width: 1200px
+.. figure:: ../../_static/流程图放大版.png
 
 示例
 --------

From 1ff0c80c6823e7cc4e2f02bceaac8fa03889f1c0 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 08:52:39 +0800
Subject: [PATCH 067/149] new dic

---
 .../\346\265\201\347\250\213\345\233\276.jpg" | Bin 0 -> 282900 bytes
 .../\346\265\201\347\250\213\345\233\276.svg" | 315 ------------------
 ...6\346\224\276\345\244\247\347\211\210.png" | Bin 44449 -> 0 bytes
 docs/source/tutorial/zh/index.rst             |   2 +-
 4 files changed, 1 insertion(+), 316 deletions(-)
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 delete mode 100644 "docs/source/_static/\346\265\201\347\250\213\345\233\276.svg"
 delete mode 100644 "docs/source/_static/\346\265\201\347\250\213\345\233\276\346\224\276\345\244\247\347\211\210.png"

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-			<title>工作表.6</title>
-			<desc>语义成分分解 使用dic2str4sci模块</desc>
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-			<title>工作表.7</title>
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-			<title>工作表.20</title>
-			<path d="M0 841.89 L85.04 841.89" class="st4"/>
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-		<g id="shape21-47" v:mID="21" v:groupContext="shape" v:layerMember="1" transform="translate(998.646,-467.717)">
-			<title>动态连接线</title>
-			<path d="M0 834.8 L177.17 834.8" class="st4"/>
-		</g>
-		<g id="shape22-52" v:mID="22" v:groupContext="shape" v:layerMember="1" transform="translate(927.78,-510.236)">
-			<title>动态连接线.22</title>
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-		<g id="shape23-57" v:mID="23" v:groupContext="shape" transform="translate(858.866,-592.362)">
-			<title>工作表.23</title>
-			<desc>Seged_item</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
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-			<text x="46" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Seged_item</text>		</g>
-		<g id="shape24-60" v:mID="24" v:groupContext="shape" transform="translate(1002.55,-476.063)">
-			<title>工作表.24</title>
-			<desc>Seged_item</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
-			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
-			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
-			<text x="46" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Seged_item</text>		</g>
-		<g id="shape25-63" v:mID="25" v:groupContext="shape" transform="translate(752.567,-453.543)">
-			<title>工作表.25</title>
-			<desc>解析后的item</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
-			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
-			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
-			<text x="40" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>解析后的item</text>		</g>
-		<g id="shape26-66" v:mID="26" v:groupContext="shape" transform="translate(559.276,-601.989)">
-			<title>工作表.26</title>
-			<path d="M0 841.89 L85.04 841.89" class="st4"/>
-		</g>
-		<g id="shape27-71" v:mID="27" v:groupContext="shape" transform="translate(438.803,-592.362)">
-			<title>工作表.27</title>
-			<desc>字典形式的item</desc>
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-			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
-			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
-			<text x="21.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>字典形式的item</text>		</g>
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-			<title>工作表.28</title>
-			<desc>字符串形式的item</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
-			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
-			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
-			<text x="15.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>字符串形式的item</text>		</g>
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-			<title>工作表.29</title>
-			<path d="M0 841.89 L85.04 841.89" class="st4"/>
-		</g>
-		<g id="shape30-82" v:mID="30" v:groupContext="shape" transform="translate(708.094,-528.583)">
-			<title>工作表.30</title>
-			<desc>处理后的字符串形式的item</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
-			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
-			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
-			<text x="9.78" y="828.29" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>处理后的字符串形式<tspan
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-		<g id="shape32-86" v:mID="32" v:groupContext="shape" transform="translate(1367.15,-464.803)">
-			<title>工作表.32</title>
-			<desc>Token</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
-			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
-			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
-			<text x="61" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Token</text>		</g>
-		<g id="shape33-89" v:mID="33" v:groupContext="shape" transform="translate(1335.62,-472.52)">
-			<title>工作表.33</title>
-			<path d="M0 841.89 L85.04 841.89" class="st4"/>
-		</g>
-		<g id="shape34-94" v:mID="34" v:groupContext="shape" v:layerMember="2" transform="translate(1058.17,-562.677)">
-			<title>判定 2</title>
-			<desc>是否为公式且使用ast进行解析</desc>
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-				<v:cp v:nameU="Duration" v:lbl="持续时间" v:type="2" v:langID="2052"/>
-				<v:cp v:nameU="Resources" v:lbl="资源" v:langID="2052"/>
-			</v:custProps>
-			<v:userDefs>
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-			<v:textRect cx="39.685" cy="802.205" width="59.53" height="69.4488"/>
-			<path d="M0 802.2 L39.69 762.52 L79.37 802.2 L39.69 841.89 L0 802.2 Z" class="st7"/>
-			<text x="15.69" y="795" class="st8" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>是否为公式且<tspan x="13.68"
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-			<title>判定 2.35</title>
-			<desc>是否使用预训练模型</desc>
-			<v:custProps>
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-				<v:cp v:nameU="Duration" v:lbl="持续时间" v:type="2" v:langID="2052"/>
-				<v:cp v:nameU="Resources" v:lbl="资源" v:langID="2052"/>
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-			<v:userDefs>
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-			<text x="15.69" y="799.8" class="st8" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>是否使用预训<tspan x="27.69"
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-			<title>工作表.37</title>
-			<path d="M0 841.89 L93.95 841.89" class="st9"/>
-		</g>
-		<g id="shape38-109" v:mID="38" v:groupContext="shape" transform="translate(1137.54,-602.362)">
-			<title>工作表.38</title>
-			<path d="M0 841.89 L52.44 841.89" class="st4"/>
-		</g>
-		<g id="shape39-114" v:mID="39" v:groupContext="shape" v:layerMember="1" transform="translate(1097.86,-642.047)">
-			<title>动态连接线.39</title>
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-		</g>
-		<g id="shape49-119" v:mID="49" v:groupContext="shape" transform="translate(2209.04,161.575) rotate(90)">
-			<title>工作表.49</title>
-			<path d="M0 841.89 L170.08 841.89" class="st10"/>
-		</g>
-		<g id="shape51-122" v:mID="51" v:groupContext="shape" transform="translate(1317.54,-604.227)">
-			<title>工作表.51</title>
-			<path d="M0 841.89 L49.61 841.89" class="st4"/>
-		</g>
-		<g id="shape52-127" v:mID="52" v:groupContext="shape" transform="translate(1331.72,-510.236)">
-			<title>工作表.52</title>
-			<path d="M0 841.89 L35.43 841.89" class="st9"/>
-		</g>
-		<g id="shape53-132" v:mID="53" v:groupContext="shape" transform="translate(1069.51,-717.165)">
-			<title>工作表.53</title>
-			<desc>是</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
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-			<text x="70" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>是</text>		</g>
-		<g id="shape54-135" v:mID="54" v:groupContext="shape" transform="translate(1087.59,-603.622)">
-			<title>工作表.54</title>
-			<desc>否</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
-			<v:textRect cx="76" cy="831.89" width="152" height="20"/>
-			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
-			<text x="70" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>否</text>		</g>
-		<g id="shape55-138" v:mID="55" v:groupContext="shape" transform="translate(2095.65,402.52) rotate(90)">
-			<title>工作表.55</title>
-			<path d="M0 841.89 L48.19 841.89" class="st4"/>
-		</g>
-		<g id="shape56-143" v:mID="56" v:groupContext="shape" transform="translate(1214.08,-405.276)">
-			<title>工作表.56</title>
-			<desc>Token</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
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-			<text x="48.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>Token</text>		</g>
-		<g id="shape57-146" v:mID="57" v:groupContext="shape" transform="translate(2095.65,530.079) rotate(90)">
-			<title>工作表.57</title>
-			<path d="M0 841.89 L42.52 841.89" class="st4"/>
-		</g>
-		<g id="shape58-151" v:mID="58" v:groupContext="shape" transform="translate(1214.08,-290.551)">
-			<title>工作表.58</title>
-			<desc>否</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
-			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
-			<rect x="0" y="821.89" width="127.559" height="20" class="st6"/>
-			<text x="57.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>否</text>		</g>
-		<g id="shape59-154" v:mID="59" v:groupContext="shape" transform="translate(1214.08,1332.28) rotate(180)">
-			<title>工作表.59</title>
-			<path d="M0 841.89 L158.74 841.89" class="st4"/>
-		</g>
-		<g id="shape60-159" v:mID="60" v:groupContext="shape" transform="translate(1070.93,-354.331)">
-			<title>工作表.60</title>
-			<desc>是</desc>
-			<v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/>
-			<v:textRect cx="63.7795" cy="831.89" width="127.56" height="20"/>
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-			<text x="57.78" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>是</text>		</g>
-		<g id="shape61-162" v:mID="61" v:groupContext="shape" v:layerMember="1" transform="translate(977.386,-311.811)">
-			<title>动态连接线.61</title>
-			<path d="M0 841.89 L0 919.84 L212.6 919.84" class="st10"/>
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-		<g id="shape62-165" v:mID="62" v:groupContext="shape" transform="translate(977.386,1449.92) rotate(180)">
-			<title>工作表.62</title>
-			<path d="M0 841.89 L92.13 841.89" class="st4"/>
-		</g>
-		<g id="shape63-170" v:mID="63" v:groupContext="shape" transform="translate(934.52,-206.772)">
-			<title>工作表.63</title>
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-			<rect x="0" y="821.89" width="152" height="20" class="st6"/>
-			<text x="46" y="835.49" class="st2" v:langID="2052"><v:paragraph v:horizAlign="1"/><v:tabList/>静态词向量</text>		</g>
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diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index a4a09c02..ef452a79 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -16,7 +16,7 @@
 主要流程
 ----------
 
-.. figure:: ../../_static/流程图放大版.png
+.. figure:: ../../_static/流程图.jpg
 
 示例
 --------

From 08c06091a02c0d06102f5314207eaa8688b88d66 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 10:05:33 +0800
Subject: [PATCH 068/149] update flow chart

---
 .../\346\265\201\347\250\213\345\233\276.jpg" | Bin 282900 -> 345727 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git "a/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg" "b/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg"
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From f7683f68d3f0456a836cfcfa3b562979fa1a3a6f Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 10:13:26 +0800
Subject: [PATCH 069/149] [DOC]Update documents

---
 docs/source/tutorial/zh/pretrain.rst |  9 ++++-----
 docs/source/tutorial/zh/tokenize.rst | 10 +++++-----
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/docs/source/tutorial/zh/pretrain.rst b/docs/source/tutorial/zh/pretrain.rst
index c7c5e697..a6d1477d 100644
--- a/docs/source/tutorial/zh/pretrain.rst
+++ b/docs/source/tutorial/zh/pretrain.rst
@@ -16,6 +16,10 @@
    from EduNLP.I2V import get_pretrained_i2v
    from EduNLP.Vector import get_pretrained_t2v
 
+训练模型
+------------
+
+如需训练模型则可直接train_vector函数接口，来使使训练模型更加方便。模块调用gensim库中的相关训练模型，目前提供了"sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf"的训练方法，并提供了embedding_dim参数，使之可以按照需求确定向量的维度。
 
 装载模型
 --------
@@ -29,11 +33,6 @@ Examples：
    >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
 
-训练模型
-------------
-
-如需训练模型则可直接train_vector函数接口，来使使训练模型更加方便。模块调用gensim库中的相关训练模型，目前提供了"sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf"的训练方法，并提供了embedding_dim参数，使之可以按照需求确定向量的维度。
-
 基本步骤
 ##################
 
diff --git a/docs/source/tutorial/zh/tokenize.rst b/docs/source/tutorial/zh/tokenize.rst
index fa54a870..4982f031 100644
--- a/docs/source/tutorial/zh/tokenize.rst
+++ b/docs/source/tutorial/zh/tokenize.rst
@@ -140,6 +140,11 @@ PureTextTokenizer
    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
    '\\quad', 'A', '\\cap', 'B', '=']
 
+GensimWordTokenizer
++++++++++++++++++++++++
+
+此令牌解析器在默认情况下对传入的item中的图片、题目空缺符等部分转换成特殊字符进行保护，从而对文本、公式、标签、分隔符进行令牌化操作。此外，从令牌化方法而言，此令牌解析器对文本均采用线性的分析方法，而对公式采用抽象语法树的分析方法，提供了general参数可供使用者选择：当general为true的时候则代表着传入的item并非标准格式，此时对公式也使用线性的分析方法；当general为false时则代表使用抽象语法树的方法对公式进行解析。
+
 GensimSegTokenizer
 ++++++++++++++++++++
 
@@ -150,11 +155,6 @@ GensimSegTokenizer
 * 提供了切分深度的选项，即可以在sep标签或者tag标签处进行切割
 * 默认在item组分（如text、formula）的头部插入开始标签
 
-GensimWordTokenizer
-+++++++++++++++++++++++
-
-此令牌解析器在默认情况下对传入的item中的图片、题目空缺符等部分转换成特殊字符进行保护，从而对文本、公式、标签、分隔符进行令牌化操作。此外，从令牌化方法而言，此令牌解析器对文本均采用线性的分析方法，而对公式采用抽象语法树的分析方法，提供了general参数可供使用者选择：当general为true的时候则代表着传入的item并非标准格式，此时对公式也使用线性的分析方法；当general为false时则代表使用抽象语法树的方法对公式进行解析。
-
 Examples
 ----------
         

From 4299e0beee96222e33f4b01e1525f1ef0126a820 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 10:25:03 +0800
Subject: [PATCH 070/149] update flow chart

---
 .../\346\265\201\347\250\213\345\233\276.jpg" | Bin 345727 -> 350294 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git "a/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg" "b/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg"
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From 2c61156db830ed65cb2022901c7fd58b8eca4058 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 10:33:39 +0800
Subject: [PATCH 071/149] [DOC]Update document

---
 docs/source/tutorial/zh/pretrain.rst | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/docs/source/tutorial/zh/pretrain.rst b/docs/source/tutorial/zh/pretrain.rst
index a6d1477d..ff4d4fed 100644
--- a/docs/source/tutorial/zh/pretrain.rst
+++ b/docs/source/tutorial/zh/pretrain.rst
@@ -21,18 +21,6 @@
 
 如需训练模型则可直接train_vector函数接口，来使使训练模型更加方便。模块调用gensim库中的相关训练模型，目前提供了"sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf"的训练方法，并提供了embedding_dim参数，使之可以按照需求确定向量的维度。
 
-装载模型
---------
-
-将所得到的模型传入I2V模块即可装载模型
- 
-Examples：
-
-::
-
-   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
-   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
-
 基本步骤
 ##################
 
@@ -54,6 +42,18 @@ Examples：
    train_vector(sif_items, "../../../data/w2v/gensim_luna_stem_tf_", 10, method="d2v")
 
 
+装载模型
+--------
+
+将所得到的模型传入I2V模块即可装载模型
+ 
+Examples：
+
+::
+
+   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+
 公开模型一览
 ------------
 

From 94e32b46db8bfd51efa389932019f9a9123c1436 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 10:42:21 +0800
Subject: [PATCH 072/149] update flow chart

---
 .../\346\265\201\347\250\213\345\233\276.jpg" | Bin 350294 -> 350119 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git "a/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg" "b/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg"
index 7563e254bbd9d270145408f50024e02f35eb1066..dfdb8737dc71801a62fb0b5b4a371bf6e3e93888 100644
GIT binary patch
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From 9b72e3247c572489d113045eb3d222792a19694a Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Sat, 11 Sep 2021 16:52:47 +0800
Subject: [PATCH 073/149] [FEATURE] change the usage of is_sif,to_sif,sif4sci

---
 EduNLP/SIF/sif.py          | 75 ++++++++++++++++++++++++--------------
 tests/test_sif/test_sif.py |  8 +++-
 2 files changed, 55 insertions(+), 28 deletions(-)

diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index 494eee77..309b25a7 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -10,51 +10,65 @@
 __all__ = ["is_sif", "to_sif", "sif4sci"]
 
 
-def is_sif(item, check_formula=True):
+def is_sif(item, check_formula=True, cache=False):
     r"""
     Parameters
     ----------
-    item
+    item: str
     check_formula: bool
-        True if check the validity of formulas in items
-        False if not check the validity of formulas in items, which is faster
+        whether to check the formulas when parsing item.
+
+        True if check the validity of formulas in item
+        False if not check the validity of formulas in item, which is faster
+    cache: bool
+        whether to put the parsed item  in return.
+
+        when True, the format of return is (bool, Parser)
+        when False, the format of return is bool
 
     Returns
     -------
     when item can not be parsed correctly, raise Error;
-    when item doesn't need to be modified, return Ture and original item ;
-    when item needs to be modified, return False and modified item;
+    when item is in stardarded format originally, return Ture (and the Parser of item);
+    when item isn't in stardarded format originally, return False (and the Parser of item);
 
     Examples
     --------
     >>> text = '若$x,y$满足约束条件' \
     ...        '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' \
     ...        '则$z=x+7 y$的最大值$\\SIFUnderline$'
-    >>> flag, _ = is_sif(text)
-    >>> print(flag)
+    >>> is_sif(text)
     True
     >>> text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
-    >>> is_sif(text)
-    (False, '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...')
+    >>> ret = is_sif(text, cache=True)
+    >>> ret # doctest: +ELLIPSIS
+    (False, <EduNLP.SIF.parser.parser.Parser object...>)
     """
     item_parser = Parser(item, check_formula)
     item_parser.description_list()
     if item_parser.fomula_illegal_flag:
         raise ValueError(item_parser.fomula_illegal_message)
-    if item_parser.error_flag == 0 and item_parser.modify_flag == 0:
-        return True, item
-    return False, item_parser.text
+    ret = True if item_parser.error_flag == 0 and item_parser.modify_flag == 0 else False
+    if cache is True:
+        return ret, item_parser
+    else:
+        return ret
 
 
-def to_sif(item, check_formula=True):
+def to_sif(item, check_formula=True, cache_parser: Parser = None):
     r"""
     Parameters
     ----------
-    item
+    item: str
+    check_formula: bool
+        whether to check the formulas when parsing item (only work when cache_parser=None).
+    cache_parser: Parser
+        the saved parser of item from is_sif.
 
     Returns
     -------
-    item
+    str
+        the parsed item
 
     Examples
     --------
@@ -62,9 +76,17 @@ def to_sif(item, check_formula=True):
     >>> siftext = to_sif(text)
     >>> siftext
     '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'
+    >>> ret = is_sif(text, cache=True)
+    >>> ret # doctest: +ELLIPSIS
+    (False, <EduNLP.SIF.parser.parser.Parser object...>)
+    >>> to_sif(text, cache_parser=ret[1])
+    '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...
+
     """
-    _, sif_item = is_sif(item, check_formula)
-    return sif_item
+    if cache_parser is not None:
+        return cache_parser.text
+    else:
+        return is_sif(item, check_formula, cache=True)[1].text
 
 
 def sif4sci(item: str, figures: (dict, bool) = None, safe_mode: int = 2, symbol: str = None, tokenization=True,
@@ -87,9 +109,9 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe_mode: int = 2, symbol:
         The combination of "t","f","g","m","a","s", which determine what types of segments to be symbolize.
         "t": text,
         "f": formula,
-        "g": figuew,
+        "g": figure,
         "m": mask,
-        "a": tab,
+        "a": tag,
         "s": sep
     tokenization: bool
         whether to tokenize item after segmentation
@@ -213,13 +235,12 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe_mode: int = 2, symbol:
     [['已知'], ['说法', '中', '正确']]
     """
     try:
-        if safe_mode == 2:
-            _, item = is_sif(item, check_formula=True)
-        elif safe_mode == 1:
-            _, item = is_sif(item, check_formula=False)
-        elif safe_mode == 0:
-            pass  # do nothing
-        else:
+        if safe_mode in [1, 2]:
+            check_formula = True if safe_mode == 1 else False
+            sif, item_parser = is_sif(item, check_formula=check_formula, cache=True)
+            if sif is not True:
+                item = to_sif(item, cache_parser=item_parser)
+        elif safe_mode != 0:
             raise KeyError(
                 "Unknown safe_mode %s, use only 0 or 1 or 2." % safe_mode
             )
diff --git a/tests/test_sif/test_sif.py b/tests/test_sif/test_sif.py
index 53ec2663..b596e927 100644
--- a/tests/test_sif/test_sif.py
+++ b/tests/test_sif/test_sif.py
@@ -11,7 +11,7 @@ def test_is_sif():
     text = '若$x,y$满足约束条件' \
            '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' \
            '则$z=x+7 y$的最大值$\\SIFUnderline$'
-    assert is_sif(text)[0] == 1
+    assert is_sif(text) == 1
 
     text = '公式需要满足完整性，完整的公式如' \
            '$\\begin{matrix} a & b \\\\ c & d \\end{matrix}$' \
@@ -31,6 +31,12 @@ def test_to_sif():
     siftext = to_sif(text)
     print(siftext)
 
+    ret = is_sif(text, cache=True)
+    assert ret[0] == 0
+    if ret[0] is not True:
+        siftext = to_sif(text, cache_parser=ret[1])
+    print(siftext)
+
 
 def test_sci4sif(figure0, figure1, figure0_base64, figure1_base64):
     repr(sif4sci(

From 00b4a3a68ee40e07be2b32fdd9f8bbcb4a738687 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 18:47:48 +0800
Subject: [PATCH 074/149] update api

---
 EduNLP/Formula/Formula.py       | 8 ++------
 EduNLP/ModelZoo/rnn/rnn.py      | 2 +-
 EduNLP/ModelZoo/utils/masker.py | 2 +-
 3 files changed, 4 insertions(+), 8 deletions(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index 6b0cc38d..71038480 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -15,6 +15,7 @@
 
 class Formula(object):
     """
+    The part transform a formula to the parsed abstracted syntax tree.
 
     Parameters
     ----------
@@ -41,9 +42,6 @@ class Formula(object):
     >>> f.elements
     [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
 
-    Returns
-    --------
-    the parsed abstracted syntax tree
     """
     def __init__(self, formula: (str, List[Dict]), variable_standardization=False, const_mathord=None,
                  init=True, *args, **kwargs):
@@ -129,6 +127,7 @@ def resetable(self):
 
 class FormulaGroup(object):
     """
+    The part transform a group of formula to the parsed abstracted syntax forest.
 
     Parameters
     ----------
@@ -157,9 +156,6 @@ class FormulaGroup(object):
 {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, \
 {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
 
-    Returns
-    --------
-    the parsed abstracted syntax forest
     """
     def __init__(self,
                  formula_list: (list, List[str], List[Formula]),
diff --git a/EduNLP/ModelZoo/rnn/rnn.py b/EduNLP/ModelZoo/rnn/rnn.py
index 97585170..6234869e 100644
--- a/EduNLP/ModelZoo/rnn/rnn.py
+++ b/EduNLP/ModelZoo/rnn/rnn.py
@@ -91,7 +91,7 @@ def forward(self, seq_idx, seq_len):
 
         Returns
         --------
-        a PackedSequence object
+            a PackedSequence object
         """
         seq = self.embedding(seq_idx)
         pack = pack_padded_sequence(seq, seq_len, batch_first=True)
diff --git a/EduNLP/ModelZoo/utils/masker.py b/EduNLP/ModelZoo/utils/masker.py
index 18e5369b..0c4a7f66 100644
--- a/EduNLP/ModelZoo/utils/masker.py
+++ b/EduNLP/ModelZoo/utils/masker.py
@@ -15,7 +15,7 @@ class Masker(object):
     seed
 
     Examples
-    -------
+    ---------
     >>> masker = Masker(per=0.5, seed=10)
     >>> items = [[1, 1, 3, 4, 6], [2], [5, 9, 1, 4]]
     >>> masked_seq, mask_label = masker(items)

From d3f6ae2f4b574a659c8619b340afba19be1c0b96 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 21:43:34 +0800
Subject: [PATCH 075/149] [DOC]Update documents

---
 EduNLP/ModelZoo/rnn/rnn.py  |  1 +
 EduNLP/SIF/sif.py           | 12 +++++++-----
 EduNLP/Vector/gensim_vec.py |  2 +-
 docs/source/api/sif.rst     |  2 +-
 4 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/EduNLP/ModelZoo/rnn/rnn.py b/EduNLP/ModelZoo/rnn/rnn.py
index 6234869e..ce3a2d34 100644
--- a/EduNLP/ModelZoo/rnn/rnn.py
+++ b/EduNLP/ModelZoo/rnn/rnn.py
@@ -91,6 +91,7 @@ def forward(self, seq_idx, seq_len):
 
         Returns
         --------
+        sequence
             a PackedSequence object
         """
         seq = self.embedding(seq_idx)
diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index 013ce87b..b4b1be9f 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -19,9 +19,10 @@ def is_sif(item):
 
     Returns
     -------
-    when item can not be parsed correctly, raise Error;
-    when item doesn't need to be modified, return Ture;
-    when item needs to be modified, return False;
+    bool
+        when item can not be parsed correctly, raise Error;
+        when item doesn't need to be modified, return Ture;
+        when item needs to be modified, return False;
 
     Examples
     --------
@@ -113,8 +114,9 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = No
 
     Returns
     -------
-    When tokenization is False, return SegmentList;
-    When tokenization is True, return TokenList
+    list
+        When tokenization is False, return SegmentList;
+        When tokenization is True, return TokenList
 
     Examples
     --------
diff --git a/EduNLP/Vector/gensim_vec.py b/EduNLP/Vector/gensim_vec.py
index ece3a077..0e2bb16c 100644
--- a/EduNLP/Vector/gensim_vec.py
+++ b/EduNLP/Vector/gensim_vec.py
@@ -148,7 +148,7 @@ class D2V(Vector):
 
     Returns
     ---------
-    D2V
+    D2V:d2v model
     """
     def __init__(self, filepath, method="d2v"):
         self._method = method
diff --git a/docs/source/api/sif.rst b/docs/source/api/sif.rst
index 95f355fb..4931807c 100644
--- a/docs/source/api/sif.rst
+++ b/docs/source/api/sif.rst
@@ -17,7 +17,7 @@ Segment
 
 Parser
 --------
-.. automodule:: EduNLP.SIF.parser.parser
+.. automodule:: EduNLP.SIF.parser.parser.Parser
    :members:
    :imported-members:
 

From 00129e31d294e8f7b02e9a2205dbe23726923851 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 21:54:32 +0800
Subject: [PATCH 076/149] update vector api

---
 EduNLP/Vector/gensim_vec.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/EduNLP/Vector/gensim_vec.py b/EduNLP/Vector/gensim_vec.py
index 0e2bb16c..b34ac3a0 100644
--- a/EduNLP/Vector/gensim_vec.py
+++ b/EduNLP/Vector/gensim_vec.py
@@ -148,7 +148,7 @@ class D2V(Vector):
 
     Returns
     ---------
-    D2V:d2v model
+    d2v model:D2V
     """
     def __init__(self, filepath, method="d2v"):
         self._method = method

From 234ce56aac74749d1063db45563b06efcd5427f8 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 22:25:13 +0800
Subject: [PATCH 077/149] [DOC]Update documents

---
 EduNLP/ModelZoo/utils/masker.py                 | 3 ++-
 EduNLP/ModelZoo/utils/padder.py                 | 6 ++++--
 EduNLP/SIF/segment/segment.py                   | 6 ++++--
 EduNLP/SIF/tokenization/formula/linear_token.py | 1 +
 EduNLP/SIF/tokenization/tokenization.py         | 6 ++++--
 5 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/EduNLP/ModelZoo/utils/masker.py b/EduNLP/ModelZoo/utils/masker.py
index 0c4a7f66..00ba5df9 100644
--- a/EduNLP/ModelZoo/utils/masker.py
+++ b/EduNLP/ModelZoo/utils/masker.py
@@ -39,7 +39,8 @@ class Masker(object):
 
     Returns
     ----------
-    list:list of masked_seq and list of masked_list
+    list
+        list of masked_seq and list of masked_list
     """
     def __init__(self, mask: (int, str, ...) = 0, per=0.2, seed=None):
         self.seed = np.random.default_rng(seed)
diff --git a/EduNLP/ModelZoo/utils/padder.py b/EduNLP/ModelZoo/utils/padder.py
index 443eb374..29407893 100644
--- a/EduNLP/ModelZoo/utils/padder.py
+++ b/EduNLP/ModelZoo/utils/padder.py
@@ -21,7 +21,8 @@ class PadSequence(object):
 
     Returns
     -------
-    ret : list of number
+    ret 
+        list of number
     """
     def __init__(self, length, pad_val=0, clip=True):
         self._length = length
@@ -53,7 +54,8 @@ def pad_sequence(sequence: list, max_length=None, pad_val=0, clip=True):
 
     Returns
     -------
-    Modified list:padding the sequence in the same size.
+    Modified list:list
+        padding the sequence in the same size.
 
     Examples
     --------
diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index f93efbf0..d706c29d 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -89,7 +89,8 @@ class SegmentList(object):
 
     Returns
     ----------
-    list:tokenizated item
+    list
+        tokenizated item
 
     Examples
     --------
@@ -276,7 +277,8 @@ def seg(item, figures=None, symbol=None):
 
     Returns
     -------
-    list:segmented item
+    list
+        segmented item
 
     Examples
     --------
diff --git a/EduNLP/SIF/tokenization/formula/linear_token.py b/EduNLP/SIF/tokenization/formula/linear_token.py
index 6775da9f..69a01e66 100644
--- a/EduNLP/SIF/tokenization/formula/linear_token.py
+++ b/EduNLP/SIF/tokenization/formula/linear_token.py
@@ -24,6 +24,7 @@ def cut(formula, preserve_braces=True, with_dollar=False,
     Returns
     --------
     list
+        return a preliminary list which cut fully
 
     Examples
     ----------
diff --git a/EduNLP/SIF/tokenization/tokenization.py b/EduNLP/SIF/tokenization/tokenization.py
index 49f31416..7ad746b8 100644
--- a/EduNLP/SIF/tokenization/tokenization.py
+++ b/EduNLP/SIF/tokenization/tokenization.py
@@ -358,7 +358,8 @@ def filter(self, drop: (set, str) = "", keep: (set, str) = "*"):
 
         Returns
         --------
-        filted list
+        list
+            filted list
         """
         _drop = {c for c in drop} if isinstance(drop, str) else drop
         if keep == "*":
@@ -407,7 +408,8 @@ def tokenize(segment_list: SegmentList, text_params=None, formula_params=None, f
 
     Returns
     ----------
-    tokenized item
+    list
+        tokenized item
 
     Examples
     --------

From 289f1aa5bb6be3a2ab6a8947e8722b41e4a67b40 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 11 Sep 2021 22:37:05 +0800
Subject: [PATCH 078/149] fix pytest error

---
 EduNLP/ModelZoo/utils/padder.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/EduNLP/ModelZoo/utils/padder.py b/EduNLP/ModelZoo/utils/padder.py
index 29407893..57f6219b 100644
--- a/EduNLP/ModelZoo/utils/padder.py
+++ b/EduNLP/ModelZoo/utils/padder.py
@@ -21,7 +21,7 @@ class PadSequence(object):
 
     Returns
     -------
-    ret 
+    ret
         list of number
     """
     def __init__(self, length, pad_val=0, clip=True):

From cf61733a67e7c72d9ec65d8490635c24d39abd6b Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 12 Sep 2021 19:36:34 +0800
Subject: [PATCH 079/149] delete api/index

---
 docs/source/index.rst | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 337dce47..fc7daac5 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -164,7 +164,6 @@ If this repository is helpful for you, please cite our work
    :hidden:
    :glob:
 
-   api/index
    api/i2v
    api/sif
    api/tokenizer

From ea06f03d505bf6e9a690b342a1f9436756e596d2 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 12 Sep 2021 21:55:18 +0800
Subject: [PATCH 080/149] Update doc

---
 EduNLP/Pretrain/gensim_vec.py                   |   6 +-----
 EduNLP/SIF/sif.py                               |   8 ++++----
 ...260\346\265\201\347\250\213\345\233\276.png" | Bin 0 -> 25991 bytes
 docs/source/index.rst                           |   6 +++---
 docs/source/tutorial/zh/index.rst               |  15 ++++++++++++++-
 5 files changed, 22 insertions(+), 13 deletions(-)
 create mode 100644 "docs/source/_static/\346\226\260\346\265\201\347\250\213\345\233\276.png"

diff --git a/EduNLP/Pretrain/gensim_vec.py b/EduNLP/Pretrain/gensim_vec.py
index cd0823b7..c119e8c2 100644
--- a/EduNLP/Pretrain/gensim_vec.py
+++ b/EduNLP/Pretrain/gensim_vec.py
@@ -194,11 +194,7 @@ def train_vector(items, w2v_prefix, embedding_dim=None, method="sg", binary=None
     ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
     >>> print(token_item[:10])
     [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
-    >>> tokenizer = GensimSegTokenizer(symbol="fgm", depth=None)
-    >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-    >>> print(token_item[:10])
-    [['公式'], ['[FORMULA]'], ['如图'], ['[FIGURE]'], ['[FORMULA]'],...['最大值'], ['[MARK]']]
+    >>> train_vector(token_item[:10], "../../../data/w2v/gensim_luna_stem_t_", 100) #doctest: +ELLIPSIS
     """
     monitor = MonitorCallback(["word", "I", "less"])
     _train_params = dict(
diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index b4b1be9f..2ef96899 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -15,7 +15,7 @@ def is_sif(item):
     Parameters
     ----------
     item:str
-        the text of question
+        a raw item which respects stem
 
     Returns
     -------
@@ -49,12 +49,12 @@ def to_sif(item):
     Parameters
     ----------
     items:str
-        the text of question
+        a raw item which respects stem
 
     Returns
     -------
     item:str
-        the question's text after sif correction
+        the item which accords with sif format
 
     Examples
     --------
@@ -78,7 +78,7 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = No
     Parameters
     ----------
     item:str
-        the text of question
+        a raw item which respects stem
     figures:dict
         {"FigureID": Base64 encoding of the figure}
 
diff --git "a/docs/source/_static/\346\226\260\346\265\201\347\250\213\345\233\276.png" "b/docs/source/_static/\346\226\260\346\265\201\347\250\213\345\233\276.png"
new file mode 100644
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diff --git a/docs/source/index.rst b/docs/source/index.rst
index fc7daac5..27e9ed8e 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -164,12 +164,12 @@ If this repository is helpful for you, please cite our work
    :hidden:
    :glob:
 
-   api/i2v
    api/sif
-   api/tokenizer
+   api/utils
    api/formula
+   api/tokenizer
    api/pretrain
    api/ModelZoo
+   api/i2v
    api/vector
-   api/utils
    
diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index ef452a79..4e3ed42b 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -16,7 +16,20 @@
 主要流程
 ----------
 
-.. figure:: ../../_static/流程图.jpg
+.. figure:: ../../_static/新流程图.png
+
+* **语法解析**：此部分主要调用的是parse模块，其作用是将传入的item转换为标准sif格式（即把字母、数字用 ``$...$`` 包裹起来，把选择填空的括号、下划线转换为特殊符号等），从而方便后期的处理。
+
+* **成分分解**：此部分主要调用的是segment模块，其作用是将传入的符合sif标准的item根据元素种类进行分割开来，从而服务于后面的令牌化环节（即可以将不同类型元素使用各自的方法令牌化）。
+
+* **令牌化**：此部分主要调用的是tokenize模块，其作用是将传入的经过分词后的item元素列表进行令牌化分解，从而服务于后面的向量化模块。
+  其中通常情况下直接使用文本形式的令牌化方法即可，对于公式而言还可使用ast方法进行解析(调用formula模块)；
+  此外，本项目还提供了令牌化容器，从而方便用户快速获得item相应的令牌化结果。
+
+* **向量化**：此部分主要调用的是I2V类及其子类，其作用是将传入的令牌化后的item元素列表进行向量化操作，最终即可得到相应的静态向量。
+  对于向量化模块来说，可以调用自己训练好的模型，也可直接调用提供的预训练模型（调用get_pretrained_i2v模块即可）。
+
+* **下游模型**：将得到的向量进一步处理，从而得到所需的结果。
 
 示例
 --------

From dd656580cf3a226d41b1468b284b2cb3ef69c6ed Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 12 Sep 2021 22:14:33 +0800
Subject: [PATCH 081/149] [DOC]Update documents

---
 EduNLP/Pretrain/gensim_vec.py     | 3 ++-
 docs/source/tutorial/zh/index.rst | 7 +++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/EduNLP/Pretrain/gensim_vec.py b/EduNLP/Pretrain/gensim_vec.py
index c119e8c2..b72b4f7b 100644
--- a/EduNLP/Pretrain/gensim_vec.py
+++ b/EduNLP/Pretrain/gensim_vec.py
@@ -194,7 +194,8 @@ def train_vector(items, w2v_prefix, embedding_dim=None, method="sg", binary=None
     ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
     >>> print(token_item[:10])
     [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
-    >>> train_vector(token_item[:10], "../../../data/w2v/gensim_luna_stem_t_", 100) #doctest: +ELLIPSIS
+    >>> train_vector(token_item[:10], "../../data/w2v/gensim_luna_stem_t_", 100) #doctest: +ELLIPSIS
+    '../../data/w2v/gensim_luna_stem_t_sg_100.kv'
     """
     monitor = MonitorCallback(["word", "I", "less"])
     _train_params = dict(
diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 4e3ed42b..572d0459 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -18,13 +18,12 @@
 
 .. figure:: ../../_static/新流程图.png
 
-* **语法解析**：此部分主要调用的是parse模块，其作用是将传入的item转换为标准sif格式（即把字母、数字用 ``$...$`` 包裹起来，把选择填空的括号、下划线转换为特殊符号等），从而方便后期的处理。
+* **语法解析**：其作用是将传入的item转换为标准sif格式（即把字母、数字用 ``$...$`` 包裹起来，把选择填空的括号、下划线转换为特殊符号等）。
 
-* **成分分解**：此部分主要调用的是segment模块，其作用是将传入的符合sif标准的item根据元素种类进行分割开来，从而服务于后面的令牌化环节（即可以将不同类型元素使用各自的方法令牌化）。
+* **成分分解**：其作用是将传入的符合sif标准的item根据元素种类进行分割开来，从而服务于后面的令牌化环节（即可以将不同类型元素使用各自的方法令牌化）。
 
-* **令牌化**：此部分主要调用的是tokenize模块，其作用是将传入的经过分词后的item元素列表进行令牌化分解，从而服务于后面的向量化模块。
+* **令牌化**：其作用是将传入的经过分词后的item元素列表进行令牌化分解，从而服务于后面的向量化模块。
   其中通常情况下直接使用文本形式的令牌化方法即可，对于公式而言还可使用ast方法进行解析(调用formula模块)；
-  此外，本项目还提供了令牌化容器，从而方便用户快速获得item相应的令牌化结果。
 
 * **向量化**：此部分主要调用的是I2V类及其子类，其作用是将传入的令牌化后的item元素列表进行向量化操作，最终即可得到相应的静态向量。
   对于向量化模块来说，可以调用自己训练好的模型，也可直接调用提供的预训练模型（调用get_pretrained_i2v模块即可）。

From 0ffb17ad06decad49bdfd9e7252bc8889b095aa4 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 12 Sep 2021 22:35:05 +0800
Subject: [PATCH 082/149] [DOC]update document

---
 EduNLP/Pretrain/gensim_vec.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/EduNLP/Pretrain/gensim_vec.py b/EduNLP/Pretrain/gensim_vec.py
index b72b4f7b..7f2aad9f 100644
--- a/EduNLP/Pretrain/gensim_vec.py
+++ b/EduNLP/Pretrain/gensim_vec.py
@@ -194,8 +194,8 @@ def train_vector(items, w2v_prefix, embedding_dim=None, method="sg", binary=None
     ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
     >>> print(token_item[:10])
     [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
-    >>> train_vector(token_item[:10], "../../data/w2v/gensim_luna_stem_t_", 100) #doctest: +ELLIPSIS
-    '../../data/w2v/gensim_luna_stem_t_sg_100.kv'
+    >>> train_vector(token_item[:10], "data/w2v/gensim_luna_stem_t_", 100) #doctest: +ELLIPSIS
+    'data/w2v/gensim_luna_stem_t_sg_100.kv'
     """
     monitor = MonitorCallback(["word", "I", "less"])
     _train_params = dict(

From 4a6673afa88549b3de2e0c347edc1ab360e70230 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 12 Sep 2021 22:39:33 +0800
Subject: [PATCH 083/149] fix pytest error

---
 EduNLP/SIF/sif.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index 2ef96899..68787131 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -12,6 +12,8 @@
 
 def is_sif(item):
     r"""
+    the part aims to check whether the input is sif format
+
     Parameters
     ----------
     item:str
@@ -46,6 +48,8 @@ def is_sif(item):
 
 def to_sif(item):
     r"""
+    the part aims to switch item to sif formate
+
     Parameters
     ----------
     items:str

From 3f1e59f3c08dda10a1046214e527d2faca8fa628 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 12 Sep 2021 22:48:01 +0800
Subject: [PATCH 084/149] fix pytest error

---
 EduNLP/Pretrain/gensim_vec.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/EduNLP/Pretrain/gensim_vec.py b/EduNLP/Pretrain/gensim_vec.py
index 7f2aad9f..51d408ae 100644
--- a/EduNLP/Pretrain/gensim_vec.py
+++ b/EduNLP/Pretrain/gensim_vec.py
@@ -194,8 +194,8 @@ def train_vector(items, w2v_prefix, embedding_dim=None, method="sg", binary=None
     ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
     >>> print(token_item[:10])
     [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
-    >>> train_vector(token_item[:10], "data/w2v/gensim_luna_stem_t_", 100) #doctest: +ELLIPSIS
-    'data/w2v/gensim_luna_stem_t_sg_100.kv'
+    >>> train_vector(token_item[:10], "examples/test_model/data/gensim_luna_stem_t_", 100) #doctest: +ELLIPSIS
+    'examples/test_model/data/gensim_luna_stem_t_sg_100.kv'
     """
     monitor = MonitorCallback(["word", "I", "less"])
     _train_params = dict(

From ef008b638c0387fd9a9c546cb43f2741836119e1 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 13 Sep 2021 00:20:16 +0800
Subject: [PATCH 085/149] [DOC]Update documents

---
 EduNLP/I2V/i2v.py                             | 12 ++++-
 EduNLP/SIF/parser/parser.py                   |  2 +-
 EduNLP/SIF/segment/segment.py                 | 10 ++++
 EduNLP/SIF/tokenization/formula/ast_token.py  |  5 ++
 EduNLP/SIF/tokenization/formula/formula.py    |  1 +
 .../SIF/tokenization/formula/linear_token.py  |  3 ++
 EduNLP/SIF/tokenization/text/tokenization.py  |  1 +
 EduNLP/SIF/tokenization/tokenization.py       | 48 ++++++++++++++-----
 8 files changed, 67 insertions(+), 15 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 3b58401f..48e64b15 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -108,8 +108,11 @@ def vector_size(self):
 
 class D2V(I2V):
     """
+    The model aims to transfer item to vector directly.
 
-    Bases: I2V
+    Bases
+    -------
+    I2V
 
     Parameters
     -----------
@@ -144,6 +147,7 @@ class D2V(I2V):
     def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=lambda x: x, *args,
                      **kwargs) -> tuple:
         '''
+        It is a function to switch item to vector. And before using the function, it is nesseary to load model.
 
         Parameters
         -----------
@@ -175,8 +179,11 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
 
 class W2V(I2V):
     """
+    The model aims to transfer tokens to vector.
 
-    Bases: I2V
+    Bases
+    --------
+    I2V
 
     Parameters
     -----------
@@ -209,6 +216,7 @@ class W2V(I2V):
     def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=lambda x: x, *args,
                      **kwargs) -> tuple:
         '''
+        It is a function to switch item to vector. And before using the function, it is nesseary to load model.
 
         Parameters
         -----------
diff --git a/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index cf7cbc30..cd2bf65e 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -3,8 +3,8 @@
 
 
 class Parser:
+    """initial data and special variable"""
     def __init__(self, data):
-        """initial data and special variable"""
         self.lookahead = 0
         self.head = 0
         self.text = data
diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index d706c29d..f85a7cea 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -160,6 +160,7 @@ def append(self, segment) -> None:
 
     @property
     def segments(self):
+        """return segments"""
         if self._seg_idx is None:
             return self._segments
         else:
@@ -167,29 +168,36 @@ def segments(self):
 
     @property
     def text_segments(self):
+        """return text segments"""
         return [self._segments[i] for i in self._text_segments]
 
     @property
     def formula_segments(self):
+        """return formula segments"""
         return [self._segments[i] for i in self._formula_segments]
 
     @property
     def figure_segments(self):
+        """return figure segments"""
         return [self._segments[i] for i in self._figure_segments]
 
     @property
     def ques_mark_segments(self):
+        """return question mark segments"""
         return [self._segments[i] for i in self._ques_mark_segments]
 
     @property
     def tag_segments(self):
+        """return tag segments"""
         return [self._segments[i] for i in self._tag_segments]
 
     def to_symbol(self, idx, symbol):
+        """switch element to its symbol"""
         self._segments[idx] = symbol
 
     def symbolize(self, to_symbolize="fgm"):
         """
+        Switch designated elements to symbol. It is a good way to protect or preserve the elements which we don't want to tokenize.
 
         Parameters
         ----------
@@ -227,6 +235,7 @@ def symbolize(self, to_symbolize="fgm"):
     @contextmanager
     def filter(self, drop: (set, str) = "", keep: (set, str) = "*"):
         """
+        Output special element list selective.Drop means not show.Keep means show.
 
         Parameters
         ----------
@@ -268,6 +277,7 @@ def describe(self):
 
 def seg(item, figures=None, symbol=None):
     r"""
+    It is a interface for SegmentList. And show it in an appropriate way.
 
     Parameters
     ----------
diff --git a/EduNLP/SIF/tokenization/formula/ast_token.py b/EduNLP/SIF/tokenization/formula/ast_token.py
index 67ca8ffd..b113e8b8 100644
--- a/EduNLP/SIF/tokenization/formula/ast_token.py
+++ b/EduNLP/SIF/tokenization/formula/ast_token.py
@@ -35,6 +35,10 @@
 #     return nodes
 
 def traversal_formula(ast, ord2token=False, var_numbering=False, strategy="post", *args, **kwargs):
+    """
+    The part will run only when the return type is list. And it provides two strategy: post and linear. 
+    Besides, tokens list will append node follow its type.
+    """
     tokens = []
     if strategy == "post":
         order = nx.dfs_postorder_nodes(ast)
@@ -58,6 +62,7 @@ def traversal_formula(ast, ord2token=False, var_numbering=False, strategy="post"
 
 def ast_tokenize(formula, ord2token=False, var_numbering=False, return_type="formula", *args, **kwargs):
     """
+    According to return type, tokenizing formula by different methods.
 
     Parameters
     ----------
diff --git a/EduNLP/SIF/tokenization/formula/formula.py b/EduNLP/SIF/tokenization/formula/formula.py
index 8afbe2da..eb08f418 100644
--- a/EduNLP/SIF/tokenization/formula/formula.py
+++ b/EduNLP/SIF/tokenization/formula/formula.py
@@ -9,6 +9,7 @@
 
 def tokenize(formula, method="linear", errors="raise", **kwargs):
     """
+    The total function to tokenize formula by linear or ast.
 
     Parameters
     ----------
diff --git a/EduNLP/SIF/tokenization/formula/linear_token.py b/EduNLP/SIF/tokenization/formula/linear_token.py
index 69a01e66..1e3236bc 100644
--- a/EduNLP/SIF/tokenization/formula/linear_token.py
+++ b/EduNLP/SIF/tokenization/formula/linear_token.py
@@ -7,6 +7,7 @@
 def cut(formula, preserve_braces=True, with_dollar=False,
         preserve_dollar=False, number_as_tag=False, preserve_src=True):  # pragma: no cover
     """
+    cut formula thoroughly
 
     Parameters
     ----------
@@ -233,6 +234,8 @@ def latex_parse(formula, preserve_braces=True, with_dollar=True,
 
 def linear_tokenize(formula, preserve_braces=True, number_as_tag=False, *args, **kwargs):
     """
+    linear tokenize formula.
+    It includes three processes:cut, reduce and connect_char.
 
     Parameters
     ----------
diff --git a/EduNLP/SIF/tokenization/text/tokenization.py b/EduNLP/SIF/tokenization/text/tokenization.py
index cee23a50..2e063f85 100644
--- a/EduNLP/SIF/tokenization/text/tokenization.py
+++ b/EduNLP/SIF/tokenization/text/tokenization.py
@@ -9,6 +9,7 @@
 
 def tokenize(text, granularity="word", stopwords="default"):
     """
+    Using jieba library to tokenize item by word or char.
 
     Parameters
     ----------
diff --git a/EduNLP/SIF/tokenization/tokenization.py b/EduNLP/SIF/tokenization/tokenization.py
index 7ad746b8..53f6fe75 100644
--- a/EduNLP/SIF/tokenization/tokenization.py
+++ b/EduNLP/SIF/tokenization/tokenization.py
@@ -16,22 +16,19 @@
 
 class TokenList(object):
     """
+    Parameters
+    ----------
+    segment_list:list
+        segmented item
+    text_params:dict
+    formula_params:dict
+    figure_params:dict
+
     Attributes
     -------------
 
     """
     def __init__(self, segment_list: SegmentList, text_params=None, formula_params=None, figure_params=None):
-        """
-
-        Parameters
-        ----------
-        segment_list:list
-            segmented item
-        text_params:dict
-        formula_params:dict
-        figure_params:dict
-
-        """
         self._tokens = []
         self._text_tokens = []
         self._formula_tokens = []
@@ -64,6 +61,7 @@ def __init__(self, segment_list: SegmentList, text_params=None, formula_params=N
         self._token_idx = None
 
     def _variable_standardization(self):
+        """It makes same parmeters have the same number."""
         if self.formula_tokenize_method == "ast":
             ast_formulas = [self._tokens[i] for i in self._formula_tokens if isinstance(self._tokens[i], Formula)]
             if ast_formulas:
@@ -72,7 +70,7 @@ def _variable_standardization(self):
     @contextmanager
     def add_seg_type(self, seg_type, tar: list, add_seg_type=True, mode="delimiter"):
         """
-        add seg tag in different position
+        Add seg tag in different position
 
         Parameters
         ----------
@@ -106,6 +104,7 @@ def add_seg_type(self, seg_type, tar: list, add_seg_type=True, mode="delimiter")
     def get_segments(self, add_seg_type=True, add_seg_mode="delimiter", keep="*", drop="",
                      depth=None):  # pragma: no cover
         r"""
+        call segment function.
 
         Parameters
         ----------
@@ -124,6 +123,8 @@ def get_segments(self, add_seg_type=True, add_seg_mode="delimiter", keep="*", dr
 
         Returns
         -------
+        list
+            segmented item
 
         """
         keep = set("tfgmas" if keep == "*" else keep) - set(drop)
@@ -152,6 +153,7 @@ def get_segments(self, add_seg_type=True, add_seg_mode="delimiter", keep="*", dr
         return _segments
 
     def __get_segments(self, seg_type):
+        """It aims to understand letters' meaning."""
         _segments = []
         for i in self._seg_types[seg_type]:
             _segment = []
@@ -164,22 +166,27 @@ def __get_segments(self, seg_type):
 
     @property
     def text_segments(self):
+        """get text segment"""
         return self.__get_segments("t")
 
     @property
     def formula_segments(self):
+        """get formula segment"""
         return self.__get_segments("f")
 
     @property
     def figure_segments(self):
+        """get figure segment"""
         return self.__get_segments("g")
 
     @property
     def ques_mark_segments(self):
+        """get question mark segment"""
         return self.__get_segments("m")
 
     @property
     def tokens(self):
+        """add token to a list"""
         tokens = []
         if self._token_idx is not None:
             for i, token in enumerate(self._tokens):
@@ -191,6 +198,7 @@ def tokens(self):
         return tokens
 
     def append_text(self, segment, symbol=False):
+        """append text"""
         with self._append("t"):
             if symbol is False:
                 tokens = text.tokenize(segment, **self.text_params)
@@ -202,6 +210,7 @@ def append_text(self, segment, symbol=False):
                 self._tokens.append(segment)
 
     def append_formula(self, segment, symbol=False, init=True):
+        """append formula by different methods"""
         with self._append("f"):
             if symbol is True:
                 self._formula_tokens.append(len(self._tokens))
@@ -225,27 +234,32 @@ def append_formula(self, segment, symbol=False, init=True):
                     self._tokens.append(token)
 
     def append_figure(self, segment, **kwargs):
+        """append figure"""
         with self._append("g"):
             self._figure_tokens.append(len(self._tokens))
             self._tokens.append(segment)
 
     def append_ques_mark(self, segment, **kwargs):
+        """append question mark"""
         with self._append("m"):
             self._ques_mark_tokens.append(len(self._tokens))
             self._tokens.append(segment)
 
     def append_tag(self, segment, **kwargs):
+        """append tag"""
         with self._append("a"):
             self._tag_tokens.append(len(self._tokens))
             self._tokens.append(segment)
 
     def append_sep(self, segment, **kwargs):
+        """append sep"""
         with self._append("s"):
             self._sep_tokens.append(len(self._tokens))
             self._tokens.append(segment)
 
     @contextmanager
     def _append(self, seg_type):
+        """It aims to understand letters' meaning."""
         start = len(self._tokens)
         yield
         end = len(self._tokens)
@@ -254,6 +268,7 @@ def _append(self, seg_type):
 
     def append(self, segment, lazy=False):
         """
+        the total api for appending elements
 
         Parameters
         ----------
@@ -295,12 +310,14 @@ def append(self, segment, lazy=False):
             raise TypeError("Unknown segment type: %s" % type(segment))
 
     def extend(self, segments):
+        """append every segment in turn"""
         for segment in segments:
             self.append(segment, True)
         self._variable_standardization()
 
     @property
     def text_tokens(self):
+        """return text tokens"""
         return [self._tokens[i] for i in self._text_tokens]
 
     def __add_token(self, token, tokens):
@@ -322,6 +339,7 @@ def __add_token(self, token, tokens):
 
     @property
     def formula_tokens(self):
+        """return formula tokens"""
         tokens = []
         for i in self._formula_tokens:
             self.__add_token(self._tokens[i], tokens)
@@ -329,6 +347,7 @@ def formula_tokens(self):
 
     @property
     def figure_tokens(self):
+        """return figure tokens"""
         tokens = []
         for i in self._figure_tokens:
             self.__add_token(self._tokens[i], tokens)
@@ -336,6 +355,7 @@ def figure_tokens(self):
 
     @property
     def ques_mark_tokens(self):
+        """return question mark tokens"""
         return [self._tokens[i] for i in self._ques_mark_tokens]
 
     def __repr__(self):
@@ -343,11 +363,13 @@ def __repr__(self):
 
     @property
     def inner_formula_tokens(self):
+        """return inner formula tokens"""
         return [self._tokens[i] for i in self._formula_tokens]
 
     @contextmanager
     def filter(self, drop: (set, str) = "", keep: (set, str) = "*"):
         """
+        Output special element list selective.Drop means not show.Keep means show.
 
         Parameters
         ----------
@@ -383,6 +405,7 @@ def filter(self, drop: (set, str) = "", keep: (set, str) = "*"):
         self._token_idx = None
 
     def describe(self):
+        """show the total number of each elements"""
         return {
             "t": len(self._text_tokens),
             "f": len(self._formula_tokens),
@@ -423,6 +446,7 @@ def tokenize(segment_list: SegmentList, text_params=None, formula_params=None, f
 
 
 def link_formulas(*token_list: TokenList, link_vars=True):
+    """call formula function"""
     ast_formulas = []
     for tl in token_list:
         if tl.formula_tokenize_method == "ast":

From edd982fa591bb4ee1785ede813d9711740630a8d Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 13 Sep 2021 00:35:06 +0800
Subject: [PATCH 086/149] fix pytest error

---
 EduNLP/SIF/segment/segment.py                | 3 ++-
 EduNLP/SIF/tokenization/formula/ast_token.py | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index f85a7cea..2bbefdca 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -197,7 +197,8 @@ def to_symbol(self, idx, symbol):
 
     def symbolize(self, to_symbolize="fgm"):
         """
-        Switch designated elements to symbol. It is a good way to protect or preserve the elements which we don't want to tokenize.
+        Switch designated elements to symbol. \
+        It is a good way to protect or preserve the elements which we don't want to tokenize.
 
         Parameters
         ----------
diff --git a/EduNLP/SIF/tokenization/formula/ast_token.py b/EduNLP/SIF/tokenization/formula/ast_token.py
index b113e8b8..22a2af23 100644
--- a/EduNLP/SIF/tokenization/formula/ast_token.py
+++ b/EduNLP/SIF/tokenization/formula/ast_token.py
@@ -36,7 +36,7 @@
 
 def traversal_formula(ast, ord2token=False, var_numbering=False, strategy="post", *args, **kwargs):
     """
-    The part will run only when the return type is list. And it provides two strategy: post and linear. 
+    The part will run only when the return type is list. And it provides two strategy: post and linear.
     Besides, tokens list will append node follow its type.
     """
     tokens = []

From 4fc89baf4aec25f3109ac01e55545cdb330f0d67 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 13 Sep 2021 00:37:30 +0800
Subject: [PATCH 087/149] update api/sif

---
 docs/source/api/sif.rst | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/source/api/sif.rst b/docs/source/api/sif.rst
index 4931807c..7467b7cb 100644
--- a/docs/source/api/sif.rst
+++ b/docs/source/api/sif.rst
@@ -8,16 +8,16 @@ SIF
    :imported-members:
 
 
-Segment
-----------
-.. automodule:: EduNLP.SIF.segment.segment
+Parser
+--------
+.. automodule:: EduNLP.SIF.parser.parser.Parser
    :members:
    :imported-members:
 
 
-Parser
---------
-.. automodule:: EduNLP.SIF.parser.parser.Parser
+Segment
+----------
+.. automodule:: EduNLP.SIF.segment.segment
    :members:
    :imported-members:
 

From a8f9fa35d75f4c3a1b0c91ac99d219f43a73e241 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 13 Sep 2021 14:40:38 +0800
Subject: [PATCH 088/149] [DOC]update documents

---
 EduNLP/I2V/i2v.py    | 8 ++++++++
 EduNLP/Vector/t2v.py | 3 +++
 2 files changed, 11 insertions(+)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 48e64b15..40c0248c 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -260,11 +260,19 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
 
 def get_pretrained_i2v(name, model_dir=MODEL_DIR):
     """
+    It is a good idea if you want to switch item to vector earily.
 
     Parameters
     -----------
     name: str
         the name of item2vector model
+        e.g.:
+        d2v_all_256
+        d2v_sci_256
+        d2v_eng_256
+        d2v_lit_256
+        w2v_sci_300
+        w2v_lit_300
     model_dir:str
         the path of model, default: MODEL_DIR = '~/.EduNLP/model'
 
diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index b2133587..dbd68855 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -21,6 +21,8 @@
 
 class T2V(object):
     """
+    The function aims to transfer token list to vector. If you have a certain model, you can use T2V directly. \
+    Otherwise, calling get_pretrained_t2v function is a better way to get vector which can switch it without your model.
 
     Parameters
     ----------
@@ -73,6 +75,7 @@ def vector_size(self) -> int:
 
 def get_pretrained_t2v(name, model_dir=MODEL_DIR):
     """
+    It is a good idea if you want to switch token list to vector earily.
 
     Parameters
     ----------

From 7cbe63d6297e58154a24dcbe2b5cd6cd987df112 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 13 Sep 2021 15:01:39 +0800
Subject: [PATCH 089/149] [DOC]Update documents

---
 EduNLP/Tokenizer/tokenizer.py | 1 +
 EduNLP/Vector/gensim_vec.py   | 1 +
 EduNLP/utils/data.py          | 2 ++
 3 files changed, 4 insertions(+)

diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py
index 761514fa..20f89e01 100644
--- a/EduNLP/Tokenizer/tokenizer.py
+++ b/EduNLP/Tokenizer/tokenizer.py
@@ -121,6 +121,7 @@ def __call__(self, items: Iterable, key=lambda x: x, *args, **kwargs):
 
 def get_tokenizer(name, *args, **kwargs):
     r"""
+    It is a total interface to use difference tokenizer.
 
     Parameters
     ----------
diff --git a/EduNLP/Vector/gensim_vec.py b/EduNLP/Vector/gensim_vec.py
index b34ac3a0..3ade74a5 100644
--- a/EduNLP/Vector/gensim_vec.py
+++ b/EduNLP/Vector/gensim_vec.py
@@ -12,6 +12,7 @@
 
 class W2V(Vector):
     """
+    The part uses gensim library providing FastText, Word2Vec and KeyedVectors method to transfer word to vector.
 
     Parameters
     ----------
diff --git a/EduNLP/utils/data.py b/EduNLP/utils/data.py
index dec5d773..e901696c 100644
--- a/EduNLP/utils/data.py
+++ b/EduNLP/utils/data.py
@@ -11,6 +11,7 @@
 
 @contextmanager
 def add_annotation(key, tag_mode, tar: list, key_as_tag=True):
+    """add tag"""
     if key_as_tag is True:
         if tag_mode == "delimiter":
             tar.append(ann_begin_format.format(key))
@@ -26,6 +27,7 @@ def add_annotation(key, tag_mode, tar: list, key_as_tag=True):
 
 def dict2str4sif(obj: dict, key_as_tag=True, tag_mode="delimiter", add_list_no_tag=True, keys=None) -> str:
     r"""
+    The function aims to transfer dictionary format item to string format item.
 
     Parameters
     ----------

From be85d719dcd89fe9cec34d17b8af8fce1cbbf7e7 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Tue, 14 Sep 2021 00:01:26 +0800
Subject: [PATCH 090/149] [DOC]Update documents

---
 EduNLP/Tokenizer/tokenizer.py     | 54 ++++++++++++++-----------------
 docs/source/tutorial/zh/index.rst |  8 ++---
 docs/source/tutorial/zh/parse.rst |  4 +--
 3 files changed, 31 insertions(+), 35 deletions(-)

diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py
index 20f89e01..64e40970 100644
--- a/EduNLP/Tokenizer/tokenizer.py
+++ b/EduNLP/Tokenizer/tokenizer.py
@@ -15,6 +15,19 @@ def __call__(self, *args, **kwargs):
 
 class PureTextTokenizer(Tokenizer):
     r"""
+    Duel with text and plain text formula.
+    And filting special formula like $\\FormFigureID{…}$ and $\\FormFigureBase64{…}.
+
+    Parameters
+    ----------
+    items: str
+    key
+    args
+    kwargs
+
+    Returns
+    -------
+    token
 
     Examples
     --------
@@ -49,27 +62,24 @@ def __init__(self, *args, **kwargs):
         }
 
     def __call__(self, items: Iterable, key=lambda x: x, *args, **kwargs):
-        """
-        Duel with text and plain text formula.
-        And filting special formula like $\\FormFigureID{…}$ and $\\FormFigureBase64{…}.
-
-        Parameters
-        ----------
-        items: str
-        key
-        args
-        kwargs
-
-        Returns
-        -------
-        token
-        """
         for item in items:
             yield tokenize(seg(key(item), symbol="gmas"), **self.tokenization_params).tokens
 
 
 class TextTokenizer(Tokenizer):
     r"""
+    Duel with text and formula including special formula.
+
+    Parameters
+    ----------
+    items: str
+    key
+    args
+    kwargs
+
+    Returns
+    -------
+    token
 
     Examples
     ----------
@@ -95,20 +105,6 @@ def __init__(self, *args, **kwargs):
         }
 
     def __call__(self, items: Iterable, key=lambda x: x, *args, **kwargs):
-        """
-        Duel with text and formula including special formula.
-
-        Parameters
-        ----------
-        items: str
-        key
-        args
-        kwargs
-
-        Returns
-        -------
-        token
-        """
         for item in items:
             yield tokenize(seg(key(item), symbol="gmas"), **self.tokenization_params).tokens
 
diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 572d0459..427c824e 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -18,14 +18,14 @@
 
 .. figure:: ../../_static/新流程图.png
 
-* **语法解析**：其作用是将传入的item转换为标准sif格式（即把字母、数字用 ``$...$`` 包裹起来，把选择填空的括号、下划线转换为特殊符号等）。
+* `语法解析 <parse.rst>`_ ：其作用是将传入的item转换为标准sif格式（即把字母、数字用 ``$...$`` 包裹起来，把选择填空的括号、下划线转换为特殊符号等）。
 
-* **成分分解**：其作用是将传入的符合sif标准的item根据元素种类进行分割开来，从而服务于后面的令牌化环节（即可以将不同类型元素使用各自的方法令牌化）。
+* `成分分解 <seg.rst>`_ ：其作用是将传入的符合sif标准的item根据元素种类进行分割开来，从而服务于后面的令牌化环节（即可以将不同类型元素使用各自的方法令牌化）。
 
-* **令牌化**：其作用是将传入的经过分词后的item元素列表进行令牌化分解，从而服务于后面的向量化模块。
+* `令牌化 <tokenize.rst>`_：其作用是将传入的经过分词后的item元素列表进行令牌化分解，从而服务于后面的向量化模块。
   其中通常情况下直接使用文本形式的令牌化方法即可，对于公式而言还可使用ast方法进行解析(调用formula模块)；
 
-* **向量化**：此部分主要调用的是I2V类及其子类，其作用是将传入的令牌化后的item元素列表进行向量化操作，最终即可得到相应的静态向量。
+* `向量化 <vectorization.rst>`_：此部分主要调用的是I2V类及其子类，其作用是将传入的令牌化后的item元素列表进行向量化操作，最终即可得到相应的静态向量。
   对于向量化模块来说，可以调用自己训练好的模型，也可直接调用提供的预训练模型（调用get_pretrained_i2v模块即可）。
 
 * **下游模型**：将得到的向量进一步处理，从而得到所需的结果。
diff --git a/docs/source/tutorial/zh/parse.rst b/docs/source/tutorial/zh/parse.rst
index 6c1dfe6a..d0d9a17b 100644
--- a/docs/source/tutorial/zh/parse.rst
+++ b/docs/source/tutorial/zh/parse.rst
@@ -18,9 +18,9 @@
 
 1.匹配公式之外的英文字母、数字，只对两个汉字之间的字母、数字做修正，其余匹配到的情况视为不合 latex 语法录入的公式
 
-2.匹配“（  ）”型括号（包含英文格式和中文格式），即括号内无内容或为空格的括号，将括号替换$\\SIFChoice$
+2.匹配“（  ）”型括号（包含英文格式和中文格式），即括号内无内容或为空格的括号，将括号替换 ``$\\SIFChoice$`` 
 
-3.匹配下划线，替换连续的下划线或下划线中夹杂空格的情况，将其替换为$\\SIFBlank$
+3.匹配下划线，替换连续的下划线或下划线中夹杂空格的情况，将其替换为 ``$\\SIFBlank$`` 
 
 4.匹配latex公式，主要检查latex公式的完整性和可解析性，对latex 中出现中文字符发出警告
 

From 40d7d93fbc626d1252b57db2f765f72bfb16b37b Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 15 Sep 2021 00:06:53 +0800
Subject: [PATCH 091/149] [DOC]Update documnets

---
 docs/source/conf.py                       |    2 +
 docs/source/tutorial/zh/index.rst         |   13 +-
 docs/source/tutorial/zh/parse.rst         |    1 +
 examples/Tokenizer/total_tokenize.ipynb   | 1620 ++++
 examples/vectorization/total_vector.ipynb | 9084 +++++++++++++++++++++
 5 files changed, 10709 insertions(+), 11 deletions(-)
 create mode 100644 examples/Tokenizer/total_tokenize.ipynb
 create mode 100644 examples/vectorization/total_vector.ipynb

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 995e97c8..7787a16d 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -77,6 +77,8 @@ def copy_tree(src, tar):
     'build/blitz/tokenizer/tokenizier': '_static/tokenizer.png',
     'build/blitz/sif/sif4sci': '_static/tokenizer.png',
     'build/blitz/vectorization/get_pretrained_i2v': '_static/i2v.png',
+    'build/blitz/tokenizer/total_tokenize': '_static/tokenizer.png',
+    'build/blitz/vectorization/total_vector': '_static/i2v.png',
 }
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 427c824e..e4661880 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -35,27 +35,18 @@
 
 为使您快速了解此项目的功能，此部分仅展示常用的函数接口使用方法（如得到令牌化序列、试题对应的向量等），对于其中间函数模块（如parse、formula、segment等）以及更细分的接口方法不做展示，如需深入学习，请查看相关部分的文档。
 
-令牌化
-^^^^^^^^^^^
 
 .. nbgallery::
     :caption: This is a thumbnail gallery:
     :name: tokenizer_gallery
     :glob:
     
-    直接使用tokenize模块来进行令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>
-    调用已经封装好的令牌化容器进行令牌化  <../../build/blitz/tokenizer/tokenizier.ipynb>
-    调用sif4sci来进行令牌化 <../../build/blitz/sif/sif4sci.ipynb>
+    令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>
 
 
-向量化
-^^^^^^^^^^^
-
 .. nbgallery::
     :caption: This is a thumbnail gallery:
     :name: vectorization_gallery
     :glob:
     
-    获得数据集  <../../build/blitz/pretrain/prepare_dataset.ipynb>
-    调用已有模型得到对应的向量  <../../build/blitz/vectorization/i2v.ipynb>
-    直接利用提供的预训练模型得到对应向量  <../../build/blitz/vectorization/get_pretrained_i2v.ipynb>
+    向量化  <../../build/blitz/vectorization/total_vector.ipynb>
diff --git a/docs/source/tutorial/zh/parse.rst b/docs/source/tutorial/zh/parse.rst
index d0d9a17b..03721ba0 100644
--- a/docs/source/tutorial/zh/parse.rst
+++ b/docs/source/tutorial/zh/parse.rst
@@ -4,6 +4,7 @@
 在教育资源中，文本、公式都具有内在的隐式或显式的语法结构，提取这种结构对后续进一步的处理是大有裨益的：
 
 * 文本语法结构解析
+
 * 公式语法结构解析
 
 其目的是：
diff --git a/examples/Tokenizer/total_tokenize.ipynb b/examples/Tokenizer/total_tokenize.ipynb
new file mode 100644
index 00000000..9be17885
--- /dev/null
+++ b/examples/Tokenizer/total_tokenize.ipynb
@@ -0,0 +1,1620 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 令牌化\r\n",
+    "\r\n",
+    "## 概述\r\n",
+    "\r\n",
+    "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## tokenize\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "此模块为令牌化的基础实现模块，仅需将分词后的item列表传入此函数中即可得到相应的令牌化结果。\r\n",
+    "\r\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\r\n",
+    "\r\n",
+    "#### 文本解析\r\n",
+    "\r\n",
+    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\r\n",
+    "\r\n",
+    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \r\n",
+    "  \r\n",
+    "\r\n",
+    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\r\n",
+    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\r\n",
+    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\r\n",
+    "\r\n",
+    "#### 公式解析\r\n",
+    "\r\n",
+    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \r\n",
+    "  "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 文本解析"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 句解析\r\n",
+    "\r\n",
+    "待实现..."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 词解析\r\n",
+    "\r\n",
+    "词解析分为两个主要步骤：  \r\n",
+    "\r\n",
+    "（1） 分词：  \r\n",
+    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \r\n",
+    "    本项目目前支持的分词工具有：`jieba`   \r\n",
+    "- 单字解析：按字符划分。\r\n",
+    "    \r\n",
+    "      \r\n",
+    "（2） 筛选：过滤指定的停用词。   \r\n",
+    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \r\n",
+    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\r\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 导入模块\r\n",
+    "from EduNLP.SIF.tokenization.text import tokenize "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输入\r\n",
+    "text = \"三角函数是基本初等函数之一\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 词组解析\r\n",
+    "\r\n",
+    "分词粒度参数选择 word： `granularity = \"word\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
+    "tokenize(text, granularity=\"word\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['三角函数', '初等', '函数']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 单字解析\r\n",
+    "\r\n",
+    "分词粒度参数选择 word： `granularity = \"char\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
+    "tokenize(text, granularity=\"char\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['三', '角', '函', '数', '基', '初', '函', '数']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 停用词表"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 获取自己的停用词表\r\n",
+    "spath = \"test_stopwords.txt\"\r\n",
+    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\r\n",
+    "stopwords = get_stopwords(spath)\r\n",
+    "stopwords"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出：传入停用词表（stopwords）\r\n",
+    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['三角函数', '是', '基本', '初等', '函数', '之一']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 公式解析\r\n",
+    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 导入模块\r\n",
+    "from EduNLP.SIF.tokenization.formula import tokenize"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输入"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokenize(formula, method=\"linear\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\r\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " '{ }',\n",
+       " 'mathord',\n",
+       " '+',\n",
+       " 'mathord',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\r\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['mathord_con',\n",
+       " '{ }',\n",
+       " 'mathord_0',\n",
+       " '+',\n",
+       " 'mathord_1',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord_0']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 综合解析\r\n",
+    "\r\n",
+    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\r\n",
+    "```python\r\n",
+    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\r\n",
+    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\r\n",
+    "```\r\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 导入模块\r\n",
+    "from EduNLP.Tokenizer import get_tokenizer\r\n",
+    "\r\n",
+    "# 输入\r\n",
+    "item = {\r\n",
+    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
+    "}\r\n",
+    "\r\n",
+    "# 输出\r\n",
+    "tokenizer = get_tokenizer(\"text\")\r\n",
+    "tokens = tokenizer(item)\r\n",
+    "next(tokens) "
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " 'ABC',\n",
+       " '斜边',\n",
+       " 'BC',\n",
+       " '直角',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\x08',\n",
+       " 'igtriangleupABC',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " 'I',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " 'II',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " 'III',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " '概率',\n",
+       " '记',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3',\n",
+       " '[MARK]',\n",
+       " '[FIGURE]']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 调用sif4sci函数来进行令牌化\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
+    "\n",
+    "![Figure](../../asset/_static/item.png)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "item = {\r\n",
+    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
+    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\r\n",
+    "}\r\n",
+    "item[\"stem\"]"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 加载图片：`$\\\\FigureID{1}$`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from PIL import Image\r\n",
+    "img = Image.open(\"../../asset/_static/item_figure.png\")\r\n",
+    "figures = {\"1\": img}\r\n",
+    "img"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 导入模块"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 验证题目格式"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "is_sif(item['stem'])"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "is_sif(text)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "to_sif(text)\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 题目切分及令牌化\r\n",
+    "\r\n",
+    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 题目切分"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 基本切分\r\n",
+    "分离文本、公式、图片和特殊符号。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\r\n",
+    "segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.text_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
+       " '的斜边',\n",
+       " ', 直角边',\n",
+       " ', ',\n",
+       " '.',\n",
+       " '的三边所围成的区域记为',\n",
+       " ',黑色部分记为',\n",
+       " ', 其余部分记为',\n",
+       " '.在整个图形中随机取一点，此点取自',\n",
+       " '的概率分别记为',\n",
+       " ',则']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 公式部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.formula_segments\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I,II,III',\n",
+       " 'p_1,p_2,p_3']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 图片部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.figure_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[\\FigureID{1}]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.figure_segments[0].figure"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 特殊符号"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.ques_mark_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['\\\\SIFChoice']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 标记化切分  \r\n",
+    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    " - symbol:\n",
+    "    - \"t\": text\n",
+    "    - \"f\": formula\n",
+    "    - \"g\": figure\n",
+    "    - \"m\": question mark"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 令牌化\r\n",
+    "\r\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
+    "\r\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens.text_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " '斜边',\n",
+       " '直角',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " '概率',\n",
+       " '记']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 公式解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens.formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 自定义参数，得到定制化解析结果"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\": {\r\n",
+    "            \"method\": \"linear\",\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[<Formula: ABC>,\n",
+       " <Formula: BC>,\n",
+       " <Formula: AB>,\n",
+       " <Formula: AC>,\n",
+       " <Formula: \\bigtriangleup ABC>,\n",
+       " <Formula: I>,\n",
+       " <Formula: II>,\n",
+       " <Formula: III>,\n",
+       " <Formula: I,II,III>,\n",
+       " <Formula: p_1,p_2,p_3>]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 语法树展示："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "f = sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"return_type\": \"ast\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "            \"var_numbering\": True,\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens\r\n",
+    "f\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "for i in range(0, len(f)):\r\n",
+    "    ForestPlotter().export(\r\n",
+    "        f[i], root_list=[node for node in f[i]],\r\n",
+    "    )\r\n",
+    "# plt.show()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"return_type\": \"list\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "            \"return_type\": \"list\",\r\n",
+    "            \"var_numbering\": True\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_0',\n",
+       " 'mathord_2',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 综合训练\r\n",
+    "\r\n",
+    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\r\n",
+    "        symbol=\"fgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## tokenizer\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "tokenizer为一个封装好的令牌化容器，让用户可以渐变的将item转换为令牌化列表，下面将介绍两种令牌化容器。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### TextTokenizer and PureTextTokenizer\r\n",
+    "\r\n",
+    "- ‘text’ Tokenizer ignores and skips the FormulaFigures and tokenize latex Formulas as Text\r\n",
+    "- ‘pure_text’ Tokenizer symbolizes the FormulaFigures as [FUMULA] and tokenize latex Formulas as Text"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### TextTokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "items = [{\r\n",
+    "        \"stem\": \"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\",\r\n",
+    "        \"options\": [\"1\", \"2\"]\r\n",
+    "        }]\r\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
+    "tokens = tokenizer(items, key=lambda x: x[\"stem\"])\r\n",
+    "print(next(tokens))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['已知', '集合', 'A', '=', '\\\\left', '\\\\{', 'x', '\\\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<', '0', '\\\\right', '\\\\}', ',', '\\\\quad', 'B', '=', '\\\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\\\}', ',', '\\\\quad', 'A', '\\\\cap', 'B', '=']\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "items = [\"有公式$\\\\FormFigureID{wrong1?}$，如图$\\\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式$\\\\FormFigureBase64{wrong2?}$,$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$\"]"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "\r\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
+    "tokens = [t for t in tokenizer(items)]\r\n",
+    "tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[['公式',\n",
+       "  '[FORMULA]',\n",
+       "  '如图',\n",
+       "  '[FIGURE]',\n",
+       "  'x',\n",
+       "  ',',\n",
+       "  'y',\n",
+       "  '约束条件',\n",
+       "  '公式',\n",
+       "  '[FORMULA]',\n",
+       "  '[SEP]',\n",
+       "  'z',\n",
+       "  '=',\n",
+       "  'x',\n",
+       "  '+',\n",
+       "  '7',\n",
+       "  'y',\n",
+       "  '最大值',\n",
+       "  '[MARK]']]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### PureTextTokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokenizer = get_tokenizer(\"pure_text\") # tokenizer = PureTextTokenizer()\r\n",
+    "tokens = [t for t in tokenizer(items)]\r\n",
+    "tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[['公式',\n",
+       "  '如图',\n",
+       "  '[FIGURE]',\n",
+       "  'x',\n",
+       "  ',',\n",
+       "  'y',\n",
+       "  '约束条件',\n",
+       "  '公式',\n",
+       "  '[SEP]',\n",
+       "  'z',\n",
+       "  '=',\n",
+       "  'x',\n",
+       "  '+',\n",
+       "  '7',\n",
+       "  'y',\n",
+       "  '最大值',\n",
+       "  '[MARK]']]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/vectorization/total_vector.ipynb b/examples/vectorization/total_vector.ipynb
new file mode 100644
index 00000000..d1c58239
--- /dev/null
+++ b/examples/vectorization/total_vector.ipynb
@@ -0,0 +1,9084 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 向量化\r\n",
+    "\r\n",
+    "## 简述\r\n",
+    "\r\n",
+    "向量化过程是将item转换为向量的过程，其前置步骤为语法解析、成分分解、令牌化，本部分将先后介绍如何获得数据集、如何使用本地的预训练模型、如何直接调用远程提供的预训练模型。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 获得数据集\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "此部分通过调用 [OpenLUNA.json](http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json) 获得。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## I2V\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。\r\n",
+    "\r\n",
+    "- 优点：可以使用自己的模型，另可调整训练参数，灵活性强。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### D2V"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 导入类"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.I2V import D2V"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 输入\r\n",
+    "\r\n",
+    "类型：str  \r\n",
+    "内容：题目文本 （text）"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "items = [\r\n",
+    "r\"1如图几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
+    "r\"2如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
+    "]"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "model_path = \"../../data/d2v/test_d2v_256.bin\"\r\n",
+    "i2v = D2V(\"pure_text\",\"d2v\",filepath=model_path, pretrained_t2v = False)\r\n",
+    "\r\n",
+    "item_vectors, token_vectors = i2v(items)\r\n",
+    "print(item_vectors,  token_vectors)\r\n",
+    "print(len(item_vectors), item_vectors[0].shape) # For d2v, token_vector is None"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[array([ 0.10634826, -0.10273553, -0.04942605,  0.15323296,  0.25780037,\n",
+      "       -0.06630539, -0.02834828,  0.0065832 , -0.07495898,  0.06529831,\n",
+      "        0.07989643,  0.14697047, -0.06513839, -0.23898382,  0.04266138,\n",
+      "        0.15211952,  0.17704317,  0.02768603, -0.1854407 ,  0.21742067,\n",
+      "        0.00984387,  0.06774732, -0.02058181, -0.13266353, -0.20851782,\n",
+      "        0.00558438,  0.00460878, -0.05139542, -0.06485009, -0.04847892,\n",
+      "        0.09366203,  0.13947366, -0.07552954,  0.05977268, -0.09442269,\n",
+      "        0.12721013,  0.13273577, -0.02567345,  0.09778284, -0.08030444,\n",
+      "       -0.05697263, -0.02538129, -0.04187621, -0.03123644,  0.11242954,\n",
+      "       -0.00650479,  0.06117244,  0.06449873,  0.05163544,  0.01810673,\n",
+      "        0.03808153,  0.03746097,  0.1275719 ,  0.07381074, -0.14620851,\n",
+      "        0.05757713,  0.02838781, -0.12927914,  0.07510903,  0.08147711,\n",
+      "       -0.06523656, -0.01486957,  0.02304654,  0.04361376,  0.21421443,\n",
+      "       -0.02614699, -0.03873423,  0.21355043, -0.09329057, -0.09335907,\n",
+      "        0.08157952,  0.00625934, -0.05051626, -0.07981606, -0.03226125,\n",
+      "        0.08958097, -0.04725425, -0.06789905,  0.06730124,  0.06633109,\n",
+      "       -0.07370041, -0.13176298, -0.01786646,  0.11614659, -0.04729063,\n",
+      "        0.06670643,  0.12553535, -0.1768527 , -0.09982615, -0.07682986,\n",
+      "        0.06889272, -0.04036296,  0.03164692, -0.1446157 ,  0.03707608,\n",
+      "        0.13580611, -0.07748953, -0.02646814, -0.01086655, -0.09684405,\n",
+      "       -0.15440914, -0.1067182 ,  0.0349819 ,  0.10395218, -0.00877615,\n",
+      "        0.05640977, -0.01675119,  0.03014355,  0.12512377,  0.01414524,\n",
+      "        0.0211464 , -0.06591589,  0.08297148,  0.07012929, -0.10675234,\n",
+      "       -0.04061191,  0.01302199,  0.11621079,  0.00885058, -0.0304758 ,\n",
+      "       -0.01553952, -0.12865704,  0.17576885, -0.02950172, -0.01326492,\n",
+      "        0.01504841,  0.09476334, -0.03984889,  0.00763261, -0.01630022,\n",
+      "       -0.03672133,  0.01365909,  0.05986133, -0.10593513, -0.08282372,\n",
+      "       -0.08374579, -0.04833878,  0.0489895 ,  0.00875692, -0.03651298,\n",
+      "       -0.01757672, -0.01623743,  0.23217827,  0.15424854, -0.26954046,\n",
+      "       -0.04381514,  0.03540273,  0.06735364,  0.09081568,  0.07033931,\n",
+      "       -0.14287043, -0.1028204 , -0.04575491, -0.01768357, -0.08948888,\n",
+      "        0.07118583,  0.1542743 ,  0.09116074, -0.08839961,  0.01807798,\n",
+      "        0.12579061, -0.03509037,  0.08215491, -0.06514787,  0.00351805,\n",
+      "        0.04778312,  0.04185999,  0.08844823,  0.10574003, -0.01214823,\n",
+      "        0.04865406, -0.03167628,  0.02753052,  0.05206006, -0.15243334,\n",
+      "       -0.02005264, -0.00607761, -0.01364831,  0.10955886, -0.0010281 ,\n",
+      "        0.11600163, -0.04597978,  0.0601487 , -0.05470827,  0.11942582,\n",
+      "        0.00830158, -0.13237426, -0.15353964, -0.07234719,  0.04098862,\n",
+      "        0.15481706,  0.01778992,  0.14323051,  0.09922674,  0.12223933,\n",
+      "        0.0526598 ,  0.0408572 , -0.13533838,  0.02140845, -0.1030432 ,\n",
+      "       -0.17960373, -0.0495283 ,  0.14249511,  0.0465369 , -0.04416716,\n",
+      "        0.11465154, -0.2066736 , -0.02712584, -0.06553549,  0.0557267 ,\n",
+      "        0.04696037, -0.02916378,  0.07578196,  0.00608585,  0.00944843,\n",
+      "        0.02514744,  0.16452865,  0.0508883 , -0.15239422, -0.01861594,\n",
+      "       -0.1283559 ,  0.06792712,  0.0826408 , -0.05190438,  0.03456785,\n",
+      "       -0.00661102,  0.00788855,  0.02678101, -0.02560065, -0.00632013,\n",
+      "       -0.193216  , -0.1322564 , -0.03174976, -0.04030088,  0.01397508,\n",
+      "        0.07423516,  0.04445267,  0.0541687 ,  0.083343  , -0.15150657,\n",
+      "        0.00937343,  0.05487543,  0.02184621, -0.00872194, -0.00329627,\n",
+      "       -0.05453907,  0.05444683, -0.02034983, -0.0742873 , -0.01699653,\n",
+      "       -0.16100745,  0.10637001, -0.12436336, -0.03511102, -0.20044689,\n",
+      "       -0.02411474], dtype=float32), array([ 1.36041582e-01, -1.08781166e-01, -5.83929941e-02,  1.91058487e-01,\n",
+      "        3.24488580e-01, -6.02278523e-02, -4.59153317e-02, -2.24303032e-04,\n",
+      "       -8.76015052e-02,  8.96644667e-02,  1.04697935e-01,  1.83520690e-01,\n",
+      "       -7.28873909e-02, -3.05169255e-01,  4.17109728e-02,  1.82436183e-01,\n",
+      "        2.26057917e-01,  2.32112277e-02, -2.30914488e-01,  2.76603520e-01,\n",
+      "       -4.85374872e-03,  7.58373216e-02, -2.29087640e-02, -1.79299608e-01,\n",
+      "       -2.54585207e-01,  1.64706651e-02,  7.45610800e-03, -7.51212165e-02,\n",
+      "       -7.42579326e-02, -4.80351038e-02,  1.25985965e-01,  1.73480287e-01,\n",
+      "       -1.01882420e-01,  6.75791055e-02, -1.05399802e-01,  1.51736602e-01,\n",
+      "        1.63938925e-01, -3.84903289e-02,  1.10449508e-01, -9.40715224e-02,\n",
+      "       -6.31079897e-02, -3.84142622e-02, -4.99494635e-02, -4.23108302e-02,\n",
+      "        1.55812502e-01, -1.16190566e-02,  5.13239168e-02,  6.35165498e-02,\n",
+      "        6.25813156e-02,  2.21146382e-02,  6.36933595e-02,  5.06636649e-02,\n",
+      "        1.47942111e-01,  9.66929942e-02, -1.76337466e-01,  7.13716373e-02,\n",
+      "        2.70809997e-02, -1.57342017e-01,  9.11386758e-02,  9.41570252e-02,\n",
+      "       -9.42364857e-02, -5.13231056e-03,  1.36982789e-02,  5.77186793e-02,\n",
+      "        2.64365107e-01, -3.76182236e-02, -4.40398045e-02,  2.51175314e-01,\n",
+      "       -1.05481505e-01, -1.20290719e-01,  9.69798341e-02,  1.16603952e-02,\n",
+      "       -7.56024718e-02, -1.08631417e-01, -4.93911207e-02,  1.18807659e-01,\n",
+      "       -5.85292391e-02, -8.24716613e-02,  7.88261220e-02,  8.72761682e-02,\n",
+      "       -1.01744331e-01, -1.44680306e-01, -2.09336001e-02,  1.33469954e-01,\n",
+      "       -3.82428057e-02,  7.42774233e-02,  1.48893908e-01, -2.09778398e-01,\n",
+      "       -1.35315523e-01, -8.64634812e-02,  9.07569081e-02, -5.20299003e-02,\n",
+      "        5.39300404e-02, -1.71408862e-01,  3.55036259e-02,  1.70207739e-01,\n",
+      "       -8.76016989e-02, -2.80412864e-02, -2.70368508e-03, -1.22690618e-01,\n",
+      "       -1.63871482e-01, -1.22708760e-01,  3.35654393e-02,  1.26829579e-01,\n",
+      "       -1.43117364e-02,  7.96694681e-02, -3.10931951e-02,  2.37075817e-02,\n",
+      "        1.55911028e-01,  1.69575382e-02,  1.54271908e-02, -9.58202332e-02,\n",
+      "        1.09241918e-01,  8.82035494e-02, -1.39677778e-01, -5.01841754e-02,\n",
+      "        2.18386631e-02,  1.57107875e-01,  7.93855730e-03, -3.16538438e-02,\n",
+      "       -1.25909718e-02, -1.62555054e-01,  2.32526332e-01, -3.71498615e-02,\n",
+      "       -2.24065650e-02,  2.61516422e-02,  1.20939091e-01, -6.72491416e-02,\n",
+      "       -4.27399318e-05, -3.77265252e-02, -4.58711684e-02,  2.53423229e-02,\n",
+      "        8.39713439e-02, -1.19929694e-01, -1.07749395e-01, -1.10882133e-01,\n",
+      "       -4.97324318e-02,  6.16546497e-02, -1.15693109e-02, -3.91881578e-02,\n",
+      "       -2.29061693e-02, -2.30852738e-02,  3.04617375e-01,  2.08306596e-01,\n",
+      "       -3.39470148e-01, -5.02127111e-02,  5.37177026e-02,  9.11831036e-02,\n",
+      "        1.14661761e-01,  9.45202038e-02, -1.62573129e-01, -1.38315439e-01,\n",
+      "       -6.47830069e-02, -2.83303317e-02, -1.22525014e-01,  9.37087536e-02,\n",
+      "        1.96664244e-01,  1.07929356e-01, -1.03713363e-01,  1.60198733e-02,\n",
+      "        1.63076490e-01, -5.17086089e-02,  1.10869825e-01, -7.68428966e-02,\n",
+      "        8.37910455e-03,  5.56400865e-02,  6.31182268e-02,  1.21523716e-01,\n",
+      "        1.36712998e-01, -3.43602896e-02,  5.95005825e-02, -3.38348858e-02,\n",
+      "        3.81066576e-02,  6.04561493e-02, -1.77799106e-01, -3.31094116e-02,\n",
+      "       -1.12194773e-02, -3.21154818e-02,  1.08467303e-01, -4.27856669e-03,\n",
+      "        1.51000738e-01, -6.47066757e-02,  6.33488074e-02, -6.28948510e-02,\n",
+      "        1.29441455e-01,  4.04741522e-03, -1.72044575e-01, -1.91321328e-01,\n",
+      "       -8.56087804e-02,  4.62383777e-02,  2.01764986e-01,  3.43908928e-02,\n",
+      "        1.77972093e-01,  1.27216563e-01,  1.52360231e-01,  5.92516772e-02,\n",
+      "        4.44654636e-02, -1.71815068e-01,  5.64131141e-02, -1.30362645e-01,\n",
+      "       -2.15643197e-01, -6.48207739e-02,  1.80758730e-01,  6.18097410e-02,\n",
+      "       -6.83161020e-02,  1.69224367e-01, -2.53407449e-01, -4.33427393e-02,\n",
+      "       -8.90076980e-02,  7.33624548e-02,  5.79761080e-02, -3.76918130e-02,\n",
+      "        8.55930522e-02,  2.99188751e-03,  1.56438854e-02,  2.43693665e-02,\n",
+      "        2.17681393e-01,  6.79032505e-02, -1.83962315e-01, -1.40863275e-02,\n",
+      "       -1.53916925e-01,  8.76621082e-02,  9.75017548e-02, -6.90853447e-02,\n",
+      "        3.22550870e-02, -1.22331316e-02,  8.18267651e-03,  3.01490221e-02,\n",
+      "       -1.40115758e-02,  1.17691020e-02, -2.29610443e-01, -1.88834459e-01,\n",
+      "       -4.37003709e-02, -3.97654139e-02,  2.05663759e-02,  9.48096588e-02,\n",
+      "        4.75249775e-02,  6.99462742e-02,  9.24514905e-02, -1.96493998e-01,\n",
+      "        3.27189267e-02,  4.80476059e-02,  3.26564163e-02, -1.90033496e-03,\n",
+      "        3.73094343e-03, -5.62442988e-02,  5.95186092e-02, -2.72717867e-02,\n",
+      "       -8.34571719e-02, -2.22789161e-02, -1.91354036e-01,  1.27723783e-01,\n",
+      "       -1.69917479e-01, -3.11680529e-02, -2.62214512e-01, -2.82380339e-02],\n",
+      "      dtype=float32)] None\n",
+      "2 (256,)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduData import get_data\r\n",
+    "\r\n",
+    "get_data(\"open-luna\", \"../../data/\")\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!\n",
+      "  warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n",
+      "downloader, INFO http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json is saved as ../../data/OpenLUNA.json\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Downloading ../../data/OpenLUNA.json 100.00%: 269KB | 269KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'../../data/OpenLUNA.json'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### W2V"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.I2V import W2V\r\n",
+    "\r\n",
+    "model_path = \"../../data/w2v/general_literal_300/general_literal_300.kv\"\r\n",
+    "i2v = W2V(\"pure_text\",\"w2v\",filepath=model_path, pretrained_t2v = False)\r\n",
+    "try:\r\n",
+    "    item_vectors, token_vectors = i2v(items)\r\n",
+    "    print(item_vectors) # 每个句子vector 为np.ndarray\r\n",
+    "    print(token_vectors) # 每个单词vector list\r\n",
+    "    print(len(item_vectors), item_vectors[0].shape,len(token_vectors), len(token_vectors[0]),len(token_vectors[0][0]))\r\n",
+    "except Exception as e:\r\n",
+    "    print(e)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[array([-1.34266680e-03,  5.19845746e-02, -1.98070258e-02, -4.17470075e-02,\n",
+      "        4.92814295e-02, -1.70883536e-01, -2.16597781e-01, -3.12069029e-01,\n",
+      "        8.96430463e-02, -1.31331667e-01,  9.16494895e-03, -3.22572999e-02,\n",
+      "        3.07940125e-01,  1.92060292e-01,  1.31043345e-02,  6.10962026e-02,\n",
+      "        2.21019030e-01, -3.53541046e-01,  1.34150490e-01,  1.14867561e-01,\n",
+      "        1.17448963e-01,  2.27990672e-01, -1.65213019e-01,  2.78246611e-01,\n",
+      "       -4.36594114e-02, -1.37816787e-01, -1.07707813e-01, -1.80805102e-01,\n",
+      "        1.20028563e-01, -1.14409983e-01,  6.19181581e-02, -1.79836392e-01,\n",
+      "        7.68677965e-02,  2.41688967e-01,  6.20721914e-02, -7.59824514e-02,\n",
+      "        1.79465964e-01,  1.69306010e-01, -1.99512452e-01, -9.75036696e-02,\n",
+      "        1.02485821e-01, -1.59723386e-01, -1.67252243e-01,  1.52240042e-02,\n",
+      "       -5.98842278e-03,  6.47612512e-02,  8.48228261e-02,  2.67874986e-01,\n",
+      "       -1.73656959e-02, -4.40101810e-02,  9.11948457e-02,  1.40905827e-01,\n",
+      "        6.33735815e-03,  2.03221604e-01, -1.97303146e-01,  1.32987842e-01,\n",
+      "       -1.80283263e-01,  3.64040211e-02,  2.49624569e-02,  7.49479085e-02,\n",
+      "       -2.05568615e-02, -4.02397066e-02, -1.08619891e-01, -1.04757406e-01,\n",
+      "       -8.36341307e-02,  6.61163032e-02, -1.11632387e-03,  3.96131463e-02,\n",
+      "       -3.51454802e-02,  9.09155831e-02,  1.87938929e-01, -2.40521863e-01,\n",
+      "       -5.97307160e-02,  1.74426511e-01,  1.56350788e-02, -4.20243442e-02,\n",
+      "       -1.90285146e-02, -3.85696471e-01, -1.01543151e-01, -1.42145246e-01,\n",
+      "        2.33298853e-01,  1.85939763e-02, -2.68633634e-01, -3.60178575e-02,\n",
+      "        3.64447385e-02, -1.70443758e-01,  1.03326524e-02, -1.47003353e-01,\n",
+      "       -9.38110873e-02,  1.63190335e-01,  1.20674491e-01, -2.36976147e-02,\n",
+      "       -6.52602538e-02,  1.29773334e-01, -4.23593611e-01,  2.43276700e-01,\n",
+      "       -8.00978579e-03, -9.39133018e-03,  1.17623486e-01,  1.16482794e-01,\n",
+      "       -9.27330479e-02,  6.17316999e-02,  1.51295820e-02,  2.25901395e-01,\n",
+      "        7.31975585e-02,  2.29724105e-02,  9.95925292e-02, -1.10697523e-01,\n",
+      "        2.28960160e-02,  8.65939483e-02,  1.16645083e-01, -7.00058565e-02,\n",
+      "        1.13389529e-01, -5.30471019e-02,  1.43660516e-01,  1.61379650e-02,\n",
+      "        6.77419230e-02,  8.09707418e-02,  2.09957212e-01, -6.64654151e-02,\n",
+      "       -1.81450248e-01,  2.21659631e-01, -4.53737518e-03,  4.69567403e-02,\n",
+      "        8.59350115e-02,  1.17934339e-01, -1.98988736e-01, -4.13361974e-02,\n",
+      "        1.26167178e-01,  3.84825058e-02,  1.64396539e-01, -1.63344927e-02,\n",
+      "        9.12889242e-02, -1.13650873e-01, -1.37156844e-02,  2.06742659e-02,\n",
+      "       -9.15742964e-02,  7.41296187e-02,  2.50813574e-01, -1.35987863e-01,\n",
+      "       -1.11708120e-01, -1.52451068e-01,  1.08608082e-01,  4.99855466e-02,\n",
+      "        1.68440521e-01, -2.47063249e-01, -2.21773341e-01,  4.81536575e-02,\n",
+      "       -7.66365305e-02,  2.55189091e-01, -5.60788438e-03, -2.69066542e-02,\n",
+      "        2.07698755e-02,  1.36008840e-02,  1.33086294e-01, -3.80828045e-02,\n",
+      "       -7.03251585e-02, -6.18199483e-02,  9.03518647e-02, -1.89310908e-01,\n",
+      "       -5.30523732e-02, -2.04426926e-02, -2.27697566e-01,  7.68405125e-02,\n",
+      "        1.28568143e-01,  1.07449636e-01, -1.98028013e-01, -2.67155319e-01,\n",
+      "       -5.17064631e-02, -1.62200809e-01,  1.87425911e-01,  4.74511087e-02,\n",
+      "       -4.24213745e-02, -2.71449953e-01, -2.83543557e-01, -2.36278087e-01,\n",
+      "       -4.38764729e-02,  1.67618364e-01, -2.51966029e-01, -2.73265123e-01,\n",
+      "       -1.68406263e-01, -3.58684808e-01,  2.44145632e-01, -2.55741596e-01,\n",
+      "       -2.28520826e-01,  2.39279866e-01, -1.68833986e-01,  1.66422993e-01,\n",
+      "       -3.53969544e-01, -1.10907286e-01, -6.29489049e-02, -4.55605611e-02,\n",
+      "        1.46765754e-01,  1.95176788e-02, -3.80197394e-04,  3.36615089e-03,\n",
+      "       -1.42359287e-01, -1.06109239e-01, -3.36164385e-02,  3.16832401e-02,\n",
+      "        1.09924652e-01,  2.10711379e-02,  1.58359021e-01,  1.71957895e-01,\n",
+      "        4.08717275e-01, -4.28679548e-02, -6.48310632e-02,  1.27063962e-02,\n",
+      "        5.73479272e-02,  1.40002951e-01, -3.66613895e-01,  8.07148069e-02,\n",
+      "        2.11823225e-01, -1.10516161e-01, -2.01001287e-01,  3.22122797e-02,\n",
+      "        5.47345541e-02,  2.30176803e-02, -9.94866490e-02, -4.44128877e-03,\n",
+      "        6.64432272e-02,  1.28168091e-01, -2.34743133e-01,  3.17057431e-01,\n",
+      "       -8.75139013e-02,  2.66474396e-01, -3.12204093e-01,  7.78969377e-03,\n",
+      "        6.21694922e-02,  7.64596611e-02, -8.79013091e-02,  1.01901866e-01,\n",
+      "        3.23867425e-02, -2.27650225e-01,  9.44062844e-02, -5.54776154e-02,\n",
+      "       -7.03687780e-03,  5.66167049e-02, -1.87480077e-01,  9.11692008e-02,\n",
+      "        1.51293352e-01, -1.92774653e-01,  2.23165095e-01,  2.26982050e-02,\n",
+      "       -2.70489484e-01,  1.25889871e-02, -2.30410039e-01,  1.40989587e-01,\n",
+      "        2.20341813e-02,  2.70313285e-02,  6.07572980e-02,  8.79322216e-02,\n",
+      "        7.42911696e-02, -2.76499927e-01,  2.05189809e-01, -1.84953049e-01,\n",
+      "       -1.68468937e-01,  1.85525760e-01, -3.32091609e-03,  2.29632735e-01,\n",
+      "        7.13749304e-02, -2.75445748e-02,  2.74335817e-02,  1.65132031e-01,\n",
+      "       -1.64373592e-01, -1.14925921e-01,  4.98081557e-02,  2.10796613e-02,\n",
+      "       -2.07561441e-02,  5.90056814e-02, -1.25214513e-02,  3.78197022e-02,\n",
+      "        3.62618983e-01,  1.72744930e-01, -8.75385627e-02,  1.52320743e-01,\n",
+      "        1.29331559e-01, -1.34815618e-01,  6.12287596e-02,  7.30569959e-02,\n",
+      "        5.37401363e-02, -1.46815628e-01, -2.61263877e-01,  2.18300954e-01,\n",
+      "        8.95068944e-02, -6.59529120e-02, -8.52308050e-02,  2.63195664e-01,\n",
+      "        2.09921718e-01, -1.73417434e-01,  2.11869497e-02,  7.06950724e-02,\n",
+      "       -7.89924189e-02,  1.11086138e-01, -1.29149273e-01,  1.16233543e-01,\n",
+      "        2.16104537e-01, -3.05427730e-01, -2.46336535e-01,  7.59556964e-02],\n",
+      "      dtype=float32), array([ 3.99896456e-03,  8.05600137e-02, -3.91732380e-02, -4.86987866e-02,\n",
+      "        3.94341163e-02, -1.30174190e-01, -2.01225460e-01, -2.85685867e-01,\n",
+      "        1.13042928e-01, -1.24627367e-01, -3.15027847e-03, -2.73833387e-02,\n",
+      "        2.88367450e-01,  1.94409564e-01,  3.40008587e-02,  6.49633706e-02,\n",
+      "        2.05017760e-01, -3.16232592e-01,  1.45942107e-01,  1.04565762e-01,\n",
+      "        1.03725404e-01,  2.52305537e-01, -1.66235551e-01,  2.67002851e-01,\n",
+      "       -3.40563171e-02, -1.48293629e-01, -8.91103819e-02, -1.95181474e-01,\n",
+      "        1.10355772e-01, -1.24735624e-01,  6.77165315e-02, -1.79110825e-01,\n",
+      "        9.29452926e-02,  2.58970559e-01,  7.42715821e-02, -6.03715926e-02,\n",
+      "        1.70794770e-01,  1.57133996e-01, -1.81736186e-01, -8.52864981e-02,\n",
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+      "       -3.27972993e-02,  6.36150762e-02,  9.31354091e-02,  2.56219536e-01,\n",
+      "       -1.03600009e-03, -5.63832745e-02,  1.04097560e-01,  1.64364725e-01,\n",
+      "        1.38764898e-03,  1.74933732e-01, -2.03917757e-01,  1.12410709e-01,\n",
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+      "       -3.34393196e-02, -3.97191271e-02, -9.81974155e-02, -9.83537436e-02,\n",
+      "       -8.92241970e-02,  3.84929292e-02, -2.34650876e-02,  3.92586254e-02,\n",
+      "       -1.21332873e-02,  8.60194713e-02,  1.90761849e-01, -2.39736110e-01,\n",
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+      "       -9.69900340e-02,  1.86301693e-01,  1.12576351e-01, -1.31493211e-02,\n",
+      "       -7.17251375e-02,  1.28246799e-01, -4.34402436e-01,  2.56356359e-01,\n",
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+      "       -6.03394024e-02, -6.65174201e-02,  5.96780106e-02, -1.83861658e-01,\n",
+      "       -4.11421135e-02, -3.86951715e-02, -2.46807054e-01,  8.56175721e-02,\n",
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+      "       -1.68186743e-02,  1.73562005e-01, -2.25999728e-01, -2.68874079e-01,\n",
+      "       -1.56421170e-01, -3.60956639e-01,  2.22289428e-01, -2.56593823e-01,\n",
+      "       -2.24844545e-01,  2.01746643e-01, -1.69296905e-01,  1.37857497e-01,\n",
+      "       -3.48357409e-01, -1.08037509e-01, -4.35788482e-02, -7.35020190e-02,\n",
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+      "       -1.20992407e-01, -9.01867524e-02, -5.39640710e-02,  2.90719923e-02,\n",
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+      "        5.47830611e-02,  1.45343632e-01, -3.50050420e-01,  7.38515854e-02,\n",
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+      "        4.69029881e-02,  8.56466219e-03, -7.87592158e-02, -2.19903085e-02,\n",
+      "        8.37039649e-02,  1.22328199e-01, -2.35467061e-01,  2.92849153e-01,\n",
+      "       -9.32604820e-02,  2.57044792e-01, -3.02668840e-01, -1.55454287e-02,\n",
+      "        3.28060500e-02,  8.62515792e-02, -9.33100730e-02,  1.22677222e-01,\n",
+      "        1.61928367e-02, -2.00113922e-01,  8.60652030e-02, -8.66189077e-02,\n",
+      "       -3.23841651e-03,  5.33611439e-02, -1.52606457e-01,  8.60279724e-02,\n",
+      "        1.27633780e-01, -1.60204068e-01,  2.37834677e-01,  2.67545842e-02,\n",
+      "       -2.55489230e-01,  6.63197774e-04, -1.99840829e-01,  1.32952914e-01,\n",
+      "        3.54236271e-03,  8.06213543e-03,  7.47034177e-02,  5.73524348e-02,\n",
+      "        7.69657269e-02, -2.73610741e-01,  1.96514055e-01, -1.91754386e-01,\n",
+      "       -1.88240603e-01,  1.87844530e-01, -2.80354973e-02,  2.05444053e-01,\n",
+      "        7.45789781e-02, -4.90248986e-02,  4.41812612e-02,  1.78540751e-01,\n",
+      "       -1.57397076e-01, -1.00983113e-01,  3.23408544e-02,  2.87504797e-03,\n",
+      "       -5.67093492e-03,  4.78498563e-02, -3.49580608e-02,  1.54349199e-02,\n",
+      "        3.57598037e-01,  1.60198748e-01, -6.42205030e-02,  1.45740673e-01,\n",
+      "        1.48926109e-01, -1.28807038e-01,  4.38908488e-02,  1.16026729e-01,\n",
+      "        5.86194582e-02, -1.27828017e-01, -2.57573873e-01,  2.15158001e-01,\n",
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+      "        -1.91340293e-03, -1.57696719e-03,  3.04173620e-04,\n",
+      "        -5.84207359e-04, -5.23861265e-04, -1.07896246e-03,\n",
+      "         2.33099703e-03,  1.71581905e-05, -2.32249891e-04,\n",
+      "        -3.17664375e-03, -2.69629643e-03,  1.33865199e-03,\n",
+      "         9.12745774e-04, -1.42755825e-03,  2.32963869e-03,\n",
+      "         2.05383217e-03,  3.06564802e-03,  7.19071249e-04,\n",
+      "         1.43896416e-03, -5.80584980e-04, -2.86931591e-03,\n",
+      "        -8.82597757e-04, -7.48244929e-05, -8.70594988e-04,\n",
+      "        -3.28865205e-03, -4.22636658e-04, -2.09302269e-03,\n",
+      "         2.38356204e-03,  2.99137435e-04,  1.99374114e-03,\n",
+      "        -3.29766423e-03, -3.83829261e-04, -6.66522974e-05,\n",
+      "         1.86359242e-03, -1.18281448e-03,  3.17071029e-03,\n",
+      "         1.43475528e-03, -5.56531770e-04, -2.99364398e-03,\n",
+      "        -8.49072938e-04,  2.90678977e-03,  1.38175010e-03,\n",
+      "        -3.24120210e-03,  1.73496245e-03,  2.14513144e-04,\n",
+      "         3.03984474e-04, -2.73339823e-03, -4.92684048e-05,\n",
+      "        -2.98183598e-03,  2.53150612e-03,  1.42910797e-03,\n",
+      "        -3.19766201e-04,  6.87253487e-04,  8.49134114e-04,\n",
+      "        -2.28563696e-03, -1.40933273e-03,  1.24189933e-03,\n",
+      "        -3.09705571e-03,  5.73805941e-04,  9.56805539e-04,\n",
+      "        -1.73228339e-03,  2.32816543e-04,  1.19607452e-04,\n",
+      "         1.69905031e-03,  7.15173897e-04,  1.78219797e-03,\n",
+      "         3.89399531e-04,  2.28876108e-03, -2.02320423e-03,\n",
+      "         1.96936447e-03, -1.55187689e-03,  7.46556907e-04,\n",
+      "        -2.10508914e-03,  2.01053941e-03, -1.14239054e-03,\n",
+      "        -2.59110215e-03, -5.16111846e-04, -1.96155859e-03,\n",
+      "         2.89981766e-03, -1.50978332e-03, -1.66428485e-03,\n",
+      "         1.64764715e-04,  2.40476523e-03,  3.12477024e-03,\n",
+      "        -1.51125831e-03,  2.34143739e-03,  1.34975911e-04], dtype=float32)]]\n",
+      "2 (300,) 2 55 300\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## get_pretrained_i2v\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "使用 EduNLP 项目组给定的预训练模型将给定的题目文本转成向量。\r\n",
+    "\r\n",
+    "- 优点：简单方便。\r\n",
+    "- 缺点：只能使用项目中给定的模型，局限性较大。\r\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 导入功能块"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP import get_pretrained_i2v"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 输入\r\n",
+    "\r\n",
+    "类型：str  \r\n",
+    "内容：题目文本 （text）"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "item = {\r\n",
+    "\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
+    "}\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 模型选择与使用"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "根据题目所属学科选择预训练模型：    \n",
+    "\n",
+    " 预训练模型名称 | 模型训练数据的所属学科   \n",
+    " -------------- | ----------------------   \n",
+    " d2v_all_256    | 全学科                   \n",
+    " d2v_sci_256    | 理科                     \n",
+    " d2v_eng_256    | 英语                     \n",
+    " d2v_lit_256    | 文科                      \n",
+    "\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "i2v = get_pretrained_i2v(\"d2v_sci_256\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "EduNLP, INFO Use pretrained t2v model d2v_sci_256\n",
+      "downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as /home/lvrui/.EduNLP/model/general_science_256.zip\n",
+      "downloader, INFO file existed, skipped\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 注意：\n",
+    "    默认的 EduNLP 项目存储地址为根目录(`~/.EduNLP`)，模型存储地址为项目存储地址下的 `model` 文件夹。您可以通过修改下面的环境变量来修改模型存储地址：\n",
+    "    - EduNLP 项目存储地址：`EDUNLPPATH = xx/xx/xx`\n",
+    "    - 模型存储地址：`EDUNLPMODELPATH = xx/xx/xx`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "print(i2v(item))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "([array([-2.38860980e-01,  7.09681511e-02, -2.71706015e-01,  1.64714813e-01,\n",
+      "        2.81243492e-02, -1.82386801e-01,  9.22331214e-02,  1.31783364e-02,\n",
+      "        9.15176645e-02,  3.14464062e-01,  9.37800854e-02, -2.28523940e-01,\n",
+      "       -2.60597020e-01,  6.49375990e-02,  9.75619778e-02, -1.97933778e-01,\n",
+      "        8.29798505e-02, -2.26491719e-01, -1.77030653e-01, -3.56038064e-02,\n",
+      "        6.22844934e-01, -2.66110301e-01,  8.00080523e-02, -1.60827965e-01,\n",
+      "       -1.78654417e-01, -1.33000776e-01,  2.76004016e-01,  1.79546073e-01,\n",
+      "        8.71006995e-02,  2.33958483e-01,  1.76031828e-01,  1.55402005e-01,\n",
+      "       -1.38987333e-01, -1.92975491e-01, -1.09528497e-01,  1.12305783e-01,\n",
+      "        2.32549626e-02,  7.75609687e-02, -2.43636876e-01,  6.35311157e-02,\n",
+      "       -4.82399836e-02, -2.24204548e-02,  7.49862418e-02, -1.91449642e-01,\n",
+      "        9.72701237e-02,  4.00750965e-01,  2.81992704e-01,  3.07581365e-01,\n",
+      "       -4.68867749e-01, -3.03025767e-02, -1.95257351e-01,  1.79073047e-02,\n",
+      "       -2.15334237e-01,  9.98005569e-02, -2.62755096e-01, -2.39337608e-01,\n",
+      "        3.44270498e-01,  1.50241479e-01, -2.96006531e-01, -3.81666899e-01,\n",
+      "       -1.19041964e-01,  6.18071109e-02,  6.49120063e-02,  9.94637012e-02,\n",
+      "        1.23297565e-01,  1.29930690e-01,  1.27305657e-01, -1.53804764e-01,\n",
+      "        7.04720244e-03, -1.33500487e-01, -1.51161134e-01,  1.13862932e-01,\n",
+      "       -2.44814962e-01, -8.95622373e-02,  4.76458520e-02, -5.92206642e-02,\n",
+      "        2.88407020e-02, -5.88610955e-02, -4.25557904e-02,  3.20446432e-01,\n",
+      "       -2.61463765e-02,  7.19539896e-02, -1.32161498e-01,  1.62227061e-02,\n",
+      "        1.20197656e-03, -2.03355268e-01, -6.83294982e-03, -2.82588631e-01,\n",
+      "       -1.61395460e-01, -5.05547188e-02, -2.27462381e-01, -1.70932785e-01,\n",
+      "        1.41351461e-01, -1.30069017e-01, -1.83039993e-01, -6.79691881e-02,\n",
+      "       -2.15642393e-01, -7.84436688e-02,  1.77202985e-01,  4.50607650e-02,\n",
+      "        7.02605024e-02,  8.01992565e-02, -1.55584306e-01, -2.00563252e-01,\n",
+      "        1.17082551e-01,  9.73844752e-02, -1.10356934e-01, -1.37866074e-02,\n",
+      "       -8.57235789e-02, -5.56467362e-02, -9.36827138e-02,  6.82030804e-03,\n",
+      "        6.92379624e-02, -2.28701755e-01,  6.70390204e-02,  1.34586483e-01,\n",
+      "        2.25231394e-01,  1.33322045e-01, -8.82911906e-02,  1.42205298e-01,\n",
+      "        2.41012901e-01,  7.94170424e-03, -7.02124536e-02,  2.51370400e-01,\n",
+      "        1.04983136e-01, -6.39194548e-02,  5.24720028e-02,  7.16757867e-03,\n",
+      "       -1.08169973e-01, -1.08731678e-02,  1.69618204e-02,  7.87692815e-02,\n",
+      "       -2.26539060e-01,  3.29003595e-02,  1.91522852e-01,  2.75921494e-01,\n",
+      "       -1.64055750e-01,  5.83723187e-02,  9.84422341e-02,  3.21688712e-01,\n",
+      "       -2.62310840e-02, -2.08140060e-01,  1.14425711e-01,  1.23823956e-01,\n",
+      "       -8.62085819e-03, -4.14005108e-02, -3.41566652e-02,  1.34680912e-01,\n",
+      "        4.27634180e-01,  1.42883554e-01, -1.54787973e-01,  7.96157196e-02,\n",
+      "        1.40678003e-01,  1.39171826e-02,  1.66003749e-01, -4.85638082e-02,\n",
+      "        5.88261709e-02,  9.51106697e-02,  1.81014258e-02,  1.44485429e-01,\n",
+      "        4.01205927e-01,  6.77596256e-02, -5.52676022e-01, -1.87850371e-01,\n",
+      "        1.12366609e-01, -6.84190989e-02,  9.48949978e-02,  2.23454669e-01,\n",
+      "       -1.69843137e-01,  2.09085494e-01,  4.29946512e-01, -3.36349100e-01,\n",
+      "        6.12608856e-03, -1.46142125e-01, -5.11092655e-02,  8.06671828e-02,\n",
+      "        1.81744993e-01, -6.78945482e-02, -5.77093139e-02,  1.52337164e-01,\n",
+      "        2.21259117e-01,  3.35705757e-01, -2.51778495e-02,  1.03662543e-01,\n",
+      "       -4.21361588e-02,  1.43061429e-01, -3.92947495e-01, -4.89463992e-02,\n",
+      "       -9.15660262e-02, -1.00108273e-01,  3.86523217e-01, -4.25569601e-02,\n",
+      "        4.10154127e-02, -3.41399819e-01,  2.13903114e-02,  8.09015241e-03,\n",
+      "        9.56344381e-02,  1.12729572e-01,  7.25207478e-02, -6.64384067e-02,\n",
+      "       -2.73666024e-01, -2.79651750e-02,  1.18422434e-01, -5.22459708e-02,\n",
+      "       -2.47057881e-02,  2.84700710e-02,  2.07451075e-01, -9.74238589e-02,\n",
+      "        8.08936954e-02,  4.07307222e-02, -1.35277033e-01,  2.18436554e-01,\n",
+      "        1.28792310e-02, -1.20433331e-01,  2.41929386e-02,  1.28128864e-02,\n",
+      "       -7.39881098e-02, -1.12995692e-01,  7.69245178e-02, -2.87000872e-02,\n",
+      "        1.64782573e-02, -2.78794408e-01, -2.64403820e-01, -2.43874848e-01,\n",
+      "        1.77457914e-01,  4.11631197e-01, -6.09753132e-02,  2.84967333e-01,\n",
+      "        9.81074646e-02, -2.68213183e-01,  1.52153388e-01,  2.42148209e-02,\n",
+      "        1.24371536e-01,  6.02926640e-03,  8.22689310e-02,  2.82294262e-04,\n",
+      "       -1.40584474e-02,  4.09389734e-02, -2.58334547e-01, -9.83026102e-02,\n",
+      "       -1.91695184e-01, -2.61005852e-02, -2.21736208e-01, -4.36628833e-02,\n",
+      "        9.49840024e-02, -5.16017936e-02,  2.17577979e-01,  2.58604765e-01,\n",
+      "        6.33814484e-02, -7.10158283e-03,  9.87893157e-03, -2.26405971e-02,\n",
+      "        1.67435139e-01,  2.90897069e-03,  2.35914681e-02,  5.43428905e-06],\n",
+      "      dtype=float32)], None)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file

From f48c842ebca13984f2648dd97f0aecababa659db Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Thu, 16 Sep 2021 18:23:04 +0800
Subject: [PATCH 092/149] [DOC]Update documents

---
 EduNLP/Formula/Formula.py               | 22 ++++++++++++++++++++++
 EduNLP/SIF/parser/parser.py             | 14 +++++++++++++-
 EduNLP/SIF/segment/segment.py           | 17 +++++++++++++++++
 EduNLP/SIF/tokenization/tokenization.py | 15 ++++++++++++++-
 4 files changed, 66 insertions(+), 2 deletions(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index 71038480..77e5f7d0 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -17,6 +17,18 @@ class Formula(object):
     """
     The part transform a formula to the parsed abstracted syntax tree.
 
+    Attributes
+    ------------
+    ast
+        show all ast details
+    elements
+        just show elements' id, type, text and role
+    ast_graph
+        draw a ast graph
+    to_str
+    resetable
+        return bool
+
     Parameters
     ----------
     formula: str or List[Dict]
@@ -129,6 +141,16 @@ class FormulaGroup(object):
     """
     The part transform a group of formula to the parsed abstracted syntax forest.
 
+    Attributes
+    ------------
+    to_str
+    ast
+        show all ast details
+    elements
+        just show elements' id, type, text and role
+    ast_graph
+        draw a ast graph
+
     Parameters
     ----------
     formula: str or List[Dict] or List[Formula]
diff --git a/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index cd2bf65e..e896dc0e 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -3,7 +3,19 @@
 
 
 class Parser:
-    """initial data and special variable"""
+    """
+    initial data and special variable
+
+    Attributes
+    ----------
+    get_token
+        Get different elements in the item.
+    txt_list
+        show txt list
+    description_list
+        use Parser to process and describe the txt
+    
+    """
     def __init__(self, data):
         self.lookahead = 0
         self.head = 0
diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index 2bbefdca..7e9ed5ed 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -82,6 +82,23 @@ class SepSegment(str):
 class SegmentList(object):
     """
 
+    Attributes
+    ----------
+    segments
+        show all segments
+    text_segments
+        show text segments
+    formula_segments
+        show formula segments
+    figure_segments
+        show figure sements
+    ques_mark_segments
+        show question mark segments
+    tag_segments
+        show tag segments
+    describe
+        show number of each elements
+
     Parameters
     ----------
     item
diff --git a/EduNLP/SIF/tokenization/tokenization.py b/EduNLP/SIF/tokenization/tokenization.py
index 53f6fe75..93eb61a1 100644
--- a/EduNLP/SIF/tokenization/tokenization.py
+++ b/EduNLP/SIF/tokenization/tokenization.py
@@ -26,7 +26,20 @@ class TokenList(object):
 
     Attributes
     -------------
-
+    tokens
+        show all tokens
+    text_tokens
+        show text tokens
+    formula_tokens
+        show formula tokens
+    figure_tokens
+        show figure tokens
+    ques_mark_tokens
+        show question mark tokens
+    tag_tokens
+        show tag tokens
+    describe
+        show number of each elements
     """
     def __init__(self, segment_list: SegmentList, text_params=None, formula_params=None, figure_params=None):
         self._tokens = []

From 2f3ed722062324c5cbaf9c9f4a4d0590439871b3 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Thu, 16 Sep 2021 19:15:20 +0800
Subject: [PATCH 093/149] fix pytest error

---
 EduNLP/SIF/parser/parser.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index e896dc0e..471bb450 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -14,7 +14,6 @@ class Parser:
         show txt list
     description_list
         use Parser to process and describe the txt
-    
     """
     def __init__(self, data):
         self.lookahead = 0

From 98393f4642e3ed2e18cf1c33524b848b32cc52fd Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 19:16:21 +0800
Subject: [PATCH 094/149] update total_vector.ipynb

---
 examples/vectorization/total_vector.ipynb | 9169 +--------------------
 1 file changed, 321 insertions(+), 8848 deletions(-)

diff --git a/examples/vectorization/total_vector.ipynb b/examples/vectorization/total_vector.ipynb
index d1c58239..4a91634c 100644
--- a/examples/vectorization/total_vector.ipynb
+++ b/examples/vectorization/total_vector.ipynb
@@ -51,11 +51,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "source": [
     "from EduNLP.I2V import D2V"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
    "metadata": {}
   },
   {
@@ -70,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "source": [
     "items = [\r\n",
     "r\"1如图几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
@@ -89,8801 +98,258 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "source": [
-    "model_path = \"../../data/d2v/test_d2v_256.bin\"\r\n",
+    "model_path = \"./d2v/test_d2v_256.bin\"\r\n",
     "i2v = D2V(\"pure_text\",\"d2v\",filepath=model_path, pretrained_t2v = False)\r\n",
     "\r\n",
     "item_vectors, token_vectors = i2v(items)\r\n",
-    "print(item_vectors,  token_vectors)\r\n",
-    "print(len(item_vectors), item_vectors[0].shape) # For d2v, token_vector is None"
+    "print(item_vectors[0])\r\n",
+    "print(token_vectors) # For d2v, token_vector is None\r\n",
+    "print(\"shape of item_vector: \",len(item_vectors), item_vectors[0].shape) "
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[array([ 0.10634826, -0.10273553, -0.04942605,  0.15323296,  0.25780037,\n",
-      "       -0.06630539, -0.02834828,  0.0065832 , -0.07495898,  0.06529831,\n",
-      "        0.07989643,  0.14697047, -0.06513839, -0.23898382,  0.04266138,\n",
-      "        0.15211952,  0.17704317,  0.02768603, -0.1854407 ,  0.21742067,\n",
-      "        0.00984387,  0.06774732, -0.02058181, -0.13266353, -0.20851782,\n",
-      "        0.00558438,  0.00460878, -0.05139542, -0.06485009, -0.04847892,\n",
-      "        0.09366203,  0.13947366, -0.07552954,  0.05977268, -0.09442269,\n",
-      "        0.12721013,  0.13273577, -0.02567345,  0.09778284, -0.08030444,\n",
-      "       -0.05697263, -0.02538129, -0.04187621, -0.03123644,  0.11242954,\n",
-      "       -0.00650479,  0.06117244,  0.06449873,  0.05163544,  0.01810673,\n",
-      "        0.03808153,  0.03746097,  0.1275719 ,  0.07381074, -0.14620851,\n",
-      "        0.05757713,  0.02838781, -0.12927914,  0.07510903,  0.08147711,\n",
-      "       -0.06523656, -0.01486957,  0.02304654,  0.04361376,  0.21421443,\n",
-      "       -0.02614699, -0.03873423,  0.21355043, -0.09329057, -0.09335907,\n",
-      "        0.08157952,  0.00625934, -0.05051626, -0.07981606, -0.03226125,\n",
-      "        0.08958097, -0.04725425, -0.06789905,  0.06730124,  0.06633109,\n",
-      "       -0.07370041, -0.13176298, -0.01786646,  0.11614659, -0.04729063,\n",
-      "        0.06670643,  0.12553535, -0.1768527 , -0.09982615, -0.07682986,\n",
-      "        0.06889272, -0.04036296,  0.03164692, -0.1446157 ,  0.03707608,\n",
-      "        0.13580611, -0.07748953, -0.02646814, -0.01086655, -0.09684405,\n",
-      "       -0.15440914, -0.1067182 ,  0.0349819 ,  0.10395218, -0.00877615,\n",
-      "        0.05640977, -0.01675119,  0.03014355,  0.12512377,  0.01414524,\n",
-      "        0.0211464 , -0.06591589,  0.08297148,  0.07012929, -0.10675234,\n",
-      "       -0.04061191,  0.01302199,  0.11621079,  0.00885058, -0.0304758 ,\n",
-      "       -0.01553952, -0.12865704,  0.17576885, -0.02950172, -0.01326492,\n",
-      "        0.01504841,  0.09476334, -0.03984889,  0.00763261, -0.01630022,\n",
-      "       -0.03672133,  0.01365909,  0.05986133, -0.10593513, -0.08282372,\n",
-      "       -0.08374579, -0.04833878,  0.0489895 ,  0.00875692, -0.03651298,\n",
-      "       -0.01757672, -0.01623743,  0.23217827,  0.15424854, -0.26954046,\n",
-      "       -0.04381514,  0.03540273,  0.06735364,  0.09081568,  0.07033931,\n",
-      "       -0.14287043, -0.1028204 , -0.04575491, -0.01768357, -0.08948888,\n",
-      "        0.07118583,  0.1542743 ,  0.09116074, -0.08839961,  0.01807798,\n",
-      "        0.12579061, -0.03509037,  0.08215491, -0.06514787,  0.00351805,\n",
-      "        0.04778312,  0.04185999,  0.08844823,  0.10574003, -0.01214823,\n",
-      "        0.04865406, -0.03167628,  0.02753052,  0.05206006, -0.15243334,\n",
-      "       -0.02005264, -0.00607761, -0.01364831,  0.10955886, -0.0010281 ,\n",
-      "        0.11600163, -0.04597978,  0.0601487 , -0.05470827,  0.11942582,\n",
-      "        0.00830158, -0.13237426, -0.15353964, -0.07234719,  0.04098862,\n",
-      "        0.15481706,  0.01778992,  0.14323051,  0.09922674,  0.12223933,\n",
-      "        0.0526598 ,  0.0408572 , -0.13533838,  0.02140845, -0.1030432 ,\n",
-      "       -0.17960373, -0.0495283 ,  0.14249511,  0.0465369 , -0.04416716,\n",
-      "        0.11465154, -0.2066736 , -0.02712584, -0.06553549,  0.0557267 ,\n",
-      "        0.04696037, -0.02916378,  0.07578196,  0.00608585,  0.00944843,\n",
-      "        0.02514744,  0.16452865,  0.0508883 , -0.15239422, -0.01861594,\n",
-      "       -0.1283559 ,  0.06792712,  0.0826408 , -0.05190438,  0.03456785,\n",
-      "       -0.00661102,  0.00788855,  0.02678101, -0.02560065, -0.00632013,\n",
-      "       -0.193216  , -0.1322564 , -0.03174976, -0.04030088,  0.01397508,\n",
-      "        0.07423516,  0.04445267,  0.0541687 ,  0.083343  , -0.15150657,\n",
-      "        0.00937343,  0.05487543,  0.02184621, -0.00872194, -0.00329627,\n",
-      "       -0.05453907,  0.05444683, -0.02034983, -0.0742873 , -0.01699653,\n",
-      "       -0.16100745,  0.10637001, -0.12436336, -0.03511102, -0.20044689,\n",
-      "       -0.02411474], dtype=float32), array([ 1.36041582e-01, -1.08781166e-01, -5.83929941e-02,  1.91058487e-01,\n",
-      "        3.24488580e-01, -6.02278523e-02, -4.59153317e-02, -2.24303032e-04,\n",
-      "       -8.76015052e-02,  8.96644667e-02,  1.04697935e-01,  1.83520690e-01,\n",
-      "       -7.28873909e-02, -3.05169255e-01,  4.17109728e-02,  1.82436183e-01,\n",
-      "        2.26057917e-01,  2.32112277e-02, -2.30914488e-01,  2.76603520e-01,\n",
-      "       -4.85374872e-03,  7.58373216e-02, -2.29087640e-02, -1.79299608e-01,\n",
-      "       -2.54585207e-01,  1.64706651e-02,  7.45610800e-03, -7.51212165e-02,\n",
-      "       -7.42579326e-02, -4.80351038e-02,  1.25985965e-01,  1.73480287e-01,\n",
-      "       -1.01882420e-01,  6.75791055e-02, -1.05399802e-01,  1.51736602e-01,\n",
-      "        1.63938925e-01, -3.84903289e-02,  1.10449508e-01, -9.40715224e-02,\n",
-      "       -6.31079897e-02, -3.84142622e-02, -4.99494635e-02, -4.23108302e-02,\n",
-      "        1.55812502e-01, -1.16190566e-02,  5.13239168e-02,  6.35165498e-02,\n",
-      "        6.25813156e-02,  2.21146382e-02,  6.36933595e-02,  5.06636649e-02,\n",
-      "        1.47942111e-01,  9.66929942e-02, -1.76337466e-01,  7.13716373e-02,\n",
-      "        2.70809997e-02, -1.57342017e-01,  9.11386758e-02,  9.41570252e-02,\n",
-      "       -9.42364857e-02, -5.13231056e-03,  1.36982789e-02,  5.77186793e-02,\n",
-      "        2.64365107e-01, -3.76182236e-02, -4.40398045e-02,  2.51175314e-01,\n",
-      "       -1.05481505e-01, -1.20290719e-01,  9.69798341e-02,  1.16603952e-02,\n",
-      "       -7.56024718e-02, -1.08631417e-01, -4.93911207e-02,  1.18807659e-01,\n",
-      "       -5.85292391e-02, -8.24716613e-02,  7.88261220e-02,  8.72761682e-02,\n",
-      "       -1.01744331e-01, -1.44680306e-01, -2.09336001e-02,  1.33469954e-01,\n",
-      "       -3.82428057e-02,  7.42774233e-02,  1.48893908e-01, -2.09778398e-01,\n",
-      "       -1.35315523e-01, -8.64634812e-02,  9.07569081e-02, -5.20299003e-02,\n",
-      "        5.39300404e-02, -1.71408862e-01,  3.55036259e-02,  1.70207739e-01,\n",
-      "       -8.76016989e-02, -2.80412864e-02, -2.70368508e-03, -1.22690618e-01,\n",
-      "       -1.63871482e-01, -1.22708760e-01,  3.35654393e-02,  1.26829579e-01,\n",
-      "       -1.43117364e-02,  7.96694681e-02, -3.10931951e-02,  2.37075817e-02,\n",
-      "        1.55911028e-01,  1.69575382e-02,  1.54271908e-02, -9.58202332e-02,\n",
-      "        1.09241918e-01,  8.82035494e-02, -1.39677778e-01, -5.01841754e-02,\n",
-      "        2.18386631e-02,  1.57107875e-01,  7.93855730e-03, -3.16538438e-02,\n",
-      "       -1.25909718e-02, -1.62555054e-01,  2.32526332e-01, -3.71498615e-02,\n",
-      "       -2.24065650e-02,  2.61516422e-02,  1.20939091e-01, -6.72491416e-02,\n",
-      "       -4.27399318e-05, -3.77265252e-02, -4.58711684e-02,  2.53423229e-02,\n",
-      "        8.39713439e-02, -1.19929694e-01, -1.07749395e-01, -1.10882133e-01,\n",
-      "       -4.97324318e-02,  6.16546497e-02, -1.15693109e-02, -3.91881578e-02,\n",
-      "       -2.29061693e-02, -2.30852738e-02,  3.04617375e-01,  2.08306596e-01,\n",
-      "       -3.39470148e-01, -5.02127111e-02,  5.37177026e-02,  9.11831036e-02,\n",
-      "        1.14661761e-01,  9.45202038e-02, -1.62573129e-01, -1.38315439e-01,\n",
-      "       -6.47830069e-02, -2.83303317e-02, -1.22525014e-01,  9.37087536e-02,\n",
-      "        1.96664244e-01,  1.07929356e-01, -1.03713363e-01,  1.60198733e-02,\n",
-      "        1.63076490e-01, -5.17086089e-02,  1.10869825e-01, -7.68428966e-02,\n",
-      "        8.37910455e-03,  5.56400865e-02,  6.31182268e-02,  1.21523716e-01,\n",
-      "        1.36712998e-01, -3.43602896e-02,  5.95005825e-02, -3.38348858e-02,\n",
-      "        3.81066576e-02,  6.04561493e-02, -1.77799106e-01, -3.31094116e-02,\n",
-      "       -1.12194773e-02, -3.21154818e-02,  1.08467303e-01, -4.27856669e-03,\n",
-      "        1.51000738e-01, -6.47066757e-02,  6.33488074e-02, -6.28948510e-02,\n",
-      "        1.29441455e-01,  4.04741522e-03, -1.72044575e-01, -1.91321328e-01,\n",
-      "       -8.56087804e-02,  4.62383777e-02,  2.01764986e-01,  3.43908928e-02,\n",
-      "        1.77972093e-01,  1.27216563e-01,  1.52360231e-01,  5.92516772e-02,\n",
-      "        4.44654636e-02, -1.71815068e-01,  5.64131141e-02, -1.30362645e-01,\n",
-      "       -2.15643197e-01, -6.48207739e-02,  1.80758730e-01,  6.18097410e-02,\n",
-      "       -6.83161020e-02,  1.69224367e-01, -2.53407449e-01, -4.33427393e-02,\n",
-      "       -8.90076980e-02,  7.33624548e-02,  5.79761080e-02, -3.76918130e-02,\n",
-      "        8.55930522e-02,  2.99188751e-03,  1.56438854e-02,  2.43693665e-02,\n",
-      "        2.17681393e-01,  6.79032505e-02, -1.83962315e-01, -1.40863275e-02,\n",
-      "       -1.53916925e-01,  8.76621082e-02,  9.75017548e-02, -6.90853447e-02,\n",
-      "        3.22550870e-02, -1.22331316e-02,  8.18267651e-03,  3.01490221e-02,\n",
-      "       -1.40115758e-02,  1.17691020e-02, -2.29610443e-01, -1.88834459e-01,\n",
-      "       -4.37003709e-02, -3.97654139e-02,  2.05663759e-02,  9.48096588e-02,\n",
-      "        4.75249775e-02,  6.99462742e-02,  9.24514905e-02, -1.96493998e-01,\n",
-      "        3.27189267e-02,  4.80476059e-02,  3.26564163e-02, -1.90033496e-03,\n",
-      "        3.73094343e-03, -5.62442988e-02,  5.95186092e-02, -2.72717867e-02,\n",
-      "       -8.34571719e-02, -2.22789161e-02, -1.91354036e-01,  1.27723783e-01,\n",
-      "       -1.69917479e-01, -3.11680529e-02, -2.62214512e-01, -2.82380339e-02],\n",
-      "      dtype=float32)] None\n",
-      "2 (256,)\n"
+      "[ 0.10603202 -0.10537548 -0.04773913  0.15573525  0.25898772 -0.06423073\n",
+      " -0.02817309  0.0068187  -0.07323898  0.06517941  0.07943465  0.14800762\n",
+      " -0.06772996 -0.23892336  0.04638071  0.1539897   0.17565852  0.02895202\n",
+      " -0.18859927  0.2180874   0.00909669  0.06621908 -0.02090263 -0.13006955\n",
+      " -0.21020882  0.00618349  0.00531093 -0.04877732 -0.06709669 -0.04705636\n",
+      "  0.09211092  0.13896106 -0.07455818  0.06019318 -0.09071473  0.12701215\n",
+      "  0.13018885 -0.02784999  0.10064025 -0.07757548 -0.05522636 -0.02657779\n",
+      " -0.04159601 -0.03008493  0.10995369 -0.00587291  0.05902484  0.06532726\n",
+      "  0.04887666  0.01902074  0.03713945  0.03691795  0.12516327  0.07410683\n",
+      " -0.14467879  0.05678609  0.02574336 -0.1320522   0.07502684  0.07929367\n",
+      " -0.06655917 -0.0144536   0.02595847  0.04403471  0.21743318 -0.02525017\n",
+      " -0.0416184   0.21441495 -0.09308876 -0.09418222  0.08030997  0.00492512\n",
+      " -0.04921608 -0.07808654 -0.03323801  0.0879296  -0.04668022 -0.0696011\n",
+      "  0.06708417  0.06555629 -0.07418457 -0.13050951 -0.01802611  0.11730465\n",
+      " -0.0479078   0.06389603  0.12324224 -0.17746696 -0.09874132 -0.07683054\n",
+      "  0.06596514 -0.04210603  0.03182372 -0.1455575   0.03900012  0.13290605\n",
+      " -0.07672353 -0.02826704 -0.00803517 -0.09681892 -0.15212329 -0.10524812\n",
+      "  0.03367848  0.10413344 -0.0089777   0.0583192  -0.01553376  0.02675472\n",
+      "  0.12278829  0.01667286  0.01958599 -0.06468913  0.08307286  0.07304061\n",
+      " -0.10451686 -0.04367925  0.0143903   0.11394493  0.00759796 -0.03158598\n",
+      " -0.01733392 -0.12918264  0.1761386  -0.02913121 -0.01364522  0.01497996\n",
+      "  0.09318532 -0.03958051  0.00465893 -0.01766865 -0.03531685  0.01445563\n",
+      "  0.05919004 -0.10480376 -0.08359206 -0.08283877 -0.04920156  0.0486405\n",
+      "  0.0059151  -0.03783213 -0.01815955 -0.0157437   0.2334638   0.15233137\n",
+      " -0.2698607  -0.04492244  0.03728078  0.06730984  0.09165722  0.07212968\n",
+      " -0.1418279  -0.10517611 -0.0469548  -0.01878718 -0.08850995  0.07481015\n",
+      "  0.15206474  0.0923347  -0.08849481  0.01736124  0.12647657 -0.03515046\n",
+      "  0.07980374 -0.06639698  0.00411603  0.0479564   0.04197159  0.0854824\n",
+      "  0.103918   -0.01195896  0.05059687 -0.03206704  0.0277859   0.05210226\n",
+      " -0.15160614 -0.01996467 -0.00720571 -0.01154042  0.10944121 -0.00173247\n",
+      "  0.11439639 -0.04765575  0.05989955 -0.05265343  0.11914644  0.0085329\n",
+      " -0.13220952 -0.1538407  -0.07261448  0.04143476  0.15447438  0.02005473\n",
+      "  0.14354227  0.10015973  0.12290012  0.05011315  0.0425972  -0.13731483\n",
+      "  0.02323116 -0.1031343  -0.17960383 -0.04875064  0.14352156  0.04516263\n",
+      " -0.04433561  0.11548021 -0.2057457  -0.02778868 -0.06643672  0.05604808\n",
+      "  0.04864014 -0.03015646  0.07734285  0.00573904  0.01155302  0.02486293\n",
+      "  0.16259493  0.05099423 -0.15283771 -0.01909443 -0.12749314  0.06718695\n",
+      "  0.08334705 -0.05442797  0.03448674 -0.00542413  0.00832719  0.02702984\n",
+      " -0.02359959 -0.00855793 -0.19381124 -0.13036375 -0.0351354  -0.03983364\n",
+      "  0.0133928   0.07395492  0.04119737  0.05661048  0.08151852 -0.1529391\n",
+      "  0.00742581  0.05521343  0.02089992 -0.00824985 -0.00211842 -0.05555268\n",
+      "  0.05448649 -0.02032894 -0.0760811  -0.01713146 -0.16146915  0.10822926\n",
+      " -0.1240218  -0.03639562 -0.20028785 -0.02452293]\n",
+      "None\n",
+      "shape of item_vector:  2 (256,)\n"
      ]
     }
    ],
    "metadata": {}
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "source": [
-    "from EduData import get_data\r\n",
-    "\r\n",
-    "get_data(\"open-luna\", \"../../data/\")\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!\n",
-      "  warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n",
-      "downloader, INFO http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json is saved as ../../data/OpenLUNA.json\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Downloading ../../data/OpenLUNA.json 100.00%: 269KB | 269KB\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'../../data/OpenLUNA.json'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
+    "### W2V"
    ],
-   "metadata": {
-    "collapsed": true
-   }
+   "metadata": {}
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 4,
    "source": [
-    "### W2V"
+    "from EduNLP.I2V import W2V"
    ],
+   "outputs": [],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "source": [
-    "from EduNLP.I2V import W2V\r\n",
-    "\r\n",
-    "model_path = \"../../data/w2v/general_literal_300/general_literal_300.kv\"\r\n",
+    "model_path = \"./w2v/general_literal_300/general_literal_300.kv\"\r\n",
     "i2v = W2V(\"pure_text\",\"w2v\",filepath=model_path, pretrained_t2v = False)\r\n",
-    "try:\r\n",
-    "    item_vectors, token_vectors = i2v(items)\r\n",
-    "    print(item_vectors) # 每个句子vector 为np.ndarray\r\n",
-    "    print(token_vectors) # 每个单词vector list\r\n",
-    "    print(len(item_vectors), item_vectors[0].shape,len(token_vectors), len(token_vectors[0]),len(token_vectors[0][0]))\r\n",
-    "except Exception as e:\r\n",
-    "    print(e)"
+    "\r\n",
+    "item_vectors, token_vectors = i2v(items)\r\n",
+    "\r\n",
+    "print(item_vectors[0])\r\n",
+    "print(token_vectors[0][0])\r\n",
+    "print(\"shape of item_vectors: \", len(item_vectors), item_vectors[0].shape)\r\n",
+    "print(\"shape of token_vectors: \", len(token_vectors), len(token_vectors[0]), len(token_vectors[0][0]))"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[array([-1.34266680e-03,  5.19845746e-02, -1.98070258e-02, -4.17470075e-02,\n",
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-      "        3.07940125e-01,  1.92060292e-01,  1.31043345e-02,  6.10962026e-02,\n",
-      "        2.21019030e-01, -3.53541046e-01,  1.34150490e-01,  1.14867561e-01,\n",
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-      "       -4.36594114e-02, -1.37816787e-01, -1.07707813e-01, -1.80805102e-01,\n",
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-      "        2.16104537e-01, -3.05427730e-01, -2.46336535e-01,  7.59556964e-02],\n",
-      "      dtype=float32), array([ 3.99896456e-03,  8.05600137e-02, -3.91732380e-02, -4.86987866e-02,\n",
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+      "  1.36686012e-03 -2.61674239e-03 -1.52974128e-04 -2.72300956e-03\n",
+      "  1.70241436e-03 -6.61658472e-04 -2.15324806e-03 -2.46914220e-03\n",
+      "  1.41488796e-03 -3.25874239e-03 -2.29610526e-03 -2.22696620e-03\n",
+      " -2.09132349e-03 -2.79461709e-03 -3.24834906e-03 -1.12362858e-03]\n",
+      "shape of item_vectors:  2 (300,)\n",
+      "shape of token_vectors:  2 55 300\n"
      ]
     }
    ],
@@ -8912,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "source": [
     "from EduNLP import get_pretrained_i2v"
    ],
@@ -8931,11 +397,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "source": [
-    "item = {\r\n",
+    "items = [\r\n",
     "\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
-    "}\r\n"
+    "]\r\n"
    ],
    "outputs": [],
    "metadata": {}
@@ -8950,23 +416,24 @@
   {
    "cell_type": "markdown",
    "source": [
-    "根据题目所属学科选择预训练模型：    \n",
-    "\n",
-    " 预训练模型名称 | 模型训练数据的所属学科   \n",
-    " -------------- | ----------------------   \n",
-    " d2v_all_256    | 全学科                   \n",
-    " d2v_sci_256    | 理科                     \n",
-    " d2v_eng_256    | 英语                     \n",
-    " d2v_lit_256    | 文科                      \n",
-    "\n"
+    "根据题目所属学科选择预训练模型：    \r\n",
+    "\r\n",
+    " 预训练模型名称 | 模型训练数据的所属学科   \r\n",
+    " -------------- | ----------------------   \r\n",
+    " d2v_all_256    | 全学科                   \r\n",
+    " d2v_sci_256    | 理科                     \r\n",
+    " d2v_eng_256    | 英语                     \r\n",
+    " d2v_lit_256    | 文科                      \r\n",
+    " w2v_eng_300    | 英语                     \r\n",
+    " w2v_lit_300    | 文科             "
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "source": [
-    "i2v = get_pretrained_i2v(\"d2v_sci_256\")"
+    "i2v = get_pretrained_i2v(\"d2v_sci_256\", model_dir=\"./d2v\")"
    ],
    "outputs": [
     {
@@ -8974,7 +441,7 @@
      "name": "stderr",
      "text": [
       "EduNLP, INFO Use pretrained t2v model d2v_sci_256\n",
-      "downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as /home/lvrui/.EduNLP/model/general_science_256.zip\n",
+      "downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as d2v\\general_science_256.zip\n",
       "downloader, INFO file existed, skipped\n"
      ]
     }
@@ -8993,80 +460,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "source": [
-    "print(i2v(item))"
+    "item_vectors, token_vectors = i2v(items)\r\n",
+    "print(item_vectors)\r\n",
+    "print(token_vectors)"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "([array([-2.38860980e-01,  7.09681511e-02, -2.71706015e-01,  1.64714813e-01,\n",
-      "        2.81243492e-02, -1.82386801e-01,  9.22331214e-02,  1.31783364e-02,\n",
-      "        9.15176645e-02,  3.14464062e-01,  9.37800854e-02, -2.28523940e-01,\n",
-      "       -2.60597020e-01,  6.49375990e-02,  9.75619778e-02, -1.97933778e-01,\n",
-      "        8.29798505e-02, -2.26491719e-01, -1.77030653e-01, -3.56038064e-02,\n",
-      "        6.22844934e-01, -2.66110301e-01,  8.00080523e-02, -1.60827965e-01,\n",
-      "       -1.78654417e-01, -1.33000776e-01,  2.76004016e-01,  1.79546073e-01,\n",
-      "        8.71006995e-02,  2.33958483e-01,  1.76031828e-01,  1.55402005e-01,\n",
-      "       -1.38987333e-01, -1.92975491e-01, -1.09528497e-01,  1.12305783e-01,\n",
-      "        2.32549626e-02,  7.75609687e-02, -2.43636876e-01,  6.35311157e-02,\n",
-      "       -4.82399836e-02, -2.24204548e-02,  7.49862418e-02, -1.91449642e-01,\n",
-      "        9.72701237e-02,  4.00750965e-01,  2.81992704e-01,  3.07581365e-01,\n",
-      "       -4.68867749e-01, -3.03025767e-02, -1.95257351e-01,  1.79073047e-02,\n",
-      "       -2.15334237e-01,  9.98005569e-02, -2.62755096e-01, -2.39337608e-01,\n",
-      "        3.44270498e-01,  1.50241479e-01, -2.96006531e-01, -3.81666899e-01,\n",
-      "       -1.19041964e-01,  6.18071109e-02,  6.49120063e-02,  9.94637012e-02,\n",
-      "        1.23297565e-01,  1.29930690e-01,  1.27305657e-01, -1.53804764e-01,\n",
-      "        7.04720244e-03, -1.33500487e-01, -1.51161134e-01,  1.13862932e-01,\n",
-      "       -2.44814962e-01, -8.95622373e-02,  4.76458520e-02, -5.92206642e-02,\n",
-      "        2.88407020e-02, -5.88610955e-02, -4.25557904e-02,  3.20446432e-01,\n",
-      "       -2.61463765e-02,  7.19539896e-02, -1.32161498e-01,  1.62227061e-02,\n",
-      "        1.20197656e-03, -2.03355268e-01, -6.83294982e-03, -2.82588631e-01,\n",
-      "       -1.61395460e-01, -5.05547188e-02, -2.27462381e-01, -1.70932785e-01,\n",
-      "        1.41351461e-01, -1.30069017e-01, -1.83039993e-01, -6.79691881e-02,\n",
-      "       -2.15642393e-01, -7.84436688e-02,  1.77202985e-01,  4.50607650e-02,\n",
-      "        7.02605024e-02,  8.01992565e-02, -1.55584306e-01, -2.00563252e-01,\n",
-      "        1.17082551e-01,  9.73844752e-02, -1.10356934e-01, -1.37866074e-02,\n",
-      "       -8.57235789e-02, -5.56467362e-02, -9.36827138e-02,  6.82030804e-03,\n",
-      "        6.92379624e-02, -2.28701755e-01,  6.70390204e-02,  1.34586483e-01,\n",
-      "        2.25231394e-01,  1.33322045e-01, -8.82911906e-02,  1.42205298e-01,\n",
-      "        2.41012901e-01,  7.94170424e-03, -7.02124536e-02,  2.51370400e-01,\n",
-      "        1.04983136e-01, -6.39194548e-02,  5.24720028e-02,  7.16757867e-03,\n",
-      "       -1.08169973e-01, -1.08731678e-02,  1.69618204e-02,  7.87692815e-02,\n",
-      "       -2.26539060e-01,  3.29003595e-02,  1.91522852e-01,  2.75921494e-01,\n",
-      "       -1.64055750e-01,  5.83723187e-02,  9.84422341e-02,  3.21688712e-01,\n",
-      "       -2.62310840e-02, -2.08140060e-01,  1.14425711e-01,  1.23823956e-01,\n",
-      "       -8.62085819e-03, -4.14005108e-02, -3.41566652e-02,  1.34680912e-01,\n",
-      "        4.27634180e-01,  1.42883554e-01, -1.54787973e-01,  7.96157196e-02,\n",
-      "        1.40678003e-01,  1.39171826e-02,  1.66003749e-01, -4.85638082e-02,\n",
-      "        5.88261709e-02,  9.51106697e-02,  1.81014258e-02,  1.44485429e-01,\n",
-      "        4.01205927e-01,  6.77596256e-02, -5.52676022e-01, -1.87850371e-01,\n",
-      "        1.12366609e-01, -6.84190989e-02,  9.48949978e-02,  2.23454669e-01,\n",
-      "       -1.69843137e-01,  2.09085494e-01,  4.29946512e-01, -3.36349100e-01,\n",
-      "        6.12608856e-03, -1.46142125e-01, -5.11092655e-02,  8.06671828e-02,\n",
-      "        1.81744993e-01, -6.78945482e-02, -5.77093139e-02,  1.52337164e-01,\n",
-      "        2.21259117e-01,  3.35705757e-01, -2.51778495e-02,  1.03662543e-01,\n",
-      "       -4.21361588e-02,  1.43061429e-01, -3.92947495e-01, -4.89463992e-02,\n",
-      "       -9.15660262e-02, -1.00108273e-01,  3.86523217e-01, -4.25569601e-02,\n",
-      "        4.10154127e-02, -3.41399819e-01,  2.13903114e-02,  8.09015241e-03,\n",
-      "        9.56344381e-02,  1.12729572e-01,  7.25207478e-02, -6.64384067e-02,\n",
-      "       -2.73666024e-01, -2.79651750e-02,  1.18422434e-01, -5.22459708e-02,\n",
-      "       -2.47057881e-02,  2.84700710e-02,  2.07451075e-01, -9.74238589e-02,\n",
-      "        8.08936954e-02,  4.07307222e-02, -1.35277033e-01,  2.18436554e-01,\n",
-      "        1.28792310e-02, -1.20433331e-01,  2.41929386e-02,  1.28128864e-02,\n",
-      "       -7.39881098e-02, -1.12995692e-01,  7.69245178e-02, -2.87000872e-02,\n",
-      "        1.64782573e-02, -2.78794408e-01, -2.64403820e-01, -2.43874848e-01,\n",
-      "        1.77457914e-01,  4.11631197e-01, -6.09753132e-02,  2.84967333e-01,\n",
-      "        9.81074646e-02, -2.68213183e-01,  1.52153388e-01,  2.42148209e-02,\n",
-      "        1.24371536e-01,  6.02926640e-03,  8.22689310e-02,  2.82294262e-04,\n",
-      "       -1.40584474e-02,  4.09389734e-02, -2.58334547e-01, -9.83026102e-02,\n",
-      "       -1.91695184e-01, -2.61005852e-02, -2.21736208e-01, -4.36628833e-02,\n",
-      "        9.49840024e-02, -5.16017936e-02,  2.17577979e-01,  2.58604765e-01,\n",
-      "        6.33814484e-02, -7.10158283e-03,  9.87893157e-03, -2.26405971e-02,\n",
-      "        1.67435139e-01,  2.90897069e-03,  2.35914681e-02,  5.43428905e-06],\n",
-      "      dtype=float32)], None)\n"
+      "[array([-0.23861311,  0.06892798, -0.27065727,  0.16547263,  0.02818857,\n",
+      "       -0.18185084,  0.09226187,  0.01612627,  0.0921795 ,  0.3134312 ,\n",
+      "        0.09265023, -0.22529641, -0.25788078,  0.06702194,  0.09765045,\n",
+      "       -0.19932257,  0.08527228, -0.22684543, -0.1776405 , -0.03682012,\n",
+      "        0.6210964 , -0.26637274,  0.08060682, -0.15860714, -0.17825642,\n",
+      "       -0.13271384,  0.27331072,  0.18202724,  0.08430962,  0.23299456,\n",
+      "        0.179898  ,  0.1571772 , -0.1406754 , -0.19508898, -0.11265783,\n",
+      "        0.11396482,  0.0223774 ,  0.07824919, -0.2421433 ,  0.06195279,\n",
+      "       -0.04763965, -0.02037446,  0.07481094, -0.1908799 ,  0.09688905,\n",
+      "        0.3995564 ,  0.28225863,  0.30547026, -0.46538818, -0.02891348,\n",
+      "       -0.19343005,  0.01966276, -0.21590087,  0.09774096, -0.26137134,\n",
+      "       -0.23963049,  0.34259936,  0.14825426, -0.2987728 , -0.38039675,\n",
+      "       -0.12087625,  0.05897354,  0.06351897,  0.10188989,  0.12092843,\n",
+      "        0.13229063,  0.12786968, -0.15378596,  0.00724137, -0.13644631,\n",
+      "       -0.15164569,  0.11535735, -0.24394232, -0.08835315,  0.05014084,\n",
+      "       -0.05980775,  0.03040357, -0.05804552, -0.04122322,  0.31905708,\n",
+      "       -0.02468318,  0.06953011, -0.1299219 ,  0.01482821, -0.00126122,\n",
+      "       -0.20185567, -0.00784766, -0.28023243, -0.16416278, -0.04939609,\n",
+      "       -0.22619021, -0.17099814,  0.1434735 , -0.13193442, -0.18329675,\n",
+      "       -0.06873035, -0.21638975, -0.0767743 ,  0.17778671,  0.0459166 ,\n",
+      "        0.0719557 ,  0.0797654 , -0.15445784, -0.20094277,  0.11860424,\n",
+      "        0.09521067, -0.10993416, -0.01273298, -0.0857757 , -0.05475522,\n",
+      "       -0.09463413,  0.00845256,  0.06638184, -0.22701578,  0.06599791,\n",
+      "        0.1323833 ,  0.2227748 ,  0.13431212, -0.08537175,  0.14300612,\n",
+      "        0.24459998,  0.00735889, -0.07123663,  0.24863936,  0.10320719,\n",
+      "       -0.06399037,  0.0537433 ,  0.00862593, -0.10747737, -0.01009098,\n",
+      "        0.01707896,  0.07951383, -0.2245529 ,  0.03152119,  0.19090259,\n",
+      "        0.27611575, -0.16507478,  0.05977706,  0.09740735,  0.32154247,\n",
+      "       -0.02540598, -0.20875612,  0.11484967,  0.12112009, -0.00937327,\n",
+      "       -0.03855037, -0.03728763,  0.13645649,  0.42706412,  0.14456204,\n",
+      "       -0.1542145 ,  0.07858715,  0.14076898,  0.01195827,  0.16896723,\n",
+      "       -0.0516856 ,  0.05795754,  0.09602529,  0.02058077,  0.14346235,\n",
+      "        0.3984762 ,  0.06770886, -0.5524451 , -0.18779868,  0.11151859,\n",
+      "       -0.06967582,  0.09465033,  0.2242416 , -0.17179447,  0.20837718,\n",
+      "        0.43269685, -0.33945957,  0.00746959, -0.14856125, -0.04883511,\n",
+      "        0.0790235 ,  0.18130969, -0.06500382, -0.05761597,  0.15247819,\n",
+      "        0.22402437,  0.33508143, -0.02544755,  0.10404763, -0.0392291 ,\n",
+      "        0.14048643, -0.39408255, -0.04759403, -0.09290893, -0.10062248,\n",
+      "        0.3836949 , -0.04212417,  0.04195033, -0.34143335,  0.02139966,\n",
+      "        0.00748172,  0.09670173,  0.11287135,  0.07313446, -0.06884305,\n",
+      "       -0.27654266, -0.02745902,  0.11782443, -0.05509072, -0.02731109,\n",
+      "        0.02932139,  0.20647307, -0.09912065,  0.08175386,  0.04051739,\n",
+      "       -0.13783188,  0.2178767 ,  0.01360986, -0.11862064,  0.02632025,\n",
+      "        0.01305837, -0.07418288, -0.11537156,  0.07784148, -0.02828423,\n",
+      "        0.0152778 , -0.27535534, -0.26457086, -0.2426946 ,  0.17839569,\n",
+      "        0.41153124, -0.06237097,  0.28373018,  0.09847705, -0.2693095 ,\n",
+      "        0.15109962,  0.02665104,  0.12224031,  0.0053689 ,  0.08057593,\n",
+      "        0.0029663 , -0.01309686,  0.04294159, -0.26014623, -0.09540065,\n",
+      "       -0.19017759, -0.02596658, -0.21918078, -0.04269371,  0.09444954,\n",
+      "       -0.05112423,  0.21732539,  0.2555126 ,  0.06598321, -0.00912136,\n",
+      "        0.01300732, -0.02216252,  0.16752972,  0.00181198,  0.02385568,\n",
+      "       -0.0017939 ], dtype=float32)]\n",
+      "None\n"
      ]
     }
    ],
@@ -9076,7 +533,23 @@
  "metadata": {
   "orig_nbformat": 4,
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.6.3",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.6.3 64-bit"
+  },
+  "interpreter": {
+   "hash": "6f23ddf1f0697a8f0c43dd2435bdb82528077c79e9967f824fba6a3b52b05faf"
   }
  },
  "nbformat": 4,

From 23788eadcb6f748ed83e4910939b2516d8796f92 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 19:28:52 +0800
Subject: [PATCH 095/149] fix tokenize example

---
 docs/source/tutorial/zh/index.rst | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index e4661880..4701837c 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -35,6 +35,8 @@
 
 为使您快速了解此项目的功能，此部分仅展示常用的函数接口使用方法（如得到令牌化序列、试题对应的向量等），对于其中间函数模块（如parse、formula、segment等）以及更细分的接口方法不做展示，如需深入学习，请查看相关部分的文档。
 
+令牌化
+>>>>>>>>>>>>
 
 .. nbgallery::
     :caption: This is a thumbnail gallery:
@@ -44,6 +46,9 @@
     令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>
 
 
+向量化
+>>>>>>>>>>>>
+
 .. nbgallery::
     :caption: This is a thumbnail gallery:
     :name: vectorization_gallery

From b6f491fd72b9020b9409daa00a238995c902a4b4 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 19:51:28 +0800
Subject: [PATCH 096/149] fix tokenize example

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 4701837c..035a3dc2 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -40,7 +40,7 @@
 
 .. nbgallery::
     :caption: This is a thumbnail gallery:
-    :name: tokenizer_gallery
+    :name: tokenize_gallery
     :glob:
     
     令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>

From 10f819687778769565d5abda2dd72a0ab3be98f0 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 19:55:05 +0800
Subject: [PATCH 097/149] try fix

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 035a3dc2..5081a7eb 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -43,7 +43,7 @@
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>
+    令牌化  <../../build/blitz/vectorization/total_vector.ipynb>
 
 
 向量化

From 2ff3185cd1fba37c06d170e78845b6fe32d036c5 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 19:59:41 +0800
Subject: [PATCH 098/149] fix tokenize example

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 5081a7eb..c9d37eef 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -43,7 +43,7 @@
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/vectorization/total_vector.ipynb>
+    令牌化  <../../build/blitz/tokenizer/total_tokenizer.ipynb>
 
 
 向量化

From 025bc527de2623fe000312dd13f62750daf968ac Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 22:21:29 +0800
Subject: [PATCH 099/149] update index

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index c9d37eef..035a3dc2 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -43,7 +43,7 @@
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/tokenizer/total_tokenizer.ipynb>
+    令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>
 
 
 向量化

From 565e718d8f31b8b343284105c329438987b804a0 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 22:55:51 +0800
Subject: [PATCH 100/149] update doc

---
 examples/Tokenizer/total_tokenize.ipynb | 440 +++---------------------
 1 file changed, 46 insertions(+), 394 deletions(-)

diff --git a/examples/Tokenizer/total_tokenize.ipynb b/examples/Tokenizer/total_tokenize.ipynb
index 9be17885..82caa16b 100644
--- a/examples/Tokenizer/total_tokenize.ipynb
+++ b/examples/Tokenizer/total_tokenize.ipynb
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "source": [
     "item = {\r\n",
     "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
@@ -519,13 +519,14 @@
    ],
    "outputs": [
     {
-     "output_type": "display_data",
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
       ]
      },
-     "metadata": {}
+     "metadata": {},
+     "execution_count": 3
     }
    ],
    "metadata": {
@@ -544,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "source": [
     "from PIL import Image\r\n",
     "img = Image.open(\"../../asset/_static/item_figure.png\")\r\n",
@@ -553,14 +554,15 @@
    ],
    "outputs": [
     {
-     "output_type": "display_data",
+     "output_type": "execute_result",
      "data": {
       "image/png": "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",
       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x2798EFFDB00>"
       ]
      },
-     "metadata": {}
+     "metadata": {},
+     "execution_count": 5
     }
    ],
    "metadata": {
@@ -581,11 +583,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "source": [
     "from EduNLP.SIF import sif4sci, is_sif, to_sif"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\python\\python36\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -669,392 +680,24 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 题目切分及令牌化\r\n",
-    "\r\n",
-    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 题目切分"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 基本切分\r\n",
-    "分离文本、公式、图片和特殊符号。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\r\n",
-    "segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 文本部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.text_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
-       " '的斜边',\n",
-       " ', 直角边',\n",
-       " ', ',\n",
-       " '.',\n",
-       " '的三边所围成的区域记为',\n",
-       " ',黑色部分记为',\n",
-       " ', 其余部分记为',\n",
-       " '.在整个图形中随机取一点，此点取自',\n",
-       " '的概率分别记为',\n",
-       " ',则']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 公式部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.formula_segments\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I,II,III',\n",
-       " 'p_1,p_2,p_3']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 图片部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.figure_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[\\FigureID{1}]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.figure_segments[0].figure"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 特殊符号"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.ques_mark_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['\\\\SIFChoice']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 标记化切分  \r\n",
-    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
+    "开始令牌化"
    ],
    "metadata": {}
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    " - symbol:\n",
-    "    - \"t\": text\n",
-    "    - \"f\": formula\n",
-    "    - \"g\": figure\n",
-    "    - \"m\": question mark"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 令牌化\r\n",
-    "\r\n",
-    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
-    "\r\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
-   ],
-   "outputs": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 文本解析结果"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokens.text_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图',\n",
-       " '古希腊',\n",
-       " '数学家',\n",
-       " '希波',\n",
-       " '克拉底',\n",
-       " '研究',\n",
-       " '几何图形',\n",
-       " '此图',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '直径',\n",
-       " '直角三角形',\n",
-       " '斜边',\n",
-       " '直角',\n",
-       " '三边',\n",
-       " '围成',\n",
-       " '区域',\n",
-       " '记',\n",
-       " '黑色',\n",
-       " '记',\n",
-       " '其余部分',\n",
-       " '记',\n",
-       " '图形',\n",
-       " '中',\n",
-       " '随机',\n",
-       " '取',\n",
-       " '一点',\n",
-       " '此点',\n",
-       " '取自',\n",
-       " '概率',\n",
-       " '记']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 公式解析结果"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "source": [
-    "tokens.formula_tokens"
+    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)\r\n",
+    "print(tokens)"
    ],
    "outputs": [
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3']"
-      ]
-     },
-     "metadata": {}
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', 'ABC', '斜边', 'BC', '直角', 'AB', 'AC', '\\\\bigtriangleup', 'ABC', '三边', '围成', '区域', '记', 'I', '黑色', '记', 'II', '其余部分', '记', 'III', '图形', '中', '随机', '取', '一点', '此点', '取自', 'I', ',', 'II', ',', 'III', '概率', '记', 'p', '_', '1', ',', 'p', '_', '2', ',', 'p', '_', '3', '\\\\SIFChoice', \\FigureID{1}]\n"
+     ]
     }
    ],
    "metadata": {
@@ -1064,13 +707,6 @@
     }
    }
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 自定义参数，得到定制化解析结果"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -1612,7 +1248,23 @@
  "metadata": {
   "orig_nbformat": 4,
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.6.3",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.6.3 64-bit"
+  },
+  "interpreter": {
+   "hash": "6f23ddf1f0697a8f0c43dd2435bdb82528077c79e9967f824fba6a3b52b05faf"
   }
  },
  "nbformat": 4,

From b6a91170e08375cf8ae7e514e15b7317021505a2 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 23:01:29 +0800
Subject: [PATCH 101/149] update doc

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 035a3dc2..f4c3fb60 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -43,7 +43,7 @@
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>
+    令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>
 
 
 向量化

From 77152bb212cd012e8cbd5eabc7d29fcfe76fb989 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 23:15:20 +0800
Subject: [PATCH 102/149] update doc

---
 examples/Tokenizer/total_tokenize.ipynb | 440 +++++++++++++++++++++---
 1 file changed, 394 insertions(+), 46 deletions(-)

diff --git a/examples/Tokenizer/total_tokenize.ipynb b/examples/Tokenizer/total_tokenize.ipynb
index 82caa16b..9be17885 100644
--- a/examples/Tokenizer/total_tokenize.ipynb
+++ b/examples/Tokenizer/total_tokenize.ipynb
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "source": [
     "item = {\r\n",
     "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
@@ -519,14 +519,13 @@
    ],
    "outputs": [
     {
-     "output_type": "execute_result",
+     "output_type": "display_data",
      "data": {
       "text/plain": [
        "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
       ]
      },
-     "metadata": {},
-     "execution_count": 3
+     "metadata": {}
     }
    ],
    "metadata": {
@@ -545,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "source": [
     "from PIL import Image\r\n",
     "img = Image.open(\"../../asset/_static/item_figure.png\")\r\n",
@@ -554,15 +553,14 @@
    ],
    "outputs": [
     {
-     "output_type": "execute_result",
+     "output_type": "display_data",
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAACICAYAAADzlJeRAAAe3klEQVR4nO2df1RT9/3/nyE/SWJyBdSUOgs72mKP1XhK24j2CGon4FagbtTMtpZt6rDzR7rjKt+2C+rnHGxdBbr2xB9zgc4j1NMJbpXg2hlsp2DnjrSHVaqcgquWbFqNLUgQkvf3DwgiSSA/7uUm8f04530O3Ny87zu5ed7X+8fr/XoJCCEEFAolrIjhuwEUCsUTKkwKJQyhwqRQwhAR3w2IBJqamuBwOHy+rtFokJKSMo4tokQ7VJgAGhoa0NzcDLvdjn//+99oa2uDw+FAa2srAIBhmFHf73K58O233wIAYmNj8cADDwAAZs+ejblz50Kr1UKn00Emk3H6OSjRg+Bum5Vtbm5Gc3MzrFYrPvnkE7S2toJhGPT09KC3t5f160mlUsTGxqKrqwsMw2D69OlYunQpdDodMjMzWb8eJTqIemHa7XbU19fjT3/6E/72t79BoVDA6XSiq6uL13YxDIOuri48/vjjyM/PR2ZmJpKSknhtEyV8iEph2mw21NbW4tChQzh16hSkUulQVzMckcvliImJgUwmQ3Z2NgwGA7RaLd/NovBIVAmzuroar732Gs6fPw8AuHnzJs8tCg61Wg2FQoFXX30VK1asGHOMS4k+Il6YNpsNf/jDH7Bz504ACGvLGChyuRxOpxOLFy/G+vXr6Zj0LiJihdnU1IQ33ngDdXV1cLlcoy5nRAMMw0AkEmHnzp14/vnn+W4OhWMiTpjNzc0oLCxEa2sr7HY7380Zd5RKJWQyGRVolBMxwrTZbFi/fj0++OAD3Lhxg+/m8A4VaHQT9sK02+347W9/i8rKyqgaP7KFUqmESqXCn//8Z+h0Or6bQ2GJsPaV3bdvH5KTk7Fv3z4qSh90dXXh66+/RlZWFjZs2HBXdu+jEhKGdHZ2Ep1OR9RqNQFAi59FJpORSZMmEYvFwuLdqCNlZRdYrI/iD2FnMfft24dZs2ahqamJjiUDxOFw4MqVK9Dr9XjqqadYsJ5tKJ+fjUNsNI4SGHw/Gdxcv36d5OXlUSvJUhGLxWTSpEmksbEx6HtyoSyNACBr6li80RS/CAuL2dDQgPvvvx/vv/8+tZIs0dfXhytXruBHP/oRSkpKAq+grRw78QrK0thvG2VseJ+VLS0txbZt2+ikBYeoVCrMmzcP1dXVfrr3WbB2/nlsPnk/dgr+Dw9eOImN07lrX0NDA4Db+17/+c9/4uuvv/Z67uzZs5GcnAwASEpKuqNEFXya65/97GeEYRjeu313QxGLxSQ5OZmcO3duzPtSt2YNqSOEkAtlJA2Df7NAT08PsVqtxGg0Ep1OR2JjYwkAwjAMYRiGiESigD6TUqkkDMMQpVJJABCtVkuys7OJ0WgMqQsfDvAizOvXr5NHH3106MbQMn4lLi6OHDlyxOe9cY8rb5fQhHnu3Dny8ssvk+TkZCISicbtQcwwDImNjSUZGRnEZDKR9vb2ED7F+DPuwmxvbycpKSlEIpHw/iO9W8uUKVPIO++843lzLpSRNcOXRurWEKSVkUAXSxobG0lhYSFJSEggKpUqYEvIdpHL5USpVJLExERiMpnI9evXQ/kJjwvjKsyzZ8+SxMRE3n+YtIBMnDiRvP3227dvzoUykjZChBfK0vwWZk9PDzGbzSQxMTGshydyuZwoFAqSnZ3N8novu4ybMM+ePUs0Gg3vN4aW2yUuLm5InAPH0siAwbxAytKGn+u7O9vZ2Uk2bdpEJkyYMDTWi5TCMAxJSEggZrN5nFTgP+MizPb2dmopw7TExcWRjRs3BnxPOzs7ycqVK4lcLue9qxpqUSqVYSdQzoXZ3t5OkpKSeP/yafFdGIbxW5w9PT3EaDSS+Ph4IhaLeW87m8Ut0JqaGk414Q+cCpOKMnIKwzBk165do97PqqoqotFoiEKh4L29XBa1Wk10Oh2vM7mcCfP69eskJSWF9y+ZFv/LlClTvC6luDcVqFQq3ts4noVhGGI0GklPTw9XMvEJJ54/DocDjzzyCFpaWtiuOuqRy+WQSCQecW6Tk5OhVqvvODcnJwcA8NFHH+G///0vrl27ht7eXvT19QUdnnPKlCloaGgYiixfX1+PZ555Bt98802QnyiyUSgUiI+Px5EjR8Y1ciEnwnzqqafw/vvvo6+vj+2qowaGYYYiuLtFl5OTM5RuQavVshodr6GhAXa7Hc3Nzaivr4fNZsNXX30FAIiJiUF/f//QuSqVCrt27cJf/vIXfPTRR9RdEkB8fDxKSkqwevXqcbke68Kkvq+eiEQiKJVKdHV1ITU1FUuXLkV6enpY5Dxxi7WjowN//etfceLECXz33XdwuVx3iJUyEFZ00aJFOHjwIPfpLtjsFzc2NpKEhATexwZ8F7frmUgkIjqdjpSUlESU7+bhw4eJRqMharWayOVyIpVKSUxMDO/fazgUsVhMZsyY4ZfPcSiwJszOzk4ybdo03r84vopMJiNKpZJotdqIE+JwTCYTSU9PJ+3t7aSqqoqsWLGCnDt3jphMJpKZmUkkEknULZMEU6ZMmULOnj3L2X1gRZg9PT0kNTWV9y+Lj6JWq0liYiLZvn17xDlKD6enp4esWLGC/PKXvyQ9PT3k448/Jh9//DHZtGkTKS0tveM8s9k8tDtEKBTyfg/4KhqNhrMHMCvCNBqNUb+2NVKMKpWKPPfcc5w+NceLc+fOEa1WS6qqqoYE6S49PT0kMzOTWK1Wj/e1t7eT7du3k8TERCKXy3m/L3yUhISEUXfrBEvIwmxvbw9rp2U2i1KpJNnZ2WHhGcIWVVVVRKfTkXPnznmI0l06OzuJVqslnZ2dPus5e/Ysee6554hSqSRSqZT3ezWexdf6byiEPCs7b948NDU1hVJFWCMSiSCXy5Gbm4utW7dGzU55h8OBoqIi2Gw2mEymMdecRSIRioqKYLFYRp2RdDgceOutt1BSUoLu7m5Oco6GI1OmTMHevXvx5JNPslJfSMLct28fNm/eHJVxemQyGWQyGX7yk59g27Zt0Gg0fDeJNWw2G/R6PXJycpCamur3+86cOYOLFy+itLR0zHMdDgcqKirw6quvoru7Gz09PaE0OSKIj4/Hhx9+yIojQtDCtNlsmDt3Lmw2W8iNCCcUCgViYmKwbt06bNmyJepS4NXX18NgMMBsNge1Trl//34sXLgwoLQMFRUV2Lx5M3p6etDd3R3wNSOJpKQkWK3WkHtWQUfJW7duXVS5aYnFYjAMg+LiYvzvf//Djh07ok6UxcXFKC8vR2NjY9DOAyaTCZWVlQENX55//nlcuXIFBw4cQHJyMmJjY4O6diTQ0dGBH/zgByFnnwvKYjY3NyM9PT1qurAMw2DZsmV46623ok6MwIB3T15eHhYuXIglS5aEXN/UqVORl5cHq9Ua1Pf1u9/9Djt27IDdbofT6Qy5PeGGRCKBVqvF6dOng64jKGEuWrQIVqs16IuGCxMmTMC9994Ls9kctQl5mpqaUFBQAJPJBJFIxFq9/f392Lp1a9C/A5vNhnXr1uH48eNR84AfjkqlwpYtW1BUVBTU+wMWZjRYS7FYDIVCgW3btmH9+vV8N4czysrKcOTIEVRVVaGtrY31+v/xj3/gxo0bwQWUHqShoQErV67E9evXo26CKJTJoIDHmC+++GJEi5JhGOTn56O9vT1qRWm326HX63Hx4kVYLBZORAkACxYsQGtrK2pra4OuIz09HZcvX8ZvfvMbxMfHs9e4MOCbb75BTk5OUOPNgCxmJFtLsViMyZMn47333ovabisAtLa2oqCgABs3bsTUqVM5v96sWbOQlZUFs9kc8k4Zm82GvLw8fPbZZ7h58yZLLeQXsViM/Px8HDhwIKD3BWQxI9VaMgyDRYsWoaWlJapFWVFRAb1eD7PZPC6iBICWlhaYzWbo9fqQZyI1Gg0aGxuxefPmqJmE6+vrw9GjR1FfXx/Q+/y2mE1NTcjKyoq4fZZxcXF45ZVXYDAY+G4KZzgcDhgMBjgcDphMJpw5c2bc23D16lVUVlaipqaGlfqamprw5JNP4sqVK6zUxzfJycn48ssv/T7fb4tpMpkiSpRisRjTpk3DyZMno1qUHR0dyMjIwJw5c/Dzn/+cF1ECQEJCAlJSUrBjxw5W6tPpdDh//jx0Oh3kcjkrdfLJ1atX8fvf/97v8/0SpsPhwKFDkZO+dOLEifjhD3+IL774gvcIAVxSW1uLvLw8mEwmzJo1i+/mYNmyZTh27NhQ9q5QYRgmarq23333HYqLi/3u7vslzOrqalbXwLgkISEB5eXlOHz4MPfhH3ikqKgIlZWVsFqtQQfe4oKamhoYDAZ0dHSwVmdxcTEsFgsSExNZq5MPent7/e5R+DXGnDlzJlpbW0NuGNdoNBpUVVUhPT2d76ZwhtsBfenSpViwYAHfzfGKvztRAqWjowNZWVkR8Vv0hUqlwhdffDHmpogxLWZra6vPJKLhRGJiIiwWS1SL0j0BZzQaw1aUwIBX0KpVq1BYWMhqvUlJSWhsbERqamrE9OBG4nQ6/Vo6GVOYe/bsCfs1pXvvvRcnT54c17if482OHTtgMBhgsVgi4kc5ffp0yGQy7N69m9V6GYbBxx9/jLS0tIh0hu/u7kZJScmYY80xhXngwIGwDWMokUiQkpKClpaWqNnAPBK3A/qNGzewc+dOzrx4uKC0tBTvvvsu6xvpZTIZTpw4gSeeeMIjCHYk4HQ6x1zXHHWM2dHRgYceeiisJhfcyOVyzJkzB3V1dRE/Y+eL5uZmFBQUwGg0IiEhge/mBMXUqVOh1+tRU1PDyWbzTZs2oaKiIuIcX8Za1xzVYtbX18PlcrHeqFCRSCSYM2cOjh8/HrWi3L17NwoLC1FTUxOxogSAS5cuoaSkBAUFBSF7BnmjrKwMRqMREydOZL1uLrl27Rqam5t9vj6qMA8dOhSW48v7778fx48fj8rlEIfDgYKCAnz66aewWq24dOkS300KGZFIhKVLlwa9BWosDAYDnnvuuYjq1nZ3d6OystLn6z67sg6HAxMnTuTkKRcKKSkpaGxsjEpL6XZAX7t2LaZPn853c1jn7bffRk5ODlasWMFJ/Xq9HrW1tWH3m/VFQkKCT5dDnxazqakp7CzStGnTgt41H+7U1tYObWiORlECgNlsxmuvvcbZOmRVVRUeffTRiJi1BgaWlXxNjPkU5ocffhhWkz6JiYk4ceJEVEWrc2MwGFBZWQmLxRJW3znbnDlzBjU1NdDr9Zz5XR87dgyPPPIIxGIxJ/WzSVdXF959912vr/nsyoZT+BCNRgOLxRJ165Tu/YdPP/10QGEkIx22d6KMxG63Y968eRHhIeSrO+vTYv7rX//itEH+kpCQgJqamqgTZUNDAzIyMlBaWnpXiRIYuKdz5sxBcXExJ/UzDAOr1Ypp06ZxUj+bOBwOr37FXoVpt9vDIoK2SqXCiy++GHWbm4uLi7F169aQwkhGOkuWLMHp06cD3kDsLxqNBseOHUNcXBwn9bOJt904XoXZ3NzMu7uTWCyGVqvlbIqdD+x2O7KysgAA27dvHzMtQbRTVVWFoqIiVneiDMe9P1SlUnFSPxt0dXXhk08+8TjuU5h8RyybPHkyjhw5wmsb2KSpqQkZGRnYuHEjK7Fdo4GWlhZUVVUhLy+PsyWO1atXY/HixWE9U+ut1+BVmGfPnuW1K5uQkID33nsvapZFdu/ejaKiItTU1ECpVPLdnLDi6tWreOmll1BQUMDZNQ4ePBjWezm/+uorjweTV2F+9tln49Igb0TTuNLhcECv1+PTTz+FxWKJCi8eLpg6dSo0Gg3Kyso4qV8mk4X1eFOpVHq453kVJl8/ILFYjMWLF0fFuLK1tRXz5s1DTk4OVq5cyVssnkihpKSE1bAkIwnn8abL5fIYZ/ucleWD+Ph4HDx4kJdrs0l1dTX0ej2qqqrGLYxkpHPmzBmYzWYYDAbOMsi5x5tCoZCT+oOlt7fX4zPfIUzi6se3Xd9BoIoF1ApgHD3yGIaB2WwOOzfAQHA4HCgsLMSRI0fQ2NiIq1ev8t2kiKKtrQ0mk4mVGLW++OMf/xh2Xdre3l6cP3/+jmN3CFMQI8K1K/+DTCwGbnYD4+QLLBaL8dhjjyEzM3N8LsgB7ng0c+bMwQsvvEC7rkHS39+PnJwczoYzDMPgjTfeCLsu7UgvJY+ubMfFi4iJCTptZlCoVCpUVFSM6zXZpL6+Hnl5eSgpKQmLMJKRTmpqKux2O2e/iWeffRbf//73Oak7WDo7O+/432Nxx27/dvAvMaCWDPzpugV81wfIZIBUCPQOWlOxGJAPntM7zMIOPw4ncGOY6R3x2gSXGEaj0dM5nThxy+kCAQBBDCRCIQQjj0MAsUiEGBD09/fDCcHA+MHZDycAxIggFbhunx8jgjRGENAXNhbFxcU4ffo0rFbrXe8wwCYmkwlZWVlISUnhZIbebDZj8eLFuHbtGut1B8PIYY+HabTbrw9GLegDbt4C4BwQJQA4nED/rUEBigfGoDe6gRu3AKkMEA8elwsHusI3uoF+IaBwe/p7vjbpnslesm4R9DsJYoRiSEUiCOGCk4w8LoY4hqDPRQAIIBLGQAACp9OJGJEYUmEMBK5+9LoA8dD/TrAVj8FutyMjIwMA8PLLL1NRsox7MqiwsJCTyUitVouVK1eG7S6U0fusfX1AL24LSyYEnH23/46RDEwSqSUAhIBw8LjLCQyehlu3AJHw9nuGvaaWSlFd9a6nWIgTzhghRAJgQHTigb+JE06BAMJBoxcjGCk2AYRC0bAPJYAwZtDSDuIKOE2vJ01NTZg3bx6MRiP14uGQS5cuobS0FHl5eZzU//rrr2PChAmc1B0qvoXpFpbDOSAsMQCh884Jof5bgxZzsLhfixllOnrwNZFIhPzlyzFn9hzv5xF3d3XkceL9+DhRVlYGg8EAq9Ua1m5e0QKXYUlkMhn2798flt5YQ8J0ufrQ6yIDyxXEOeyUQaspV9y2lsCgYCXDllQGu7YOJwDh7eNCIdDvvP2ewddUKhXeLC8FiRF6eToIICDu7isAkAFLJxBCCIL+IbNHfLyffdxhJC9evBhxYSQjHTYS5PoiNzc3LCfshn7TAggAVz/iJ09GTP+ICRKHE8AIa4k+oNcJSBWD3Vnh4OuDY1P3cbET6O67/Z6bt6BQxWHz/9sCgUQGydBkDEF/fx/6BwUoEQrgdPaht78Pvf1OYKhbGwO4+geOuwSD7yfod7pAQOB09sM15v+B0dzcjKysLKxatQrLly8P8N0UNuAyLInJZAo7q+kRwaChoQF5eXkjBtxiQNbH2rpmXFwcLl++HBHOBBUVFdizZw/MZjN1GOCZhIQE6PV6NDY2sv7b4Ttix8hIBv71AmVCwDn2af6gUChQVFQU9qJ0e/GcOHECVquVijIMuHr1KoxGI/R6Pet179q1i1erOdJ100OYdwhGMdgdheP2ZFCISKVS/OpXv2KnMo4Il2SwFE/YTpDrRqvVYtKkSazWGQgjDZWHMDUaze1wF90jZltDJBKsZbglg6V4wnaCXDe7du3iLXv1mBYzKSmJsxCK4W4twzUZLMUTLhLk5ubmYsqUKazVFwgjrbXXMSYXDr7hbC1tNhvmzZsHtVqNX//619SLJwJoaWmByWRiPScKH1ZTLpdj9uzZdxzzKsz4+HjWLy4UCvHMM8+wXm+oNDQ0ICsrC6WlpWGdDJbiCRcJcnNzczF58mTW6vMHiUTiEZ7VqzC5MOcPP/xw2EVR37FjB7Zu3QqLxXLXhpGMdLhIkPvSSy+Nq1dXT08PUlJS7jjmVZhs77pXq9XYsGEDq3WGwvBksNu3b6dePBEO2wlyc3NzIZFIxj6RJaRSqUfgOa/CfOCBB1h9YgiFwrDZBN3c3IyMjAysWrUKy5Yt47s5FBZgOyyJRqPBY489xkLL/OPBBx/0OOZVmAsWLGBtsVUkEmH58uVhMekTLclgKZ6wnSB3w4YNkEqlLLRsdEQiERYvXuxx3KswdTodawGfFQoF1q1bx0pdwRKNyWApnrC5EyUzM3NcjIlSqfS6ddCrMBmGYW1m9p577uE1IZA7jOTChQtpGMm7gNTUVNhsNlRXV4dUj0wmQ05ODkut8o3D4fAaocGnrywbYpLL5di4cWPI9QRLdXU1CgoKUFVVFbXJYCmesLUTpbCwkHOrqdVqvV7DpzCfeOKJkPvYTqeTs7Teo+FwOGAwGHDkyBFYLBbqgH6XwVaCXJ1Ox2mEA6lUiqefftrraz6FqdPpQs74NXPmzHHPP2Kz2ZCVlYX77rsPL7zwAvXiuUu5dOkSjEZjyDlR1qxZw1KLPJFKpT5XK0btyjqdwe/1EolE474c4U4GW1JSctclg6V4wkaC3CVLlnAWsCs+Pt7DscCNT2HKZDI8/vjjQV/U12wTV9BksBRvhJogV6fTcZJSYazVilE3Sj/77LNBO7Q7nc5xydhFk8FSxiKUBLkymQxz585lv1EAfvGLX/h8bVRhZmZmBt2dTU1N5XxGyx1GkiaDpYxGqAly8/PzIRCwGyh84cKFo86/jCpMhmHw0EMPBXxRqVSKJ598MuD3BUJZWRmKiopgtVrDLpASJfwIJUFueno6q+NMhmG8BDm/kzFj/qxatSrg/WmjzTaFit1uh16vx8WLF2GxWKgDOsVvgk2Qy7aDzMSJE8fUx5jCzM3NDfjCEonE52xTKLS2tiIrKws5OTlYvnw59eKhBEywCXLZGmeq1Wrs2rVrzPPGFGYwnvaPPvpoQOf7gzsZrNlspslgKUET7E6UH//4x6xcPy4uzi9j51f4yg0bNkCtVvt1YZFIFNIyy0hoMlgK2wSTIHfJkiUhp6f011oCXgI+++Lee+/F119/7dfFKyoqguoCj6SjowMFBQV4+umnacQ6CuucOXMGFy9eRGlp6ZjnOhwOyOVy+CkXryQnJ+PLL7/061y/HwEGgwEKhWLM8wghrIwvaTJYCtcEkiBXJpOFtPynVqtx+PBhv8/3W5ijLYYO5+bNmyELs6ioCOXl5bBardSLh8IpJpMJlZWVfoUlCTZmlUgkQn5+fkCzu34Lk2EY/PSnPx0z5Mj3vvc9vy8+EpvNhoyMDKjVapoMljIuBJIgN9iZWZVKhTfffDOg9wQ0ml23bt2Y3dmZM2cG1AA3TU1NyMrKgtFopGEkKeOKvwlytVptwB5AarUa+/fvD7gbHJAwtVot8vPzfVrNYGdk3clgLRYLTQZL4QV/wpI88sgjAdUpFouxaNGioCZCA57/3bZtm09PIIVCEdD4kiaDpYQTYyXITUlJCchi3nPPPTh48GBQbQlYmBqNBqtWrfJqmgOZkaXJYCnhyGhhSZKSkvxeLomLi8OxY8eCnskNasX09ddf92o1XS6XXw2pqKhAYWEhzGYzDSNJCStaWlpgNpt9Oh/4EwharVZjx44dIa1OBCVMmUyGoqIij4kgh8Mx6pSyO4wkTQZLCWdGS5A7Vhws97hy9erVIbXBb8+fkTgcDsyePRsXLly447iv6lpbW1FQUIC1a9fSiHWUiODo0aNQq9XYsmXL0LH4+Hhcu3bN6/kSiQQPP/wwjh8/HvJe5KCd/2QyGd555507Nnv6inZQW1uLgoICmEwmKkpKxOAtQe5oET1mz57NiiiBEIQJDMRDefbZZ4ca4q3/bTAYUFlZCYvFQpPBUiKOkQlyfW2YTklJwQcffMBa1I7Q3OUxMBHk9vYZvh3LnQz2vvvuo8lgKRHLyAS53nZZpaSkoLGxkdVQrSELUyaT4dChQ3eY+OHJYGkYSUqkMzxB7vAJT3dAALZFCbAgTGDAIygtLQ0zZsygyWApUYk7Qa5UKoVQKERsbCy0Wi0nogQCEKZlrQACwYgyvxxuX50lS5bAarWitbUVf//736kXDyXqKC0txX/+8x/I5XLk5OTg9OnTnGUaCGi5pK18PmZ8/grIniwAFqwVZGPvmjqQPVk4cOAAtmzZgsuXL7PeSK1Wy8oXkJ6ePvR3RUVFUHFGKRSRSIS+vj5OrxGAMNtQPn8GPn+FYM9AfGVY1gqQjbpBoVIo0U9HRwc6OjrueMgDACxrIcjeO/TvmjqCPViLtdiDYOTh/xiz7SgOnVqD3MGLtJXPR/beNJRtpqKk3D0kJSWNEGUbyucLIMgG6ggBGSy5tQIIslvw4IzgruO3MNuOHsIp7EX24PhyFSpByElspP4ClLuWgV7kJpThAtmD4SYqa08d1mAW7g9SH34Ksw1HD53CmrrBJ8KFMmDTDMwvpxM8lLuXtvJV2HQqDWWVG+Gpvxl4sCwXwfYn/RtjtpVj/ozP8cqwp4JlrQDZe9egbsSTgkK5Oxi0lrO4mWPxy2K2HT2EU2uGq78N51sApD2IILvQFEqEcwGfnwLSgh1EjoEfcTwGu7Gv3JalZe0MbDoFrKnzZsIplLuAtvPg0sl0TIspEAyIcG/2bceC7L1pKLtAgpoGplCigun3YxaAU59f8Pqypfy2800wBL0fk0K56xlcu0wru4CTQ8sTnuv9wUCFSaGEQls55s/YhFNDB9JQdiH0ZUQqTAolDGFldwmFQmEXKkwKJQz5/6GFNae7ZI7lAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x2798EFFDB00>"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
       ]
      },
-     "metadata": {},
-     "execution_count": 5
+     "metadata": {}
     }
    ],
    "metadata": {
@@ -583,20 +581,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "source": [
     "from EduNLP.SIF import sif4sci, is_sif, to_sif"
    ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "D:\\python\\python36\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -680,24 +669,392 @@
   {
    "cell_type": "markdown",
    "source": [
-    "开始令牌化"
+    "### 题目切分及令牌化\r\n",
+    "\r\n",
+    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 题目切分"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 基本切分\r\n",
+    "分离文本、公式、图片和特殊符号。"
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "source": [
-    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)\r\n",
-    "print(tokens)"
+    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\r\n",
+    "segments"
    ],
    "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', 'ABC', '斜边', 'BC', '直角', 'AB', 'AC', '\\\\bigtriangleup', 'ABC', '三边', '围成', '区域', '记', 'I', '黑色', '记', 'II', '其余部分', '记', 'III', '图形', '中', '随机', '取', '一点', '此点', '取自', 'I', ',', 'II', ',', 'III', '概率', '记', 'p', '_', '1', ',', 'p', '_', '2', ',', 'p', '_', '3', '\\\\SIFChoice', \\FigureID{1}]\n"
-     ]
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.text_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
+       " '的斜边',\n",
+       " ', 直角边',\n",
+       " ', ',\n",
+       " '.',\n",
+       " '的三边所围成的区域记为',\n",
+       " ',黑色部分记为',\n",
+       " ', 其余部分记为',\n",
+       " '.在整个图形中随机取一点，此点取自',\n",
+       " '的概率分别记为',\n",
+       " ',则']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 公式部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.formula_segments\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I,II,III',\n",
+       " 'p_1,p_2,p_3']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 图片部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.figure_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[\\FigureID{1}]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.figure_segments[0].figure"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 特殊符号"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.ques_mark_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['\\\\SIFChoice']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 标记化切分  \r\n",
+    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    " - symbol:\n",
+    "    - \"t\": text\n",
+    "    - \"f\": formula\n",
+    "    - \"g\": figure\n",
+    "    - \"m\": question mark"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 令牌化\r\n",
+    "\r\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
+    "\r\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens.text_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " '斜边',\n",
+       " '直角',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " '概率',\n",
+       " '记']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 公式解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens.formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {}
     }
    ],
    "metadata": {
@@ -707,6 +1064,13 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 自定义参数，得到定制化解析结果"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "markdown",
    "source": [
@@ -1248,23 +1612,7 @@
  "metadata": {
   "orig_nbformat": 4,
   "language_info": {
-   "name": "python",
-   "version": "3.6.3",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.6.3 64-bit"
-  },
-  "interpreter": {
-   "hash": "6f23ddf1f0697a8f0c43dd2435bdb82528077c79e9967f824fba6a3b52b05faf"
+   "name": "python"
   }
  },
  "nbformat": 4,

From 43f369b501c42e7e7e47a3394c7b95bb1d03b991 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 23:15:50 +0800
Subject: [PATCH 103/149] update doc

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index f4c3fb60..035a3dc2 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -43,7 +43,7 @@
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>
+    令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>
 
 
 向量化

From 4e7cbc7c55c4ec684ffdf00e2acb3cd7d4bea2c2 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 23:21:06 +0800
Subject: [PATCH 104/149] update doc

---
 examples/Tokenizer/total_tokenize.ipynb | 240 ------------------------
 1 file changed, 240 deletions(-)

diff --git a/examples/Tokenizer/total_tokenize.ipynb b/examples/Tokenizer/total_tokenize.ipynb
index 9be17885..8a6df6ea 100644
--- a/examples/Tokenizer/total_tokenize.ipynb
+++ b/examples/Tokenizer/total_tokenize.ipynb
@@ -666,246 +666,6 @@
    ],
    "metadata": {}
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 题目切分及令牌化\r\n",
-    "\r\n",
-    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 题目切分"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 基本切分\r\n",
-    "分离文本、公式、图片和特殊符号。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\r\n",
-    "segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 文本部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.text_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
-       " '的斜边',\n",
-       " ', 直角边',\n",
-       " ', ',\n",
-       " '.',\n",
-       " '的三边所围成的区域记为',\n",
-       " ',黑色部分记为',\n",
-       " ', 其余部分记为',\n",
-       " '.在整个图形中随机取一点，此点取自',\n",
-       " '的概率分别记为',\n",
-       " ',则']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 公式部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.formula_segments\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I,II,III',\n",
-       " 'p_1,p_2,p_3']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 图片部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.figure_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[\\FigureID{1}]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.figure_segments[0].figure"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 特殊符号"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.ques_mark_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['\\\\SIFChoice']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 标记化切分  \r\n",
-    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    " - symbol:\n",
-    "    - \"t\": text\n",
-    "    - \"f\": formula\n",
-    "    - \"g\": figure\n",
-    "    - \"m\": question mark"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
   {
    "cell_type": "markdown",
    "source": [

From 7bb13cb6d1e48f8af1a91f9bb70e37df39133977 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 23:26:56 +0800
Subject: [PATCH 105/149] update doc

---
 examples/Tokenizer/total_tokenize.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/Tokenizer/total_tokenize.ipynb b/examples/Tokenizer/total_tokenize.ipynb
index 8a6df6ea..2cebfd1d 100644
--- a/examples/Tokenizer/total_tokenize.ipynb
+++ b/examples/Tokenizer/total_tokenize.ipynb
@@ -669,7 +669,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "#### 令牌化\r\n",
+    "### 令牌化\r\n",
     "\r\n",
     "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
     "\r\n",

From 171b3f8c1083fbea4fd2b1a984d50fe6f0662f8d Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 23:35:08 +0800
Subject: [PATCH 106/149] update doc

---
 examples/Tokenizer/total_tokenize.ipynb | 731 ------------------------
 1 file changed, 731 deletions(-)

diff --git a/examples/Tokenizer/total_tokenize.ipynb b/examples/Tokenizer/total_tokenize.ipynb
index 2cebfd1d..23eee1de 100644
--- a/examples/Tokenizer/total_tokenize.ipynb
+++ b/examples/Tokenizer/total_tokenize.ipynb
@@ -475,737 +475,6 @@
    ],
    "metadata": {}
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 调用sif4sci函数来进行令牌化\r\n",
-    "\r\n",
-    "### 概述\r\n",
-    "\r\n",
-    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
-   ],
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
-    "\n",
-    "![Figure](../../asset/_static/item.png)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "item = {\r\n",
-    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
-    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\r\n",
-    "}\r\n",
-    "item[\"stem\"]"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 加载图片：`$\\\\FigureID{1}$`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "from PIL import Image\r\n",
-    "img = Image.open(\"../../asset/_static/item_figure.png\")\r\n",
-    "figures = {\"1\": img}\r\n",
-    "img"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAACICAYAAADzlJeRAAAe3klEQVR4nO2df1RT9/3/nyE/SWJyBdSUOgs72mKP1XhK24j2CGon4FagbtTMtpZt6rDzR7rjKt+2C+rnHGxdBbr2xB9zgc4j1NMJbpXg2hlsp2DnjrSHVaqcgquWbFqNLUgQkvf3DwgiSSA/7uUm8f04530O3Ny87zu5ed7X+8fr/XoJCCEEFAolrIjhuwEUCsUTKkwKJQyhwqRQwhAR3w2IBJqamuBwOHy+rtFokJKSMo4tokQ7VJgAGhoa0NzcDLvdjn//+99oa2uDw+FAa2srAIBhmFHf73K58O233wIAYmNj8cADDwAAZs+ejblz50Kr1UKn00Emk3H6OSjRg+Bum5Vtbm5Gc3MzrFYrPvnkE7S2toJhGPT09KC3t5f160mlUsTGxqKrqwsMw2D69OlYunQpdDodMjMzWb8eJTqIemHa7XbU19fjT3/6E/72t79BoVDA6XSiq6uL13YxDIOuri48/vjjyM/PR2ZmJpKSknhtEyV8iEph2mw21NbW4tChQzh16hSkUulQVzMckcvliImJgUwmQ3Z2NgwGA7RaLd/NovBIVAmzuroar732Gs6fPw8AuHnzJs8tCg61Wg2FQoFXX30VK1asGHOMS4k+Il6YNpsNf/jDH7Bz504ACGvLGChyuRxOpxOLFy/G+vXr6Zj0LiJihdnU1IQ33ngDdXV1cLlcoy5nRAMMw0AkEmHnzp14/vnn+W4OhWMiTpjNzc0oLCxEa2sr7HY7380Zd5RKJWQyGRVolBMxwrTZbFi/fj0++OAD3Lhxg+/m8A4VaHQT9sK02+347W9/i8rKyqgaP7KFUqmESqXCn//8Z+h0Or6bQ2GJsPaV3bdvH5KTk7Fv3z4qSh90dXXh66+/RlZWFjZs2HBXdu+jEhKGdHZ2Ep1OR9RqNQFAi59FJpORSZMmEYvFwuLdqCNlZRdYrI/iD2FnMfft24dZs2ahqamJjiUDxOFw4MqVK9Dr9XjqqadYsJ5tKJ+fjUNsNI4SGHw/Gdxcv36d5OXlUSvJUhGLxWTSpEmksbEx6HtyoSyNACBr6li80RS/CAuL2dDQgPvvvx/vv/8+tZIs0dfXhytXruBHP/oRSkpKAq+grRw78QrK0thvG2VseJ+VLS0txbZt2+ikBYeoVCrMmzcP1dXVfrr3WbB2/nlsPnk/dgr+Dw9eOImN07lrX0NDA4Db+17/+c9/4uuvv/Z67uzZs5GcnAwASEpKuqNEFXya65/97GeEYRjeu313QxGLxSQ5OZmcO3duzPtSt2YNqSOEkAtlJA2Df7NAT08PsVqtxGg0Ep1OR2JjYwkAwjAMYRiGiESigD6TUqkkDMMQpVJJABCtVkuys7OJ0WgMqQsfDvAizOvXr5NHH3106MbQMn4lLi6OHDlyxOe9cY8rb5fQhHnu3Dny8ssvk+TkZCISicbtQcwwDImNjSUZGRnEZDKR9vb2ED7F+DPuwmxvbycpKSlEIpHw/iO9W8uUKVPIO++843lzLpSRNcOXRurWEKSVkUAXSxobG0lhYSFJSEggKpUqYEvIdpHL5USpVJLExERiMpnI9evXQ/kJjwvjKsyzZ8+SxMRE3n+YtIBMnDiRvP3227dvzoUykjZChBfK0vwWZk9PDzGbzSQxMTGshydyuZwoFAqSnZ3N8novu4ybMM+ePUs0Gg3vN4aW2yUuLm5InAPH0siAwbxAytKGn+u7O9vZ2Uk2bdpEJkyYMDTWi5TCMAxJSEggZrN5nFTgP+MizPb2dmopw7TExcWRjRs3BnxPOzs7ycqVK4lcLue9qxpqUSqVYSdQzoXZ3t5OkpKSeP/yafFdGIbxW5w9PT3EaDSS+Ph4IhaLeW87m8Ut0JqaGk414Q+cCpOKMnIKwzBk165do97PqqoqotFoiEKh4L29XBa1Wk10Oh2vM7mcCfP69eskJSWF9y+ZFv/LlClTvC6luDcVqFQq3ts4noVhGGI0GklPTw9XMvEJJ54/DocDjzzyCFpaWtiuOuqRy+WQSCQecW6Tk5OhVqvvODcnJwcA8NFHH+G///0vrl27ht7eXvT19QUdnnPKlCloaGgYiixfX1+PZ555Bt98802QnyiyUSgUiI+Px5EjR8Y1ciEnwnzqqafw/vvvo6+vj+2qowaGYYYiuLtFl5OTM5RuQavVshodr6GhAXa7Hc3Nzaivr4fNZsNXX30FAIiJiUF/f//QuSqVCrt27cJf/vIXfPTRR9RdEkB8fDxKSkqwevXqcbke68Kkvq+eiEQiKJVKdHV1ITU1FUuXLkV6enpY5Dxxi7WjowN//etfceLECXz33XdwuVx3iJUyEFZ00aJFOHjwIPfpLtjsFzc2NpKEhATexwZ8F7frmUgkIjqdjpSUlESU7+bhw4eJRqMharWayOVyIpVKSUxMDO/fazgUsVhMZsyY4ZfPcSiwJszOzk4ybdo03r84vopMJiNKpZJotdqIE+JwTCYTSU9PJ+3t7aSqqoqsWLGCnDt3jphMJpKZmUkkEknULZMEU6ZMmULOnj3L2X1gRZg9PT0kNTWV9y+Lj6JWq0liYiLZvn17xDlKD6enp4esWLGC/PKXvyQ9PT3k448/Jh9//DHZtGkTKS0tveM8s9k8tDtEKBTyfg/4KhqNhrMHMCvCNBqNUb+2NVKMKpWKPPfcc5w+NceLc+fOEa1WS6qqqoYE6S49PT0kMzOTWK1Wj/e1t7eT7du3k8TERCKXy3m/L3yUhISEUXfrBEvIwmxvbw9rp2U2i1KpJNnZ2WHhGcIWVVVVRKfTkXPnznmI0l06OzuJVqslnZ2dPus5e/Ysee6554hSqSRSqZT3ezWexdf6byiEPCs7b948NDU1hVJFWCMSiSCXy5Gbm4utW7dGzU55h8OBoqIi2Gw2mEymMdecRSIRioqKYLFYRp2RdDgceOutt1BSUoLu7m5Oco6GI1OmTMHevXvx5JNPslJfSMLct28fNm/eHJVxemQyGWQyGX7yk59g27Zt0Gg0fDeJNWw2G/R6PXJycpCamur3+86cOYOLFy+itLR0zHMdDgcqKirw6quvoru7Gz09PaE0OSKIj4/Hhx9+yIojQtDCtNlsmDt3Lmw2W8iNCCcUCgViYmKwbt06bNmyJepS4NXX18NgMMBsNge1Trl//34sXLgwoLQMFRUV2Lx5M3p6etDd3R3wNSOJpKQkWK3WkHtWQUfJW7duXVS5aYnFYjAMg+LiYvzvf//Djh07ok6UxcXFKC8vR2NjY9DOAyaTCZWVlQENX55//nlcuXIFBw4cQHJyMmJjY4O6diTQ0dGBH/zgByFnnwvKYjY3NyM9PT1qurAMw2DZsmV46623ok6MwIB3T15eHhYuXIglS5aEXN/UqVORl5cHq9Ua1Pf1u9/9Djt27IDdbofT6Qy5PeGGRCKBVqvF6dOng64jKGEuWrQIVqs16IuGCxMmTMC9994Ls9kctQl5mpqaUFBQAJPJBJFIxFq9/f392Lp1a9C/A5vNhnXr1uH48eNR84AfjkqlwpYtW1BUVBTU+wMWZjRYS7FYDIVCgW3btmH9+vV8N4czysrKcOTIEVRVVaGtrY31+v/xj3/gxo0bwQWUHqShoQErV67E9evXo26CKJTJoIDHmC+++GJEi5JhGOTn56O9vT1qRWm326HX63Hx4kVYLBZORAkACxYsQGtrK2pra4OuIz09HZcvX8ZvfvMbxMfHs9e4MOCbb75BTk5OUOPNgCxmJFtLsViMyZMn47333ovabisAtLa2oqCgABs3bsTUqVM5v96sWbOQlZUFs9kc8k4Zm82GvLw8fPbZZ7h58yZLLeQXsViM/Px8HDhwIKD3BWQxI9VaMgyDRYsWoaWlJapFWVFRAb1eD7PZPC6iBICWlhaYzWbo9fqQZyI1Gg0aGxuxefPmqJmE6+vrw9GjR1FfXx/Q+/y2mE1NTcjKyoq4fZZxcXF45ZVXYDAY+G4KZzgcDhgMBjgcDphMJpw5c2bc23D16lVUVlaipqaGlfqamprw5JNP4sqVK6zUxzfJycn48ssv/T7fb4tpMpkiSpRisRjTpk3DyZMno1qUHR0dyMjIwJw5c/Dzn/+cF1ECQEJCAlJSUrBjxw5W6tPpdDh//jx0Oh3kcjkrdfLJ1atX8fvf/97v8/0SpsPhwKFDkZO+dOLEifjhD3+IL774gvcIAVxSW1uLvLw8mEwmzJo1i+/mYNmyZTh27NhQ9q5QYRgmarq23333HYqLi/3u7vslzOrqalbXwLgkISEB5eXlOHz4MPfhH3ikqKgIlZWVsFqtQQfe4oKamhoYDAZ0dHSwVmdxcTEsFgsSExNZq5MPent7/e5R+DXGnDlzJlpbW0NuGNdoNBpUVVUhPT2d76ZwhtsBfenSpViwYAHfzfGKvztRAqWjowNZWVkR8Vv0hUqlwhdffDHmpogxLWZra6vPJKLhRGJiIiwWS1SL0j0BZzQaw1aUwIBX0KpVq1BYWMhqvUlJSWhsbERqamrE9OBG4nQ6/Vo6GVOYe/bsCfs1pXvvvRcnT54c17if482OHTtgMBhgsVgi4kc5ffp0yGQy7N69m9V6GYbBxx9/jLS0tIh0hu/u7kZJScmYY80xhXngwIGwDWMokUiQkpKClpaWqNnAPBK3A/qNGzewc+dOzrx4uKC0tBTvvvsu6xvpZTIZTpw4gSeeeMIjCHYk4HQ6x1zXHHWM2dHRgYceeiisJhfcyOVyzJkzB3V1dRE/Y+eL5uZmFBQUwGg0IiEhge/mBMXUqVOh1+tRU1PDyWbzTZs2oaKiIuIcX8Za1xzVYtbX18PlcrHeqFCRSCSYM2cOjh8/HrWi3L17NwoLC1FTUxOxogSAS5cuoaSkBAUFBSF7BnmjrKwMRqMREydOZL1uLrl27Rqam5t9vj6qMA8dOhSW48v7778fx48fj8rlEIfDgYKCAnz66aewWq24dOkS300KGZFIhKVLlwa9BWosDAYDnnvuuYjq1nZ3d6OystLn6z67sg6HAxMnTuTkKRcKKSkpaGxsjEpL6XZAX7t2LaZPn853c1jn7bffRk5ODlasWMFJ/Xq9HrW1tWH3m/VFQkKCT5dDnxazqakp7CzStGnTgt41H+7U1tYObWiORlECgNlsxmuvvcbZOmRVVRUeffTRiJi1BgaWlXxNjPkU5ocffhhWkz6JiYk4ceJEVEWrc2MwGFBZWQmLxRJW3znbnDlzBjU1NdDr9Zz5XR87dgyPPPIIxGIxJ/WzSVdXF959912vr/nsyoZT+BCNRgOLxRJ165Tu/YdPP/10QGEkIx22d6KMxG63Y968eRHhIeSrO+vTYv7rX//itEH+kpCQgJqamqgTZUNDAzIyMlBaWnpXiRIYuKdz5sxBcXExJ/UzDAOr1Ypp06ZxUj+bOBwOr37FXoVpt9vDIoK2SqXCiy++GHWbm4uLi7F169aQwkhGOkuWLMHp06cD3kDsLxqNBseOHUNcXBwn9bOJt904XoXZ3NzMu7uTWCyGVqvlbIqdD+x2O7KysgAA27dvHzMtQbRTVVWFoqIiVneiDMe9P1SlUnFSPxt0dXXhk08+8TjuU5h8RyybPHkyjhw5wmsb2KSpqQkZGRnYuHEjK7Fdo4GWlhZUVVUhLy+PsyWO1atXY/HixWE9U+ut1+BVmGfPnuW1K5uQkID33nsvapZFdu/ejaKiItTU1ECpVPLdnLDi6tWreOmll1BQUMDZNQ4ePBjWezm/+uorjweTV2F+9tln49Igb0TTuNLhcECv1+PTTz+FxWKJCi8eLpg6dSo0Gg3Kyso4qV8mk4X1eFOpVHq453kVJl8/ILFYjMWLF0fFuLK1tRXz5s1DTk4OVq5cyVssnkihpKSE1bAkIwnn8abL5fIYZ/ucleWD+Ph4HDx4kJdrs0l1dTX0ej2qqqrGLYxkpHPmzBmYzWYYDAbOMsi5x5tCoZCT+oOlt7fX4zPfIUzi6se3Xd9BoIoF1ApgHD3yGIaB2WwOOzfAQHA4HCgsLMSRI0fQ2NiIq1ev8t2kiKKtrQ0mk4mVGLW++OMf/xh2Xdre3l6cP3/+jmN3CFMQI8K1K/+DTCwGbnYD4+QLLBaL8dhjjyEzM3N8LsgB7ng0c+bMwQsvvEC7rkHS39+PnJwczoYzDMPgjTfeCLsu7UgvJY+ubMfFi4iJCTptZlCoVCpUVFSM6zXZpL6+Hnl5eSgpKQmLMJKRTmpqKux2O2e/iWeffRbf//73Oak7WDo7O+/432Nxx27/dvAvMaCWDPzpugV81wfIZIBUCPQOWlOxGJAPntM7zMIOPw4ncGOY6R3x2gSXGEaj0dM5nThxy+kCAQBBDCRCIQQjj0MAsUiEGBD09/fDCcHA+MHZDycAxIggFbhunx8jgjRGENAXNhbFxcU4ffo0rFbrXe8wwCYmkwlZWVlISUnhZIbebDZj8eLFuHbtGut1B8PIYY+HabTbrw9GLegDbt4C4BwQJQA4nED/rUEBigfGoDe6gRu3AKkMEA8elwsHusI3uoF+IaBwe/p7vjbpnslesm4R9DsJYoRiSEUiCOGCk4w8LoY4hqDPRQAIIBLGQAACp9OJGJEYUmEMBK5+9LoA8dD/TrAVj8FutyMjIwMA8PLLL1NRsox7MqiwsJCTyUitVouVK1eG7S6U0fusfX1AL24LSyYEnH23/46RDEwSqSUAhIBw8LjLCQyehlu3AJHw9nuGvaaWSlFd9a6nWIgTzhghRAJgQHTigb+JE06BAMJBoxcjGCk2AYRC0bAPJYAwZtDSDuIKOE2vJ01NTZg3bx6MRiP14uGQS5cuobS0FHl5eZzU//rrr2PChAmc1B0qvoXpFpbDOSAsMQCh884Jof5bgxZzsLhfixllOnrwNZFIhPzlyzFn9hzv5xF3d3XkceL9+DhRVlYGg8EAq9Ua1m5e0QKXYUlkMhn2798flt5YQ8J0ufrQ6yIDyxXEOeyUQaspV9y2lsCgYCXDllQGu7YOJwDh7eNCIdDvvP2ewddUKhXeLC8FiRF6eToIICDu7isAkAFLJxBCCIL+IbNHfLyffdxhJC9evBhxYSQjHTYS5PoiNzc3LCfshn7TAggAVz/iJ09GTP+ICRKHE8AIa4k+oNcJSBWD3Vnh4OuDY1P3cbET6O67/Z6bt6BQxWHz/9sCgUQGydBkDEF/fx/6BwUoEQrgdPaht78Pvf1OYKhbGwO4+geOuwSD7yfod7pAQOB09sM15v+B0dzcjKysLKxatQrLly8P8N0UNuAyLInJZAo7q+kRwaChoQF5eXkjBtxiQNbH2rpmXFwcLl++HBHOBBUVFdizZw/MZjN1GOCZhIQE6PV6NDY2sv7b4Ttix8hIBv71AmVCwDn2af6gUChQVFQU9qJ0e/GcOHECVquVijIMuHr1KoxGI/R6Pet179q1i1erOdJ100OYdwhGMdgdheP2ZFCISKVS/OpXv2KnMo4Il2SwFE/YTpDrRqvVYtKkSazWGQgjDZWHMDUaze1wF90jZltDJBKsZbglg6V4wnaCXDe7du3iLXv1mBYzKSmJsxCK4W4twzUZLMUTLhLk5ubmYsqUKazVFwgjrbXXMSYXDr7hbC1tNhvmzZsHtVqNX//619SLJwJoaWmByWRiPScKH1ZTLpdj9uzZdxzzKsz4+HjWLy4UCvHMM8+wXm+oNDQ0ICsrC6WlpWGdDJbiCRcJcnNzczF58mTW6vMHiUTiEZ7VqzC5MOcPP/xw2EVR37FjB7Zu3QqLxXLXhpGMdLhIkPvSSy+Nq1dXT08PUlJS7jjmVZhs77pXq9XYsGEDq3WGwvBksNu3b6dePBEO2wlyc3NzIZFIxj6RJaRSqUfgOa/CfOCBB1h9YgiFwrDZBN3c3IyMjAysWrUKy5Yt47s5FBZgOyyJRqPBY489xkLL/OPBBx/0OOZVmAsWLGBtsVUkEmH58uVhMekTLclgKZ6wnSB3w4YNkEqlLLRsdEQiERYvXuxx3KswdTodawGfFQoF1q1bx0pdwRKNyWApnrC5EyUzM3NcjIlSqfS6ddCrMBmGYW1m9p577uE1IZA7jOTChQtpGMm7gNTUVNhsNlRXV4dUj0wmQ05ODkut8o3D4fAaocGnrywbYpLL5di4cWPI9QRLdXU1CgoKUFVVFbXJYCmesLUTpbCwkHOrqdVqvV7DpzCfeOKJkPvYTqeTs7Teo+FwOGAwGHDkyBFYLBbqgH6XwVaCXJ1Ox2mEA6lUiqefftrraz6FqdPpQs74NXPmzHHPP2Kz2ZCVlYX77rsPL7zwAvXiuUu5dOkSjEZjyDlR1qxZw1KLPJFKpT5XK0btyjqdwe/1EolE474c4U4GW1JSctclg6V4wkaC3CVLlnAWsCs+Pt7DscCNT2HKZDI8/vjjQV/U12wTV9BksBRvhJogV6fTcZJSYazVilE3Sj/77LNBO7Q7nc5xydhFk8FSxiKUBLkymQxz585lv1EAfvGLX/h8bVRhZmZmBt2dTU1N5XxGyx1GkiaDpYxGqAly8/PzIRCwGyh84cKFo86/jCpMhmHw0EMPBXxRqVSKJ598MuD3BUJZWRmKiopgtVrDLpASJfwIJUFueno6q+NMhmG8BDm/kzFj/qxatSrg/WmjzTaFit1uh16vx8WLF2GxWKgDOsVvgk2Qy7aDzMSJE8fUx5jCzM3NDfjCEonE52xTKLS2tiIrKws5OTlYvnw59eKhBEywCXLZGmeq1Wrs2rVrzPPGFGYwnvaPPvpoQOf7gzsZrNlspslgKUET7E6UH//4x6xcPy4uzi9j51f4yg0bNkCtVvt1YZFIFNIyy0hoMlgK2wSTIHfJkiUhp6f011oCXgI+++Lee+/F119/7dfFKyoqguoCj6SjowMFBQV4+umnacQ6CuucOXMGFy9eRGlp6ZjnOhwOyOVy+CkXryQnJ+PLL7/061y/HwEGgwEKhWLM8wghrIwvaTJYCtcEkiBXJpOFtPynVqtx+PBhv8/3W5ijLYYO5+bNmyELs6ioCOXl5bBardSLh8IpJpMJlZWVfoUlCTZmlUgkQn5+fkCzu34Lk2EY/PSnPx0z5Mj3vvc9vy8+EpvNhoyMDKjVapoMljIuBJIgN9iZWZVKhTfffDOg9wQ0ml23bt2Y3dmZM2cG1AA3TU1NyMrKgtFopGEkKeOKvwlytVptwB5AarUa+/fvD7gbHJAwtVot8vPzfVrNYGdk3clgLRYLTQZL4QV/wpI88sgjAdUpFouxaNGioCZCA57/3bZtm09PIIVCEdD4kiaDpYQTYyXITUlJCchi3nPPPTh48GBQbQlYmBqNBqtWrfJqmgOZkaXJYCnhyGhhSZKSkvxeLomLi8OxY8eCnskNasX09ddf92o1XS6XXw2pqKhAYWEhzGYzDSNJCStaWlpgNpt9Oh/4EwharVZjx44dIa1OBCVMmUyGoqIij4kgh8Mx6pSyO4wkTQZLCWdGS5A7Vhws97hy9erVIbXBb8+fkTgcDsyePRsXLly447iv6lpbW1FQUIC1a9fSiHWUiODo0aNQq9XYsmXL0LH4+Hhcu3bN6/kSiQQPP/wwjh8/HvJe5KCd/2QyGd555507Nnv6inZQW1uLgoICmEwmKkpKxOAtQe5oET1mz57NiiiBEIQJDMRDefbZZ4ca4q3/bTAYUFlZCYvFQpPBUiKOkQlyfW2YTklJwQcffMBa1I7Q3OUxMBHk9vYZvh3LnQz2vvvuo8lgKRHLyAS53nZZpaSkoLGxkdVQrSELUyaT4dChQ3eY+OHJYGkYSUqkMzxB7vAJT3dAALZFCbAgTGDAIygtLQ0zZsygyWApUYk7Qa5UKoVQKERsbCy0Wi0nogQCEKZlrQACwYgyvxxuX50lS5bAarWitbUVf//736kXDyXqKC0txX/+8x/I5XLk5OTg9OnTnGUaCGi5pK18PmZ8/grIniwAFqwVZGPvmjqQPVk4cOAAtmzZgsuXL7PeSK1Wy8oXkJ6ePvR3RUVFUHFGKRSRSIS+vj5OrxGAMNtQPn8GPn+FYM9AfGVY1gqQjbpBoVIo0U9HRwc6OjrueMgDACxrIcjeO/TvmjqCPViLtdiDYOTh/xiz7SgOnVqD3MGLtJXPR/beNJRtpqKk3D0kJSWNEGUbyucLIMgG6ggBGSy5tQIIslvw4IzgruO3MNuOHsIp7EX24PhyFSpByElspP4ClLuWgV7kJpThAtmD4SYqa08d1mAW7g9SH34Ksw1HD53CmrrBJ8KFMmDTDMwvpxM8lLuXtvJV2HQqDWWVG+Gpvxl4sCwXwfYn/RtjtpVj/ozP8cqwp4JlrQDZe9egbsSTgkK5Oxi0lrO4mWPxy2K2HT2EU2uGq78N51sApD2IILvQFEqEcwGfnwLSgh1EjoEfcTwGu7Gv3JalZe0MbDoFrKnzZsIplLuAtvPg0sl0TIspEAyIcG/2bceC7L1pKLtAgpoGplCigun3YxaAU59f8Pqypfy2800wBL0fk0K56xlcu0wru4CTQ8sTnuv9wUCFSaGEQls55s/YhFNDB9JQdiH0ZUQqTAolDGFldwmFQmEXKkwKJQz5/6GFNae7ZI7lAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 导入模块"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
-   ],
-   "outputs": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 验证题目格式"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "is_sif(item['stem'])"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
-    "is_sif(text)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
-    "to_sif(text)\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 令牌化\r\n",
-    "\r\n",
-    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
-    "\r\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
-   ],
-   "outputs": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 文本解析结果"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokens.text_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图',\n",
-       " '古希腊',\n",
-       " '数学家',\n",
-       " '希波',\n",
-       " '克拉底',\n",
-       " '研究',\n",
-       " '几何图形',\n",
-       " '此图',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '直径',\n",
-       " '直角三角形',\n",
-       " '斜边',\n",
-       " '直角',\n",
-       " '三边',\n",
-       " '围成',\n",
-       " '区域',\n",
-       " '记',\n",
-       " '黑色',\n",
-       " '记',\n",
-       " '其余部分',\n",
-       " '记',\n",
-       " '图形',\n",
-       " '中',\n",
-       " '随机',\n",
-       " '取',\n",
-       " '一点',\n",
-       " '此点',\n",
-       " '取自',\n",
-       " '概率',\n",
-       " '记']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 公式解析结果"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokens.formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 自定义参数，得到定制化解析结果"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\": {\r\n",
-    "            \"method\": \"linear\",\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
-    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
-    "> 因此，ast 可以看做是公式的语法结构表征。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\":{\r\n",
-    "            \"method\": \"ast\",\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[<Formula: ABC>,\n",
-       " <Formula: BC>,\n",
-       " <Formula: AB>,\n",
-       " <Formula: AC>,\n",
-       " <Formula: \\bigtriangleup ABC>,\n",
-       " <Formula: I>,\n",
-       " <Formula: II>,\n",
-       " <Formula: III>,\n",
-       " <Formula: I,II,III>,\n",
-       " <Formula: p_1,p_2,p_3>]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 语法树展示："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "f = sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\":{\r\n",
-    "            \"method\": \"ast\",\r\n",
-    "            \"return_type\": \"ast\",\r\n",
-    "            \"ord2token\": True,\r\n",
-    "            \"var_numbering\": True,\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens\r\n",
-    "f\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "for i in range(0, len(f)):\r\n",
-    "    ForestPlotter().export(\r\n",
-    "        f[i], root_list=[node for node in f[i]],\r\n",
-    "    )\r\n",
-    "# plt.show()\r\n"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
-    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\":{\r\n",
-    "            \"method\": \"ast\",\r\n",
-    "            \"return_type\": \"list\",\r\n",
-    "            \"ord2token\": True,\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\":{\r\n",
-    "            \"method\": \"ast\",\r\n",
-    "            \"ord2token\": True,\r\n",
-    "            \"return_type\": \"list\",\r\n",
-    "            \"var_numbering\": True\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_0',\n",
-       " 'mathord_2',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " ',',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " ',',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 综合训练\r\n",
-    "\r\n",
-    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\r\n",
-    "        symbol=\"fgm\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
   {
    "cell_type": "markdown",
    "source": [

From 668dde7c10c7e8b5f631c38e64ec97139fe76a37 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 23:38:54 +0800
Subject: [PATCH 107/149] update doc

---
 examples/Tokenizer/total_tokenize.ipynb | 162 ------------------------
 1 file changed, 162 deletions(-)

diff --git a/examples/Tokenizer/total_tokenize.ipynb b/examples/Tokenizer/total_tokenize.ipynb
index 23eee1de..a3638389 100644
--- a/examples/Tokenizer/total_tokenize.ipynb
+++ b/examples/Tokenizer/total_tokenize.ipynb
@@ -474,168 +474,6 @@
     }
    ],
    "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## tokenizer\r\n",
-    "\r\n",
-    "### 概述\r\n",
-    "\r\n",
-    "tokenizer为一个封装好的令牌化容器，让用户可以渐变的将item转换为令牌化列表，下面将介绍两种令牌化容器。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### TextTokenizer and PureTextTokenizer\r\n",
-    "\r\n",
-    "- ‘text’ Tokenizer ignores and skips the FormulaFigures and tokenize latex Formulas as Text\r\n",
-    "- ‘pure_text’ Tokenizer symbolizes the FormulaFigures as [FUMULA] and tokenize latex Formulas as Text"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### TextTokenizer"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "items = [{\r\n",
-    "        \"stem\": \"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\",\r\n",
-    "        \"options\": [\"1\", \"2\"]\r\n",
-    "        }]\r\n",
-    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
-    "tokens = tokenizer(items, key=lambda x: x[\"stem\"])\r\n",
-    "print(next(tokens))"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "['已知', '集合', 'A', '=', '\\\\left', '\\\\{', 'x', '\\\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<', '0', '\\\\right', '\\\\}', ',', '\\\\quad', 'B', '=', '\\\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\\\}', ',', '\\\\quad', 'A', '\\\\cap', 'B', '=']\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "items = [\"有公式$\\\\FormFigureID{wrong1?}$，如图$\\\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式$\\\\FormFigureBase64{wrong2?}$,$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$\"]"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "\r\n",
-    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
-    "tokens = [t for t in tokenizer(items)]\r\n",
-    "tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[['公式',\n",
-       "  '[FORMULA]',\n",
-       "  '如图',\n",
-       "  '[FIGURE]',\n",
-       "  'x',\n",
-       "  ',',\n",
-       "  'y',\n",
-       "  '约束条件',\n",
-       "  '公式',\n",
-       "  '[FORMULA]',\n",
-       "  '[SEP]',\n",
-       "  'z',\n",
-       "  '=',\n",
-       "  'x',\n",
-       "  '+',\n",
-       "  '7',\n",
-       "  'y',\n",
-       "  '最大值',\n",
-       "  '[MARK]']]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### PureTextTokenizer"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokenizer = get_tokenizer(\"pure_text\") # tokenizer = PureTextTokenizer()\r\n",
-    "tokens = [t for t in tokenizer(items)]\r\n",
-    "tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[['公式',\n",
-       "  '如图',\n",
-       "  '[FIGURE]',\n",
-       "  'x',\n",
-       "  ',',\n",
-       "  'y',\n",
-       "  '约束条件',\n",
-       "  '公式',\n",
-       "  '[SEP]',\n",
-       "  'z',\n",
-       "  '=',\n",
-       "  'x',\n",
-       "  '+',\n",
-       "  '7',\n",
-       "  'y',\n",
-       "  '最大值',\n",
-       "  '[MARK]']]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
   }
  ],
  "metadata": {

From f609df64fc89798d3b9a58781300a318fa2251fa Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sat, 18 Sep 2021 23:50:22 +0800
Subject: [PATCH 108/149] update doc

---
 docs/source/tutorial/zh/index.rst   |    2 +-
 examples/Tokenizer/t_tokenize.ipynb | 1620 +++++++++++++++++++++++++++
 2 files changed, 1621 insertions(+), 1 deletion(-)
 create mode 100644 examples/Tokenizer/t_tokenize.ipynb

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 035a3dc2..e5188f44 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -43,7 +43,7 @@
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>
+    令牌化  <../../build/blitz/tokenizer/t_tokenize.ipynb>
 
 
 向量化
diff --git a/examples/Tokenizer/t_tokenize.ipynb b/examples/Tokenizer/t_tokenize.ipynb
new file mode 100644
index 00000000..9be17885
--- /dev/null
+++ b/examples/Tokenizer/t_tokenize.ipynb
@@ -0,0 +1,1620 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 令牌化\r\n",
+    "\r\n",
+    "## 概述\r\n",
+    "\r\n",
+    "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## tokenize\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "此模块为令牌化的基础实现模块，仅需将分词后的item列表传入此函数中即可得到相应的令牌化结果。\r\n",
+    "\r\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\r\n",
+    "\r\n",
+    "#### 文本解析\r\n",
+    "\r\n",
+    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\r\n",
+    "\r\n",
+    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \r\n",
+    "  \r\n",
+    "\r\n",
+    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\r\n",
+    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\r\n",
+    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\r\n",
+    "\r\n",
+    "#### 公式解析\r\n",
+    "\r\n",
+    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \r\n",
+    "  "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 文本解析"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 句解析\r\n",
+    "\r\n",
+    "待实现..."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 词解析\r\n",
+    "\r\n",
+    "词解析分为两个主要步骤：  \r\n",
+    "\r\n",
+    "（1） 分词：  \r\n",
+    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \r\n",
+    "    本项目目前支持的分词工具有：`jieba`   \r\n",
+    "- 单字解析：按字符划分。\r\n",
+    "    \r\n",
+    "      \r\n",
+    "（2） 筛选：过滤指定的停用词。   \r\n",
+    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \r\n",
+    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\r\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 导入模块\r\n",
+    "from EduNLP.SIF.tokenization.text import tokenize "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输入\r\n",
+    "text = \"三角函数是基本初等函数之一\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 词组解析\r\n",
+    "\r\n",
+    "分词粒度参数选择 word： `granularity = \"word\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
+    "tokenize(text, granularity=\"word\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['三角函数', '初等', '函数']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 单字解析\r\n",
+    "\r\n",
+    "分词粒度参数选择 word： `granularity = \"char\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
+    "tokenize(text, granularity=\"char\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['三', '角', '函', '数', '基', '初', '函', '数']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 停用词表"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 获取自己的停用词表\r\n",
+    "spath = \"test_stopwords.txt\"\r\n",
+    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\r\n",
+    "stopwords = get_stopwords(spath)\r\n",
+    "stopwords"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出：传入停用词表（stopwords）\r\n",
+    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['三角函数', '是', '基本', '初等', '函数', '之一']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 公式解析\r\n",
+    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 导入模块\r\n",
+    "from EduNLP.SIF.tokenization.formula import tokenize"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输入"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokenize(formula, method=\"linear\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\r\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " '{ }',\n",
+       " 'mathord',\n",
+       " '+',\n",
+       " 'mathord',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\r\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['mathord_con',\n",
+       " '{ }',\n",
+       " 'mathord_0',\n",
+       " '+',\n",
+       " 'mathord_1',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord_0']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 综合解析\r\n",
+    "\r\n",
+    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\r\n",
+    "```python\r\n",
+    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\r\n",
+    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\r\n",
+    "```\r\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# 导入模块\r\n",
+    "from EduNLP.Tokenizer import get_tokenizer\r\n",
+    "\r\n",
+    "# 输入\r\n",
+    "item = {\r\n",
+    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
+    "}\r\n",
+    "\r\n",
+    "# 输出\r\n",
+    "tokenizer = get_tokenizer(\"text\")\r\n",
+    "tokens = tokenizer(item)\r\n",
+    "next(tokens) "
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " 'ABC',\n",
+       " '斜边',\n",
+       " 'BC',\n",
+       " '直角',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\x08',\n",
+       " 'igtriangleupABC',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " 'I',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " 'II',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " 'III',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " '概率',\n",
+       " '记',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3',\n",
+       " '[MARK]',\n",
+       " '[FIGURE]']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 调用sif4sci函数来进行令牌化\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
+    "\n",
+    "![Figure](../../asset/_static/item.png)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "item = {\r\n",
+    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
+    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\r\n",
+    "}\r\n",
+    "item[\"stem\"]"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 加载图片：`$\\\\FigureID{1}$`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from PIL import Image\r\n",
+    "img = Image.open(\"../../asset/_static/item_figure.png\")\r\n",
+    "figures = {\"1\": img}\r\n",
+    "img"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 导入模块"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 验证题目格式"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "is_sif(item['stem'])"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "is_sif(text)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "to_sif(text)\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 题目切分及令牌化\r\n",
+    "\r\n",
+    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 题目切分"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 基本切分\r\n",
+    "分离文本、公式、图片和特殊符号。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\r\n",
+    "segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.text_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
+       " '的斜边',\n",
+       " ', 直角边',\n",
+       " ', ',\n",
+       " '.',\n",
+       " '的三边所围成的区域记为',\n",
+       " ',黑色部分记为',\n",
+       " ', 其余部分记为',\n",
+       " '.在整个图形中随机取一点，此点取自',\n",
+       " '的概率分别记为',\n",
+       " ',则']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 公式部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.formula_segments\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I,II,III',\n",
+       " 'p_1,p_2,p_3']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 图片部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.figure_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[\\FigureID{1}]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.figure_segments[0].figure"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 特殊符号"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.ques_mark_segments"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['\\\\SIFChoice']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 标记化切分  \r\n",
+    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    " - symbol:\n",
+    "    - \"t\": text\n",
+    "    - \"f\": formula\n",
+    "    - \"g\": figure\n",
+    "    - \"m\": question mark"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 令牌化\r\n",
+    "\r\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
+    "\r\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens.text_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " '斜边',\n",
+       " '直角',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " '概率',\n",
+       " '记']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 公式解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens.formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 自定义参数，得到定制化解析结果"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\": {\r\n",
+    "            \"method\": \"linear\",\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[<Formula: ABC>,\n",
+       " <Formula: BC>,\n",
+       " <Formula: AB>,\n",
+       " <Formula: AC>,\n",
+       " <Formula: \\bigtriangleup ABC>,\n",
+       " <Formula: I>,\n",
+       " <Formula: II>,\n",
+       " <Formula: III>,\n",
+       " <Formula: I,II,III>,\n",
+       " <Formula: p_1,p_2,p_3>]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 语法树展示："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "f = sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"return_type\": \"ast\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "            \"var_numbering\": True,\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens\r\n",
+    "f\r\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "for i in range(0, len(f)):\r\n",
+    "    ForestPlotter().export(\r\n",
+    "        f[i], root_list=[node for node in f[i]],\r\n",
+    "    )\r\n",
+    "# plt.show()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"return_type\": \"list\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "            \"return_type\": \"list\",\r\n",
+    "            \"var_numbering\": True\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_0',\n",
+       " 'mathord_2',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 综合训练\r\n",
+    "\r\n",
+    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\r\n",
+    "        symbol=\"fgm\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## tokenizer\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "tokenizer为一个封装好的令牌化容器，让用户可以渐变的将item转换为令牌化列表，下面将介绍两种令牌化容器。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### TextTokenizer and PureTextTokenizer\r\n",
+    "\r\n",
+    "- ‘text’ Tokenizer ignores and skips the FormulaFigures and tokenize latex Formulas as Text\r\n",
+    "- ‘pure_text’ Tokenizer symbolizes the FormulaFigures as [FUMULA] and tokenize latex Formulas as Text"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### TextTokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "items = [{\r\n",
+    "        \"stem\": \"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\",\r\n",
+    "        \"options\": [\"1\", \"2\"]\r\n",
+    "        }]\r\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
+    "tokens = tokenizer(items, key=lambda x: x[\"stem\"])\r\n",
+    "print(next(tokens))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['已知', '集合', 'A', '=', '\\\\left', '\\\\{', 'x', '\\\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<', '0', '\\\\right', '\\\\}', ',', '\\\\quad', 'B', '=', '\\\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\\\}', ',', '\\\\quad', 'A', '\\\\cap', 'B', '=']\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "items = [\"有公式$\\\\FormFigureID{wrong1?}$，如图$\\\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式$\\\\FormFigureBase64{wrong2?}$,$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$\"]"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "\r\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
+    "tokens = [t for t in tokenizer(items)]\r\n",
+    "tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[['公式',\n",
+       "  '[FORMULA]',\n",
+       "  '如图',\n",
+       "  '[FIGURE]',\n",
+       "  'x',\n",
+       "  ',',\n",
+       "  'y',\n",
+       "  '约束条件',\n",
+       "  '公式',\n",
+       "  '[FORMULA]',\n",
+       "  '[SEP]',\n",
+       "  'z',\n",
+       "  '=',\n",
+       "  'x',\n",
+       "  '+',\n",
+       "  '7',\n",
+       "  'y',\n",
+       "  '最大值',\n",
+       "  '[MARK]']]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### PureTextTokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokenizer = get_tokenizer(\"pure_text\") # tokenizer = PureTextTokenizer()\r\n",
+    "tokens = [t for t in tokenizer(items)]\r\n",
+    "tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[['公式',\n",
+       "  '如图',\n",
+       "  '[FIGURE]',\n",
+       "  'x',\n",
+       "  ',',\n",
+       "  'y',\n",
+       "  '约束条件',\n",
+       "  '公式',\n",
+       "  '[SEP]',\n",
+       "  'z',\n",
+       "  '=',\n",
+       "  'x',\n",
+       "  '+',\n",
+       "  '7',\n",
+       "  'y',\n",
+       "  '最大值',\n",
+       "  '[MARK]']]"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file

From 72788f1a306b5843e0be63ff111275fee4616155 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 00:18:33 +0800
Subject: [PATCH 109/149] update doc

---
 examples/Tokenizer/t_tokenize.ipynb | 1133 ---------------------------
 1 file changed, 1133 deletions(-)

diff --git a/examples/Tokenizer/t_tokenize.ipynb b/examples/Tokenizer/t_tokenize.ipynb
index 9be17885..a3638389 100644
--- a/examples/Tokenizer/t_tokenize.ipynb
+++ b/examples/Tokenizer/t_tokenize.ipynb
@@ -474,1139 +474,6 @@
     }
    ],
    "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 调用sif4sci函数来进行令牌化\r\n",
-    "\r\n",
-    "### 概述\r\n",
-    "\r\n",
-    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
-   ],
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
-    "\n",
-    "![Figure](../../asset/_static/item.png)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "item = {\r\n",
-    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
-    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\r\n",
-    "}\r\n",
-    "item[\"stem\"]"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 加载图片：`$\\\\FigureID{1}$`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "from PIL import Image\r\n",
-    "img = Image.open(\"../../asset/_static/item_figure.png\")\r\n",
-    "figures = {\"1\": img}\r\n",
-    "img"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAACICAYAAADzlJeRAAAe3klEQVR4nO2df1RT9/3/nyE/SWJyBdSUOgs72mKP1XhK24j2CGon4FagbtTMtpZt6rDzR7rjKt+2C+rnHGxdBbr2xB9zgc4j1NMJbpXg2hlsp2DnjrSHVaqcgquWbFqNLUgQkvf3DwgiSSA/7uUm8f04530O3Ny87zu5ed7X+8fr/XoJCCEEFAolrIjhuwEUCsUTKkwKJQyhwqRQwhAR3w2IBJqamuBwOHy+rtFokJKSMo4tokQ7VJgAGhoa0NzcDLvdjn//+99oa2uDw+FAa2srAIBhmFHf73K58O233wIAYmNj8cADDwAAZs+ejblz50Kr1UKn00Emk3H6OSjRg+Bum5Vtbm5Gc3MzrFYrPvnkE7S2toJhGPT09KC3t5f160mlUsTGxqKrqwsMw2D69OlYunQpdDodMjMzWb8eJTqIemHa7XbU19fjT3/6E/72t79BoVDA6XSiq6uL13YxDIOuri48/vjjyM/PR2ZmJpKSknhtEyV8iEph2mw21NbW4tChQzh16hSkUulQVzMckcvliImJgUwmQ3Z2NgwGA7RaLd/NovBIVAmzuroar732Gs6fPw8AuHnzJs8tCg61Wg2FQoFXX30VK1asGHOMS4k+Il6YNpsNf/jDH7Bz504ACGvLGChyuRxOpxOLFy/G+vXr6Zj0LiJihdnU1IQ33ngDdXV1cLlcoy5nRAMMw0AkEmHnzp14/vnn+W4OhWMiTpjNzc0oLCxEa2sr7HY7380Zd5RKJWQyGRVolBMxwrTZbFi/fj0++OAD3Lhxg+/m8A4VaHQT9sK02+347W9/i8rKyqgaP7KFUqmESqXCn//8Z+h0Or6bQ2GJsPaV3bdvH5KTk7Fv3z4qSh90dXXh66+/RlZWFjZs2HBXdu+jEhKGdHZ2Ep1OR9RqNQFAi59FJpORSZMmEYvFwuLdqCNlZRdYrI/iD2FnMfft24dZs2ahqamJjiUDxOFw4MqVK9Dr9XjqqadYsJ5tKJ+fjUNsNI4SGHw/Gdxcv36d5OXlUSvJUhGLxWTSpEmksbEx6HtyoSyNACBr6li80RS/CAuL2dDQgPvvvx/vv/8+tZIs0dfXhytXruBHP/oRSkpKAq+grRw78QrK0thvG2VseJ+VLS0txbZt2+ikBYeoVCrMmzcP1dXVfrr3WbB2/nlsPnk/dgr+Dw9eOImN07lrX0NDA4Db+17/+c9/4uuvv/Z67uzZs5GcnAwASEpKuqNEFXya65/97GeEYRjeu313QxGLxSQ5OZmcO3duzPtSt2YNqSOEkAtlJA2Df7NAT08PsVqtxGg0Ep1OR2JjYwkAwjAMYRiGiESigD6TUqkkDMMQpVJJABCtVkuys7OJ0WgMqQsfDvAizOvXr5NHH3106MbQMn4lLi6OHDlyxOe9cY8rb5fQhHnu3Dny8ssvk+TkZCISicbtQcwwDImNjSUZGRnEZDKR9vb2ED7F+DPuwmxvbycpKSlEIpHw/iO9W8uUKVPIO++843lzLpSRNcOXRurWEKSVkUAXSxobG0lhYSFJSEggKpUqYEvIdpHL5USpVJLExERiMpnI9evXQ/kJjwvjKsyzZ8+SxMRE3n+YtIBMnDiRvP3227dvzoUykjZChBfK0vwWZk9PDzGbzSQxMTGshydyuZwoFAqSnZ3N8novu4ybMM+ePUs0Gg3vN4aW2yUuLm5InAPH0siAwbxAytKGn+u7O9vZ2Uk2bdpEJkyYMDTWi5TCMAxJSEggZrN5nFTgP+MizPb2dmopw7TExcWRjRs3BnxPOzs7ycqVK4lcLue9qxpqUSqVYSdQzoXZ3t5OkpKSeP/yafFdGIbxW5w9PT3EaDSS+Ph4IhaLeW87m8Ut0JqaGk414Q+cCpOKMnIKwzBk165do97PqqoqotFoiEKh4L29XBa1Wk10Oh2vM7mcCfP69eskJSWF9y+ZFv/LlClTvC6luDcVqFQq3ts4noVhGGI0GklPTw9XMvEJJ54/DocDjzzyCFpaWtiuOuqRy+WQSCQecW6Tk5OhVqvvODcnJwcA8NFHH+G///0vrl27ht7eXvT19QUdnnPKlCloaGgYiixfX1+PZ555Bt98802QnyiyUSgUiI+Px5EjR8Y1ciEnwnzqqafw/vvvo6+vj+2qowaGYYYiuLtFl5OTM5RuQavVshodr6GhAXa7Hc3Nzaivr4fNZsNXX30FAIiJiUF/f//QuSqVCrt27cJf/vIXfPTRR9RdEkB8fDxKSkqwevXqcbke68Kkvq+eiEQiKJVKdHV1ITU1FUuXLkV6enpY5Dxxi7WjowN//etfceLECXz33XdwuVx3iJUyEFZ00aJFOHjwIPfpLtjsFzc2NpKEhATexwZ8F7frmUgkIjqdjpSUlESU7+bhw4eJRqMharWayOVyIpVKSUxMDO/fazgUsVhMZsyY4ZfPcSiwJszOzk4ybdo03r84vopMJiNKpZJotdqIE+JwTCYTSU9PJ+3t7aSqqoqsWLGCnDt3jphMJpKZmUkkEknULZMEU6ZMmULOnj3L2X1gRZg9PT0kNTWV9y+Lj6JWq0liYiLZvn17xDlKD6enp4esWLGC/PKXvyQ9PT3k448/Jh9//DHZtGkTKS0tveM8s9k8tDtEKBTyfg/4KhqNhrMHMCvCNBqNUb+2NVKMKpWKPPfcc5w+NceLc+fOEa1WS6qqqoYE6S49PT0kMzOTWK1Wj/e1t7eT7du3k8TERCKXy3m/L3yUhISEUXfrBEvIwmxvbw9rp2U2i1KpJNnZ2WHhGcIWVVVVRKfTkXPnznmI0l06OzuJVqslnZ2dPus5e/Ysee6554hSqSRSqZT3ezWexdf6byiEPCs7b948NDU1hVJFWCMSiSCXy5Gbm4utW7dGzU55h8OBoqIi2Gw2mEymMdecRSIRioqKYLFYRp2RdDgceOutt1BSUoLu7m5Oco6GI1OmTMHevXvx5JNPslJfSMLct28fNm/eHJVxemQyGWQyGX7yk59g27Zt0Gg0fDeJNWw2G/R6PXJycpCamur3+86cOYOLFy+itLR0zHMdDgcqKirw6quvoru7Gz09PaE0OSKIj4/Hhx9+yIojQtDCtNlsmDt3Lmw2W8iNCCcUCgViYmKwbt06bNmyJepS4NXX18NgMMBsNge1Trl//34sXLgwoLQMFRUV2Lx5M3p6etDd3R3wNSOJpKQkWK3WkHtWQUfJW7duXVS5aYnFYjAMg+LiYvzvf//Djh07ok6UxcXFKC8vR2NjY9DOAyaTCZWVlQENX55//nlcuXIFBw4cQHJyMmJjY4O6diTQ0dGBH/zgByFnnwvKYjY3NyM9PT1qurAMw2DZsmV46623ok6MwIB3T15eHhYuXIglS5aEXN/UqVORl5cHq9Ua1Pf1u9/9Djt27IDdbofT6Qy5PeGGRCKBVqvF6dOng64jKGEuWrQIVqs16IuGCxMmTMC9994Ls9kctQl5mpqaUFBQAJPJBJFIxFq9/f392Lp1a9C/A5vNhnXr1uH48eNR84AfjkqlwpYtW1BUVBTU+wMWZjRYS7FYDIVCgW3btmH9+vV8N4czysrKcOTIEVRVVaGtrY31+v/xj3/gxo0bwQWUHqShoQErV67E9evXo26CKJTJoIDHmC+++GJEi5JhGOTn56O9vT1qRWm326HX63Hx4kVYLBZORAkACxYsQGtrK2pra4OuIz09HZcvX8ZvfvMbxMfHs9e4MOCbb75BTk5OUOPNgCxmJFtLsViMyZMn47333ovabisAtLa2oqCgABs3bsTUqVM5v96sWbOQlZUFs9kc8k4Zm82GvLw8fPbZZ7h58yZLLeQXsViM/Px8HDhwIKD3BWQxI9VaMgyDRYsWoaWlJapFWVFRAb1eD7PZPC6iBICWlhaYzWbo9fqQZyI1Gg0aGxuxefPmqJmE6+vrw9GjR1FfXx/Q+/y2mE1NTcjKyoq4fZZxcXF45ZVXYDAY+G4KZzgcDhgMBjgcDphMJpw5c2bc23D16lVUVlaipqaGlfqamprw5JNP4sqVK6zUxzfJycn48ssv/T7fb4tpMpkiSpRisRjTpk3DyZMno1qUHR0dyMjIwJw5c/Dzn/+cF1ECQEJCAlJSUrBjxw5W6tPpdDh//jx0Oh3kcjkrdfLJ1atX8fvf/97v8/0SpsPhwKFDkZO+dOLEifjhD3+IL774gvcIAVxSW1uLvLw8mEwmzJo1i+/mYNmyZTh27NhQ9q5QYRgmarq23333HYqLi/3u7vslzOrqalbXwLgkISEB5eXlOHz4MPfhH3ikqKgIlZWVsFqtQQfe4oKamhoYDAZ0dHSwVmdxcTEsFgsSExNZq5MPent7/e5R+DXGnDlzJlpbW0NuGNdoNBpUVVUhPT2d76ZwhtsBfenSpViwYAHfzfGKvztRAqWjowNZWVkR8Vv0hUqlwhdffDHmpogxLWZra6vPJKLhRGJiIiwWS1SL0j0BZzQaw1aUwIBX0KpVq1BYWMhqvUlJSWhsbERqamrE9OBG4nQ6/Vo6GVOYe/bsCfs1pXvvvRcnT54c17if482OHTtgMBhgsVgi4kc5ffp0yGQy7N69m9V6GYbBxx9/jLS0tIh0hu/u7kZJScmYY80xhXngwIGwDWMokUiQkpKClpaWqNnAPBK3A/qNGzewc+dOzrx4uKC0tBTvvvsu6xvpZTIZTpw4gSeeeMIjCHYk4HQ6x1zXHHWM2dHRgYceeiisJhfcyOVyzJkzB3V1dRE/Y+eL5uZmFBQUwGg0IiEhge/mBMXUqVOh1+tRU1PDyWbzTZs2oaKiIuIcX8Za1xzVYtbX18PlcrHeqFCRSCSYM2cOjh8/HrWi3L17NwoLC1FTUxOxogSAS5cuoaSkBAUFBSF7BnmjrKwMRqMREydOZL1uLrl27Rqam5t9vj6qMA8dOhSW48v7778fx48fj8rlEIfDgYKCAnz66aewWq24dOkS300KGZFIhKVLlwa9BWosDAYDnnvuuYjq1nZ3d6OystLn6z67sg6HAxMnTuTkKRcKKSkpaGxsjEpL6XZAX7t2LaZPn853c1jn7bffRk5ODlasWMFJ/Xq9HrW1tWH3m/VFQkKCT5dDnxazqakp7CzStGnTgt41H+7U1tYObWiORlECgNlsxmuvvcbZOmRVVRUeffTRiJi1BgaWlXxNjPkU5ocffhhWkz6JiYk4ceJEVEWrc2MwGFBZWQmLxRJW3znbnDlzBjU1NdDr9Zz5XR87dgyPPPIIxGIxJ/WzSVdXF959912vr/nsyoZT+BCNRgOLxRJ165Tu/YdPP/10QGEkIx22d6KMxG63Y968eRHhIeSrO+vTYv7rX//itEH+kpCQgJqamqgTZUNDAzIyMlBaWnpXiRIYuKdz5sxBcXExJ/UzDAOr1Ypp06ZxUj+bOBwOr37FXoVpt9vDIoK2SqXCiy++GHWbm4uLi7F169aQwkhGOkuWLMHp06cD3kDsLxqNBseOHUNcXBwn9bOJt904XoXZ3NzMu7uTWCyGVqvlbIqdD+x2O7KysgAA27dvHzMtQbRTVVWFoqIiVneiDMe9P1SlUnFSPxt0dXXhk08+8TjuU5h8RyybPHkyjhw5wmsb2KSpqQkZGRnYuHEjK7Fdo4GWlhZUVVUhLy+PsyWO1atXY/HixWE9U+ut1+BVmGfPnuW1K5uQkID33nsvapZFdu/ejaKiItTU1ECpVPLdnLDi6tWreOmll1BQUMDZNQ4ePBjWezm/+uorjweTV2F+9tln49Igb0TTuNLhcECv1+PTTz+FxWKJCi8eLpg6dSo0Gg3Kyso4qV8mk4X1eFOpVHq453kVJl8/ILFYjMWLF0fFuLK1tRXz5s1DTk4OVq5cyVssnkihpKSE1bAkIwnn8abL5fIYZ/ucleWD+Ph4HDx4kJdrs0l1dTX0ej2qqqrGLYxkpHPmzBmYzWYYDAbOMsi5x5tCoZCT+oOlt7fX4zPfIUzi6se3Xd9BoIoF1ApgHD3yGIaB2WwOOzfAQHA4HCgsLMSRI0fQ2NiIq1ev8t2kiKKtrQ0mk4mVGLW++OMf/xh2Xdre3l6cP3/+jmN3CFMQI8K1K/+DTCwGbnYD4+QLLBaL8dhjjyEzM3N8LsgB7ng0c+bMwQsvvEC7rkHS39+PnJwczoYzDMPgjTfeCLsu7UgvJY+ubMfFi4iJCTptZlCoVCpUVFSM6zXZpL6+Hnl5eSgpKQmLMJKRTmpqKux2O2e/iWeffRbf//73Oak7WDo7O+/432Nxx27/dvAvMaCWDPzpugV81wfIZIBUCPQOWlOxGJAPntM7zMIOPw4ncGOY6R3x2gSXGEaj0dM5nThxy+kCAQBBDCRCIQQjj0MAsUiEGBD09/fDCcHA+MHZDycAxIggFbhunx8jgjRGENAXNhbFxcU4ffo0rFbrXe8wwCYmkwlZWVlISUnhZIbebDZj8eLFuHbtGut1B8PIYY+HabTbrw9GLegDbt4C4BwQJQA4nED/rUEBigfGoDe6gRu3AKkMEA8elwsHusI3uoF+IaBwe/p7vjbpnslesm4R9DsJYoRiSEUiCOGCk4w8LoY4hqDPRQAIIBLGQAACp9OJGJEYUmEMBK5+9LoA8dD/TrAVj8FutyMjIwMA8PLLL1NRsox7MqiwsJCTyUitVouVK1eG7S6U0fusfX1AL24LSyYEnH23/46RDEwSqSUAhIBw8LjLCQyehlu3AJHw9nuGvaaWSlFd9a6nWIgTzhghRAJgQHTigb+JE06BAMJBoxcjGCk2AYRC0bAPJYAwZtDSDuIKOE2vJ01NTZg3bx6MRiP14uGQS5cuobS0FHl5eZzU//rrr2PChAmc1B0qvoXpFpbDOSAsMQCh884Jof5bgxZzsLhfixllOnrwNZFIhPzlyzFn9hzv5xF3d3XkceL9+DhRVlYGg8EAq9Ua1m5e0QKXYUlkMhn2798flt5YQ8J0ufrQ6yIDyxXEOeyUQaspV9y2lsCgYCXDllQGu7YOJwDh7eNCIdDvvP2ewddUKhXeLC8FiRF6eToIICDu7isAkAFLJxBCCIL+IbNHfLyffdxhJC9evBhxYSQjHTYS5PoiNzc3LCfshn7TAggAVz/iJ09GTP+ICRKHE8AIa4k+oNcJSBWD3Vnh4OuDY1P3cbET6O67/Z6bt6BQxWHz/9sCgUQGydBkDEF/fx/6BwUoEQrgdPaht78Pvf1OYKhbGwO4+geOuwSD7yfod7pAQOB09sM15v+B0dzcjKysLKxatQrLly8P8N0UNuAyLInJZAo7q+kRwaChoQF5eXkjBtxiQNbH2rpmXFwcLl++HBHOBBUVFdizZw/MZjN1GOCZhIQE6PV6NDY2sv7b4Ttix8hIBv71AmVCwDn2af6gUChQVFQU9qJ0e/GcOHECVquVijIMuHr1KoxGI/R6Pet179q1i1erOdJ100OYdwhGMdgdheP2ZFCISKVS/OpXv2KnMo4Il2SwFE/YTpDrRqvVYtKkSazWGQgjDZWHMDUaze1wF90jZltDJBKsZbglg6V4wnaCXDe7du3iLXv1mBYzKSmJsxCK4W4twzUZLMUTLhLk5ubmYsqUKazVFwgjrbXXMSYXDr7hbC1tNhvmzZsHtVqNX//619SLJwJoaWmByWRiPScKH1ZTLpdj9uzZdxzzKsz4+HjWLy4UCvHMM8+wXm+oNDQ0ICsrC6WlpWGdDJbiCRcJcnNzczF58mTW6vMHiUTiEZ7VqzC5MOcPP/xw2EVR37FjB7Zu3QqLxXLXhpGMdLhIkPvSSy+Nq1dXT08PUlJS7jjmVZhs77pXq9XYsGEDq3WGwvBksNu3b6dePBEO2wlyc3NzIZFIxj6RJaRSqUfgOa/CfOCBB1h9YgiFwrDZBN3c3IyMjAysWrUKy5Yt47s5FBZgOyyJRqPBY489xkLL/OPBBx/0OOZVmAsWLGBtsVUkEmH58uVhMekTLclgKZ6wnSB3w4YNkEqlLLRsdEQiERYvXuxx3KswdTodawGfFQoF1q1bx0pdwRKNyWApnrC5EyUzM3NcjIlSqfS6ddCrMBmGYW1m9p577uE1IZA7jOTChQtpGMm7gNTUVNhsNlRXV4dUj0wmQ05ODkut8o3D4fAaocGnrywbYpLL5di4cWPI9QRLdXU1CgoKUFVVFbXJYCmesLUTpbCwkHOrqdVqvV7DpzCfeOKJkPvYTqeTs7Teo+FwOGAwGHDkyBFYLBbqgH6XwVaCXJ1Ox2mEA6lUiqefftrraz6FqdPpQs74NXPmzHHPP2Kz2ZCVlYX77rsPL7zwAvXiuUu5dOkSjEZjyDlR1qxZw1KLPJFKpT5XK0btyjqdwe/1EolE474c4U4GW1JSctclg6V4wkaC3CVLlnAWsCs+Pt7DscCNT2HKZDI8/vjjQV/U12wTV9BksBRvhJogV6fTcZJSYazVilE3Sj/77LNBO7Q7nc5xydhFk8FSxiKUBLkymQxz585lv1EAfvGLX/h8bVRhZmZmBt2dTU1N5XxGyx1GkiaDpYxGqAly8/PzIRCwGyh84cKFo86/jCpMhmHw0EMPBXxRqVSKJ598MuD3BUJZWRmKiopgtVrDLpASJfwIJUFueno6q+NMhmG8BDm/kzFj/qxatSrg/WmjzTaFit1uh16vx8WLF2GxWKgDOsVvgk2Qy7aDzMSJE8fUx5jCzM3NDfjCEonE52xTKLS2tiIrKws5OTlYvnw59eKhBEywCXLZGmeq1Wrs2rVrzPPGFGYwnvaPPvpoQOf7gzsZrNlspslgKUET7E6UH//4x6xcPy4uzi9j51f4yg0bNkCtVvt1YZFIFNIyy0hoMlgK2wSTIHfJkiUhp6f011oCXgI+++Lee+/F119/7dfFKyoqguoCj6SjowMFBQV4+umnacQ6CuucOXMGFy9eRGlp6ZjnOhwOyOVy+CkXryQnJ+PLL7/061y/HwEGgwEKhWLM8wghrIwvaTJYCtcEkiBXJpOFtPynVqtx+PBhv8/3W5ijLYYO5+bNmyELs6ioCOXl5bBardSLh8IpJpMJlZWVfoUlCTZmlUgkQn5+fkCzu34Lk2EY/PSnPx0z5Mj3vvc9vy8+EpvNhoyMDKjVapoMljIuBJIgN9iZWZVKhTfffDOg9wQ0ml23bt2Y3dmZM2cG1AA3TU1NyMrKgtFopGEkKeOKvwlytVptwB5AarUa+/fvD7gbHJAwtVot8vPzfVrNYGdk3clgLRYLTQZL4QV/wpI88sgjAdUpFouxaNGioCZCA57/3bZtm09PIIVCEdD4kiaDpYQTYyXITUlJCchi3nPPPTh48GBQbQlYmBqNBqtWrfJqmgOZkaXJYCnhyGhhSZKSkvxeLomLi8OxY8eCnskNasX09ddf92o1XS6XXw2pqKhAYWEhzGYzDSNJCStaWlpgNpt9Oh/4EwharVZjx44dIa1OBCVMmUyGoqIij4kgh8Mx6pSyO4wkTQZLCWdGS5A7Vhws97hy9erVIbXBb8+fkTgcDsyePRsXLly447iv6lpbW1FQUIC1a9fSiHWUiODo0aNQq9XYsmXL0LH4+Hhcu3bN6/kSiQQPP/wwjh8/HvJe5KCd/2QyGd555507Nnv6inZQW1uLgoICmEwmKkpKxOAtQe5oET1mz57NiiiBEIQJDMRDefbZZ4ca4q3/bTAYUFlZCYvFQpPBUiKOkQlyfW2YTklJwQcffMBa1I7Q3OUxMBHk9vYZvh3LnQz2vvvuo8lgKRHLyAS53nZZpaSkoLGxkdVQrSELUyaT4dChQ3eY+OHJYGkYSUqkMzxB7vAJT3dAALZFCbAgTGDAIygtLQ0zZsygyWApUYk7Qa5UKoVQKERsbCy0Wi0nogQCEKZlrQACwYgyvxxuX50lS5bAarWitbUVf//736kXDyXqKC0txX/+8x/I5XLk5OTg9OnTnGUaCGi5pK18PmZ8/grIniwAFqwVZGPvmjqQPVk4cOAAtmzZgsuXL7PeSK1Wy8oXkJ6ePvR3RUVFUHFGKRSRSIS+vj5OrxGAMNtQPn8GPn+FYM9AfGVY1gqQjbpBoVIo0U9HRwc6OjrueMgDACxrIcjeO/TvmjqCPViLtdiDYOTh/xiz7SgOnVqD3MGLtJXPR/beNJRtpqKk3D0kJSWNEGUbyucLIMgG6ggBGSy5tQIIslvw4IzgruO3MNuOHsIp7EX24PhyFSpByElspP4ClLuWgV7kJpThAtmD4SYqa08d1mAW7g9SH34Ksw1HD53CmrrBJ8KFMmDTDMwvpxM8lLuXtvJV2HQqDWWVG+Gpvxl4sCwXwfYn/RtjtpVj/ozP8cqwp4JlrQDZe9egbsSTgkK5Oxi0lrO4mWPxy2K2HT2EU2uGq78N51sApD2IILvQFEqEcwGfnwLSgh1EjoEfcTwGu7Gv3JalZe0MbDoFrKnzZsIplLuAtvPg0sl0TIspEAyIcG/2bceC7L1pKLtAgpoGplCigun3YxaAU59f8Pqypfy2800wBL0fk0K56xlcu0wru4CTQ8sTnuv9wUCFSaGEQls55s/YhFNDB9JQdiH0ZUQqTAolDGFldwmFQmEXKkwKJQz5/6GFNae7ZI7lAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 导入模块"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
-   ],
-   "outputs": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 验证题目格式"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "is_sif(item['stem'])"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
-    "is_sif(text)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
-    "to_sif(text)\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 题目切分及令牌化\r\n",
-    "\r\n",
-    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 题目切分"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 基本切分\r\n",
-    "分离文本、公式、图片和特殊符号。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\r\n",
-    "segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 文本部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.text_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
-       " '的斜边',\n",
-       " ', 直角边',\n",
-       " ', ',\n",
-       " '.',\n",
-       " '的三边所围成的区域记为',\n",
-       " ',黑色部分记为',\n",
-       " ', 其余部分记为',\n",
-       " '.在整个图形中随机取一点，此点取自',\n",
-       " '的概率分别记为',\n",
-       " ',则']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 公式部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.formula_segments\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I,II,III',\n",
-       " 'p_1,p_2,p_3']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 图片部分"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.figure_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[\\FigureID{1}]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.figure_segments[0].figure"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 特殊符号"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "segments.ques_mark_segments"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['\\\\SIFChoice']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 标记化切分  \r\n",
-    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    " - symbol:\n",
-    "    - \"t\": text\n",
-    "    - \"f\": formula\n",
-    "    - \"g\": figure\n",
-    "    - \"m\": question mark"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 令牌化\r\n",
-    "\r\n",
-    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
-    "\r\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
-   ],
-   "outputs": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 文本解析结果"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokens.text_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图',\n",
-       " '古希腊',\n",
-       " '数学家',\n",
-       " '希波',\n",
-       " '克拉底',\n",
-       " '研究',\n",
-       " '几何图形',\n",
-       " '此图',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '直径',\n",
-       " '直角三角形',\n",
-       " '斜边',\n",
-       " '直角',\n",
-       " '三边',\n",
-       " '围成',\n",
-       " '区域',\n",
-       " '记',\n",
-       " '黑色',\n",
-       " '记',\n",
-       " '其余部分',\n",
-       " '记',\n",
-       " '图形',\n",
-       " '中',\n",
-       " '随机',\n",
-       " '取',\n",
-       " '一点',\n",
-       " '此点',\n",
-       " '取自',\n",
-       " '概率',\n",
-       " '记']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 公式解析结果"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokens.formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 自定义参数，得到定制化解析结果"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\": {\r\n",
-    "            \"method\": \"linear\",\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['ABC',\n",
-       " 'BC',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'ABC',\n",
-       " 'I',\n",
-       " 'II',\n",
-       " 'III',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
-    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
-    "> 因此，ast 可以看做是公式的语法结构表征。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\":{\r\n",
-    "            \"method\": \"ast\",\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[<Formula: ABC>,\n",
-       " <Formula: BC>,\n",
-       " <Formula: AB>,\n",
-       " <Formula: AC>,\n",
-       " <Formula: \\bigtriangleup ABC>,\n",
-       " <Formula: I>,\n",
-       " <Formula: II>,\n",
-       " <Formula: III>,\n",
-       " <Formula: I,II,III>,\n",
-       " <Formula: p_1,p_2,p_3>]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 语法树展示："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "f = sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\":{\r\n",
-    "            \"method\": \"ast\",\r\n",
-    "            \"return_type\": \"ast\",\r\n",
-    "            \"ord2token\": True,\r\n",
-    "            \"var_numbering\": True,\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens\r\n",
-    "f\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "for i in range(0, len(f)):\r\n",
-    "    ForestPlotter().export(\r\n",
-    "        f[i], root_list=[node for node in f[i]],\r\n",
-    "    )\r\n",
-    "# plt.show()\r\n"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
-    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\":{\r\n",
-    "            \"method\": \"ast\",\r\n",
-    "            \"return_type\": \"list\",\r\n",
-    "            \"ord2token\": True,\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord',\n",
-       " 'textord',\n",
-       " '\\\\supsub']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\r\n",
-    "    item[\"stem\"],\r\n",
-    "    figures=figures,\r\n",
-    "    tokenization=True,\r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\":{\r\n",
-    "            \"method\": \"ast\",\r\n",
-    "            \"ord2token\": True,\r\n",
-    "            \"return_type\": \"list\",\r\n",
-    "            \"var_numbering\": True\r\n",
-    "        }\r\n",
-    "    }\r\n",
-    ").formula_tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_0',\n",
-       " 'mathord_2',\n",
-       " '\\\\bigtriangleup',\n",
-       " 'mathord_0',\n",
-       " 'mathord_1',\n",
-       " 'mathord_2',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " ',',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " ',',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_3',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub',\n",
-       " ',',\n",
-       " 'mathord_4',\n",
-       " 'textord',\n",
-       " '\\\\supsub']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 综合训练\r\n",
-    "\r\n",
-    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\r\n",
-    "        symbol=\"fgm\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## tokenizer\r\n",
-    "\r\n",
-    "### 概述\r\n",
-    "\r\n",
-    "tokenizer为一个封装好的令牌化容器，让用户可以渐变的将item转换为令牌化列表，下面将介绍两种令牌化容器。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### TextTokenizer and PureTextTokenizer\r\n",
-    "\r\n",
-    "- ‘text’ Tokenizer ignores and skips the FormulaFigures and tokenize latex Formulas as Text\r\n",
-    "- ‘pure_text’ Tokenizer symbolizes the FormulaFigures as [FUMULA] and tokenize latex Formulas as Text"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### TextTokenizer"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "items = [{\r\n",
-    "        \"stem\": \"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\",\r\n",
-    "        \"options\": [\"1\", \"2\"]\r\n",
-    "        }]\r\n",
-    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
-    "tokens = tokenizer(items, key=lambda x: x[\"stem\"])\r\n",
-    "print(next(tokens))"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "['已知', '集合', 'A', '=', '\\\\left', '\\\\{', 'x', '\\\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<', '0', '\\\\right', '\\\\}', ',', '\\\\quad', 'B', '=', '\\\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\\\}', ',', '\\\\quad', 'A', '\\\\cap', 'B', '=']\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "items = [\"有公式$\\\\FormFigureID{wrong1?}$，如图$\\\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式$\\\\FormFigureBase64{wrong2?}$,$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$\"]"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "\r\n",
-    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
-    "tokens = [t for t in tokenizer(items)]\r\n",
-    "tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[['公式',\n",
-       "  '[FORMULA]',\n",
-       "  '如图',\n",
-       "  '[FIGURE]',\n",
-       "  'x',\n",
-       "  ',',\n",
-       "  'y',\n",
-       "  '约束条件',\n",
-       "  '公式',\n",
-       "  '[FORMULA]',\n",
-       "  '[SEP]',\n",
-       "  'z',\n",
-       "  '=',\n",
-       "  'x',\n",
-       "  '+',\n",
-       "  '7',\n",
-       "  'y',\n",
-       "  '最大值',\n",
-       "  '[MARK]']]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### PureTextTokenizer"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokenizer = get_tokenizer(\"pure_text\") # tokenizer = PureTextTokenizer()\r\n",
-    "tokens = [t for t in tokenizer(items)]\r\n",
-    "tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "[['公式',\n",
-       "  '如图',\n",
-       "  '[FIGURE]',\n",
-       "  'x',\n",
-       "  ',',\n",
-       "  'y',\n",
-       "  '约束条件',\n",
-       "  '公式',\n",
-       "  '[SEP]',\n",
-       "  'z',\n",
-       "  '=',\n",
-       "  'x',\n",
-       "  '+',\n",
-       "  '7',\n",
-       "  'y',\n",
-       "  '最大值',\n",
-       "  '[MARK]']]"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
   }
  ],
  "metadata": {

From fb9359d24f986efbc4e4cd968b14da3a0de1e071 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 00:34:59 +0800
Subject: [PATCH 110/149] update doc

---
 examples/Tokenizer/t_tokenize.ipynb | 464 ----------------------------
 1 file changed, 464 deletions(-)

diff --git a/examples/Tokenizer/t_tokenize.ipynb b/examples/Tokenizer/t_tokenize.ipynb
index a3638389..59e617ff 100644
--- a/examples/Tokenizer/t_tokenize.ipynb
+++ b/examples/Tokenizer/t_tokenize.ipynb
@@ -10,470 +10,6 @@
     "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
    ],
    "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## tokenize\r\n",
-    "\r\n",
-    "### 概述\r\n",
-    "\r\n",
-    "此模块为令牌化的基础实现模块，仅需将分词后的item列表传入此函数中即可得到相应的令牌化结果。\r\n",
-    "\r\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\r\n",
-    "\r\n",
-    "#### 文本解析\r\n",
-    "\r\n",
-    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\r\n",
-    "\r\n",
-    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \r\n",
-    "  \r\n",
-    "\r\n",
-    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\r\n",
-    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\r\n",
-    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\r\n",
-    "\r\n",
-    "#### 公式解析\r\n",
-    "\r\n",
-    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \r\n",
-    "  "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 文本解析"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 句解析\r\n",
-    "\r\n",
-    "待实现..."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 词解析\r\n",
-    "\r\n",
-    "词解析分为两个主要步骤：  \r\n",
-    "\r\n",
-    "（1） 分词：  \r\n",
-    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \r\n",
-    "    本项目目前支持的分词工具有：`jieba`   \r\n",
-    "- 单字解析：按字符划分。\r\n",
-    "    \r\n",
-    "      \r\n",
-    "（2） 筛选：过滤指定的停用词。   \r\n",
-    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \r\n",
-    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\r\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 导入模块\r\n",
-    "from EduNLP.SIF.tokenization.text import tokenize "
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输入\r\n",
-    "text = \"三角函数是基本初等函数之一\""
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 词组解析\r\n",
-    "\r\n",
-    "分词粒度参数选择 word： `granularity = \"word\"` "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
-    "tokenize(text, granularity=\"word\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['三角函数', '初等', '函数']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 单字解析\r\n",
-    "\r\n",
-    "分词粒度参数选择 word： `granularity = \"char\"` "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
-    "tokenize(text, granularity=\"char\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['三', '角', '函', '数', '基', '初', '函', '数']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 停用词表"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 获取自己的停用词表\r\n",
-    "spath = \"test_stopwords.txt\"\r\n",
-    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\r\n",
-    "stopwords = get_stopwords(spath)\r\n",
-    "stopwords"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出：传入停用词表（stopwords）\r\n",
-    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['三角函数', '是', '基本', '初等', '函数', '之一']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 公式解析\r\n",
-    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 导入模块\r\n",
-    "from EduNLP.SIF.tokenization.formula import tokenize"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 输入"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 输出"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokenize(formula, method=\"linear\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
-    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
-    "> 因此，ast 可以看做是公式的语法结构表征。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
-    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\r\n",
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['mathord',\n",
-       " '{ }',\n",
-       " 'mathord',\n",
-       " '+',\n",
-       " 'mathord',\n",
-       " '{ }',\n",
-       " '\\\\frac',\n",
-       " '+',\n",
-       " 'textord',\n",
-       " '=',\n",
-       " 'mathord']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\r\n",
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['mathord_con',\n",
-       " '{ }',\n",
-       " 'mathord_0',\n",
-       " '+',\n",
-       " 'mathord_1',\n",
-       " '{ }',\n",
-       " '\\\\frac',\n",
-       " '+',\n",
-       " 'textord',\n",
-       " '=',\n",
-       " 'mathord_0']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 综合解析\r\n",
-    "\r\n",
-    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\r\n",
-    "```python\r\n",
-    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\r\n",
-    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\r\n",
-    "```\r\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 导入模块\r\n",
-    "from EduNLP.Tokenizer import get_tokenizer\r\n",
-    "\r\n",
-    "# 输入\r\n",
-    "item = {\r\n",
-    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
-    "}\r\n",
-    "\r\n",
-    "# 输出\r\n",
-    "tokenizer = get_tokenizer(\"text\")\r\n",
-    "tokens = tokenizer(item)\r\n",
-    "next(tokens) "
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图',\n",
-       " '古希腊',\n",
-       " '数学家',\n",
-       " '希波',\n",
-       " '克拉底',\n",
-       " '研究',\n",
-       " '几何图形',\n",
-       " '此图',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '直径',\n",
-       " '直角三角形',\n",
-       " 'ABC',\n",
-       " '斜边',\n",
-       " 'BC',\n",
-       " '直角',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\x08',\n",
-       " 'igtriangleupABC',\n",
-       " '三边',\n",
-       " '围成',\n",
-       " '区域',\n",
-       " '记',\n",
-       " 'I',\n",
-       " '黑色',\n",
-       " '记',\n",
-       " 'II',\n",
-       " '其余部分',\n",
-       " '记',\n",
-       " 'III',\n",
-       " '图形',\n",
-       " '中',\n",
-       " '随机',\n",
-       " '取',\n",
-       " '一点',\n",
-       " '此点',\n",
-       " '取自',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " '概率',\n",
-       " '记',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3',\n",
-       " '[MARK]',\n",
-       " '[FIGURE]']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
   }
  ],
  "metadata": {

From 0d80987d5e6cc91a315fe5a88f94cafc46c8891a Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 00:40:10 +0800
Subject: [PATCH 111/149] update doc

---
 docs/source/tutorial/zh/index.rst   |   2 +-
 examples/Tokenizer/t_tokenize.ipynb | 169 +++++++++++++++++++++++++++-
 2 files changed, 164 insertions(+), 7 deletions(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index e5188f44..d23001c8 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -43,7 +43,7 @@
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/tokenizer/t_tokenize.ipynb>
+    令牌化  <../../build/blitz/tokenizer/t_tokenize2.ipynb>
 
 
 向量化
diff --git a/examples/Tokenizer/t_tokenize.ipynb b/examples/Tokenizer/t_tokenize.ipynb
index 59e617ff..8dcec093 100644
--- a/examples/Tokenizer/t_tokenize.ipynb
+++ b/examples/Tokenizer/t_tokenize.ipynb
@@ -1,23 +1,180 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "markdown",
    "source": [
-    "# 令牌化\r\n",
+    "# TextTokenizer and PureTextTokenizer\r\n",
     "\r\n",
-    "## 概述\r\n",
+    "- ‘text’ Tokenizer ignores and skips the FormulaFigures and tokenize latex Formulas as Text\r\n",
+    "- ‘pure_text’ Tokenizer symbolizes the FormulaFigures as [FUMULA] and tokenize latex Formulas as Text"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## TextTokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "items = [{\r\n",
+    "        \"stem\": \"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\",\r\n",
+    "        \"options\": [\"1\", \"2\"]\r\n",
+    "        }]\r\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
+    "tokens = tokenizer(items, key=lambda x: x[\"stem\"])\r\n",
+    "print(next(tokens))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['已知', '集合', 'A', '=', '\\\\left', '\\\\{', 'x', '\\\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<', '0', '\\\\right', '\\\\}', ',', '\\\\quad', 'B', '=', '\\\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\\\}', ',', '\\\\quad', 'A', '\\\\cap', 'B', '=']\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "items = [\"有公式$\\\\FormFigureID{wrong1?}$，如图$\\\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式$\\\\FormFigureBase64{wrong2?}$,$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$\"]"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
     "\r\n",
-    "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
+    "tokens = [t for t in tokenizer(items)]\r\n",
+    "tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['公式',\n",
+       "  '[FORMULA]',\n",
+       "  '如图',\n",
+       "  '[FIGURE]',\n",
+       "  'x',\n",
+       "  ',',\n",
+       "  'y',\n",
+       "  '约束条件',\n",
+       "  '公式',\n",
+       "  '[FORMULA]',\n",
+       "  '[SEP]',\n",
+       "  'z',\n",
+       "  '=',\n",
+       "  'x',\n",
+       "  '+',\n",
+       "  '7',\n",
+       "  'y',\n",
+       "  '最大值',\n",
+       "  '[MARK]']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## PureTextTokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "tokenizer = get_tokenizer(\"pure_text\") # tokenizer = PureTextTokenizer()\r\n",
+    "tokens = [t for t in tokenizer(items)]\r\n",
+    "tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[['公式',\n",
+       "  '如图',\n",
+       "  '[FIGURE]',\n",
+       "  'x',\n",
+       "  ',',\n",
+       "  'y',\n",
+       "  '约束条件',\n",
+       "  '公式',\n",
+       "  '[SEP]',\n",
+       "  'z',\n",
+       "  '=',\n",
+       "  'x',\n",
+       "  '+',\n",
+       "  '7',\n",
+       "  'y',\n",
+       "  '最大值',\n",
+       "  '[MARK]']]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
    ],
    "metadata": {}
   }
  ],
  "metadata": {
-  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.6.13 64-bit ('data': conda)"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  },
+  "interpreter": {
+   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 5
 }
\ No newline at end of file

From a487d58873a65ae9e899b9cb7c372c1515734444 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 00:45:01 +0800
Subject: [PATCH 112/149] update doc

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index d23001c8..e5188f44 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -43,7 +43,7 @@
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/tokenizer/t_tokenize2.ipynb>
+    令牌化  <../../build/blitz/tokenizer/t_tokenize.ipynb>
 
 
 向量化

From d72bf5ff64c3942b7f9efa7a3897bc9d92707a45 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 00:49:20 +0800
Subject: [PATCH 113/149] update doc

---
 docs/source/tutorial/zh/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index e5188f44..aee3bb74 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -55,3 +55,4 @@
     :glob:
     
     向量化  <../../build/blitz/vectorization/total_vector.ipynb>
+    令牌化  <../../build/blitz/tokenizer/t_tokenize.ipynb>

From 17f1b934ab3516e6581eb36e24ee5ee58b1f5677 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 00:57:09 +0800
Subject: [PATCH 114/149] update doc

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index aee3bb74..c40a603d 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -55,4 +55,4 @@
     :glob:
     
     向量化  <../../build/blitz/vectorization/total_vector.ipynb>
-    令牌化  <../../build/blitz/tokenizer/t_tokenize.ipynb>
+    令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>

From 730e520e29f27595df015c958419db48fd7fc667 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 01:03:29 +0800
Subject: [PATCH 115/149] update doc

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index c40a603d..e2c19611 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -55,4 +55,4 @@
     :glob:
     
     向量化  <../../build/blitz/vectorization/total_vector.ipynb>
-    令牌化  <../../build/blitz/tokenizer/total_tokenize.ipynb>
+    令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>

From bbebaa1e1403955b6403627377583d2c864ec456 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 01:09:29 +0800
Subject: [PATCH 116/149] update doc

---
 docs/source/tutorial/zh/index.rst     | 2 +-
 examples/Tokenizer/new_tokenize.ipynb | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 examples/Tokenizer/new_tokenize.ipynb

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index e2c19611..7c1d83e4 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -55,4 +55,4 @@
     :glob:
     
     向量化  <../../build/blitz/vectorization/total_vector.ipynb>
-    令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>
+    令牌化  <../../build/blitz/tokenizer/new_tokenize.ipynb>
diff --git a/examples/Tokenizer/new_tokenize.ipynb b/examples/Tokenizer/new_tokenize.ipynb
new file mode 100644
index 00000000..e69de29b

From 41a99f7cf8a0083ceb44e5a166a943d96a9d48c8 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 01:17:18 +0800
Subject: [PATCH 117/149] update doc

---
 examples/Tokenizer/new_tokenize.ipynb | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/examples/Tokenizer/new_tokenize.ipynb b/examples/Tokenizer/new_tokenize.ipynb
index e69de29b..59e617ff 100644
--- a/examples/Tokenizer/new_tokenize.ipynb
+++ b/examples/Tokenizer/new_tokenize.ipynb
@@ -0,0 +1,23 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 令牌化\r\n",
+    "\r\n",
+    "## 概述\r\n",
+    "\r\n",
+    "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file

From 7cab7d4a6d84ab3c912a20eff50c283d0a155e74 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 01:21:00 +0800
Subject: [PATCH 118/149] update doc

---
 examples/Tokenizer/new_tokenize.ipynb | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/examples/Tokenizer/new_tokenize.ipynb b/examples/Tokenizer/new_tokenize.ipynb
index 59e617ff..ef633d0e 100644
--- a/examples/Tokenizer/new_tokenize.ipynb
+++ b/examples/Tokenizer/new_tokenize.ipynb
@@ -10,6 +10,24 @@
     "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
    ],
    "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {

From 8fc156e75b5250d746fa76550b5491a60cd3e402 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 01:24:53 +0800
Subject: [PATCH 119/149] update doc

---
 examples/Tokenizer/new_tokenize.ipynb | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/examples/Tokenizer/new_tokenize.ipynb b/examples/Tokenizer/new_tokenize.ipynb
index ef633d0e..c7edb2d3 100644
--- a/examples/Tokenizer/new_tokenize.ipynb
+++ b/examples/Tokenizer/new_tokenize.ipynb
@@ -1,16 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 令牌化\r\n",
-    "\r\n",
-    "## 概述\r\n",
-    "\r\n",
-    "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From 1d2b90d6a6b04717d34511ba7cfb58fd9085ece7 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 01:28:35 +0800
Subject: [PATCH 120/149] update doc

---
 docs/source/tutorial/zh/index.rst                          | 2 +-
 examples/Tokenizer/{new_tokenize.ipynb => new_token.ipynb} | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename examples/Tokenizer/{new_tokenize.ipynb => new_token.ipynb} (100%)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 7c1d83e4..6765481f 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -55,4 +55,4 @@
     :glob:
     
     向量化  <../../build/blitz/vectorization/total_vector.ipynb>
-    令牌化  <../../build/blitz/tokenizer/new_tokenize.ipynb>
+    令牌化  <../../build/blitz/tokenizer/new_token.ipynb>
diff --git a/examples/Tokenizer/new_tokenize.ipynb b/examples/Tokenizer/new_token.ipynb
similarity index 100%
rename from examples/Tokenizer/new_tokenize.ipynb
rename to examples/Tokenizer/new_token.ipynb

From bb9a8c33a7bd465955467aed7bb3e133fd7c80df Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 09:34:45 +0800
Subject: [PATCH 121/149] update doc

---
 docs/source/tutorial/zh/index.rst   |   2 +-
 examples/Tokenizer/tokenizer2.ipynb | 501 ++++++++++++++++++++++++++++
 2 files changed, 502 insertions(+), 1 deletion(-)
 create mode 100644 examples/Tokenizer/tokenizer2.ipynb

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 6765481f..2a05d035 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -55,4 +55,4 @@
     :glob:
     
     向量化  <../../build/blitz/vectorization/total_vector.ipynb>
-    令牌化  <../../build/blitz/tokenizer/new_token.ipynb>
+    令牌化  <../../build/blitz/tokenizer/tokenizer2.ipynb>
diff --git a/examples/Tokenizer/tokenizer2.ipynb b/examples/Tokenizer/tokenizer2.ipynb
new file mode 100644
index 00000000..4819b00d
--- /dev/null
+++ b/examples/Tokenizer/tokenizer2.ipynb
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Tokenizer\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
+    "\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\n",
+    "\n",
+    "### 文本解析\n",
+    "\n",
+    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\n",
+    "\n",
+    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
+    "  \n",
+    "\n",
+    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\n",
+    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\n",
+    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\n",
+    "\n",
+    "### 公式解析\n",
+    "\n",
+    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \n",
+    "  "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 文本解析"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 句解析\n",
+    "\n",
+    "待实现..."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 词解析\n",
+    "\n",
+    "词解析分为两个主要步骤：  \n",
+    "\n",
+    "（1） 分词：  \n",
+    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \n",
+    "    本项目目前支持的分词工具有：`jieba`   \n",
+    "- 单字解析：按字符划分。\n",
+    "    \n",
+    "      \n",
+    "（2） 筛选：过滤指定的停用词。   \n",
+    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n",
+    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "# 导入模块\n",
+    "from EduNLP.SIF.tokenization.text import tokenize "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "# 输入\n",
+    "text = \"三角函数是基本初等函数之一\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 词组解析\n",
+    "\n",
+    "分词粒度参数选择 word： `granularity = \"word\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
+    "tokenize(text, granularity=\"word\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三角函数', '初等', '函数']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 单字解析\n",
+    "\n",
+    "分词粒度参数选择 word： `granularity = \"char\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
+    "tokenize(text, granularity=\"char\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三', '角', '函', '数', '基', '初', '函', '数']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 停用词表"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# 获取自己的停用词表\n",
+    "spath = \"test_stopwords.txt\"\n",
+    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\n",
+    "stopwords = get_stopwords(spath)\n",
+    "stopwords"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# 输出：传入停用词表（stopwords）\n",
+    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三角函数', '是', '基本', '初等', '函数', '之一']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 公式解析\n",
+    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# 导入模块\n",
+    "from EduNLP.SIF.tokenization.formula import tokenize"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输入"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "tokenize(formula, method=\"linear\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " '{ }',\n",
+       " 'mathord',\n",
+       " '+',\n",
+       " 'mathord',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord_con',\n",
+       " '{ }',\n",
+       " 'mathord_0',\n",
+       " '+',\n",
+       " 'mathord_1',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord_0']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 综合解析\n",
+    "\n",
+    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\n",
+    "```python\n",
+    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\n",
+    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\n",
+    "```\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "source": [
+    "# 导入模块\n",
+    "from EduNLP.Tokenizer import get_tokenizer\n",
+    "\n",
+    "# 输入\n",
+    "item = {\n",
+    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
+    "}\n",
+    "\n",
+    "# 输出\n",
+    "tokenizer = get_tokenizer(\"text\")\n",
+    "tokens = tokenizer(item)\n",
+    "next(tokens) "
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " 'ABC',\n",
+       " '斜边',\n",
+       " 'BC',\n",
+       " '直角',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\x08',\n",
+       " 'igtriangleupABC',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " 'I',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " 'II',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " 'III',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " '概率',\n",
+       " '记',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3',\n",
+       " '[MARK]',\n",
+       " '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file

From 6a011062493a34bcc20f22cb9fc8dab9880bbe2e Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 19 Sep 2021 09:38:48 +0800
Subject: [PATCH 122/149] update doc

---
 docs/source/tutorial/zh/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 2a05d035..e2c19611 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -55,4 +55,4 @@
     :glob:
     
     向量化  <../../build/blitz/vectorization/total_vector.ipynb>
-    令牌化  <../../build/blitz/tokenizer/tokenizer2.ipynb>
+    令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>

From f786dbbc630201e66c2d932425636e9ce205bd62 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 20 Sep 2021 22:11:02 +0800
Subject: [PATCH 123/149] Delete examples/Tokenizer directory

---
 examples/Tokenizer/new_token.ipynb      |  30 --
 examples/Tokenizer/t_tokenize.ipynb     | 180 ---------
 examples/Tokenizer/test_stopwords.txt   |   9 -
 examples/Tokenizer/tokenizer.ipynb      | 501 ------------------------
 examples/Tokenizer/tokenizer2.ipynb     | 501 ------------------------
 examples/Tokenizer/total_tokenize.ipynb | 487 -----------------------
 6 files changed, 1708 deletions(-)
 delete mode 100644 examples/Tokenizer/new_token.ipynb
 delete mode 100644 examples/Tokenizer/t_tokenize.ipynb
 delete mode 100644 examples/Tokenizer/test_stopwords.txt
 delete mode 100644 examples/Tokenizer/tokenizer.ipynb
 delete mode 100644 examples/Tokenizer/tokenizer2.ipynb
 delete mode 100644 examples/Tokenizer/total_tokenize.ipynb

diff --git a/examples/Tokenizer/new_token.ipynb b/examples/Tokenizer/new_token.ipynb
deleted file mode 100644
index c7edb2d3..00000000
--- a/examples/Tokenizer/new_token.ipynb
+++ /dev/null
@@ -1,30 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/examples/Tokenizer/t_tokenize.ipynb b/examples/Tokenizer/t_tokenize.ipynb
deleted file mode 100644
index 8dcec093..00000000
--- a/examples/Tokenizer/t_tokenize.ipynb
+++ /dev/null
@@ -1,180 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "source": [
-    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# TextTokenizer and PureTextTokenizer\r\n",
-    "\r\n",
-    "- ‘text’ Tokenizer ignores and skips the FormulaFigures and tokenize latex Formulas as Text\r\n",
-    "- ‘pure_text’ Tokenizer symbolizes the FormulaFigures as [FUMULA] and tokenize latex Formulas as Text"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## TextTokenizer"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "source": [
-    "items = [{\r\n",
-    "        \"stem\": \"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\",\r\n",
-    "        \"options\": [\"1\", \"2\"]\r\n",
-    "        }]\r\n",
-    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
-    "tokens = tokenizer(items, key=lambda x: x[\"stem\"])\r\n",
-    "print(next(tokens))"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "['已知', '集合', 'A', '=', '\\\\left', '\\\\{', 'x', '\\\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<', '0', '\\\\right', '\\\\}', ',', '\\\\quad', 'B', '=', '\\\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\\\}', ',', '\\\\quad', 'A', '\\\\cap', 'B', '=']\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "source": [
-    "items = [\"有公式$\\\\FormFigureID{wrong1?}$，如图$\\\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式$\\\\FormFigureBase64{wrong2?}$,$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$\"]"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "\r\n",
-    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
-    "tokens = [t for t in tokenizer(items)]\r\n",
-    "tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['公式',\n",
-       "  '[FORMULA]',\n",
-       "  '如图',\n",
-       "  '[FIGURE]',\n",
-       "  'x',\n",
-       "  ',',\n",
-       "  'y',\n",
-       "  '约束条件',\n",
-       "  '公式',\n",
-       "  '[FORMULA]',\n",
-       "  '[SEP]',\n",
-       "  'z',\n",
-       "  '=',\n",
-       "  'x',\n",
-       "  '+',\n",
-       "  '7',\n",
-       "  'y',\n",
-       "  '最大值',\n",
-       "  '[MARK]']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 4
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## PureTextTokenizer"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "tokenizer = get_tokenizer(\"pure_text\") # tokenizer = PureTextTokenizer()\r\n",
-    "tokens = [t for t in tokenizer(items)]\r\n",
-    "tokens"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[['公式',\n",
-       "  '如图',\n",
-       "  '[FIGURE]',\n",
-       "  'x',\n",
-       "  ',',\n",
-       "  'y',\n",
-       "  '约束条件',\n",
-       "  '公式',\n",
-       "  '[SEP]',\n",
-       "  'z',\n",
-       "  '=',\n",
-       "  'x',\n",
-       "  '+',\n",
-       "  '7',\n",
-       "  'y',\n",
-       "  '最大值',\n",
-       "  '[MARK]']]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 5
-    }
-   ],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.6.13 64-bit ('data': conda)"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.13"
-  },
-  "interpreter": {
-   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/examples/Tokenizer/test_stopwords.txt b/examples/Tokenizer/test_stopwords.txt
deleted file mode 100644
index 8183ecf4..00000000
--- a/examples/Tokenizer/test_stopwords.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-一旦
-一时
-一来
-一样
-一次
-一片
-一番
-一直
-一致
\ No newline at end of file
diff --git a/examples/Tokenizer/tokenizer.ipynb b/examples/Tokenizer/tokenizer.ipynb
deleted file mode 100644
index 4819b00d..00000000
--- a/examples/Tokenizer/tokenizer.ipynb
+++ /dev/null
@@ -1,501 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Tokenizer\n",
-    "\n",
-    "## 概述\n",
-    "\n",
-    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
-    "\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\n",
-    "\n",
-    "### 文本解析\n",
-    "\n",
-    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\n",
-    "\n",
-    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
-    "  \n",
-    "\n",
-    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\n",
-    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\n",
-    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\n",
-    "\n",
-    "### 公式解析\n",
-    "\n",
-    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \n",
-    "  "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 文本解析"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 句解析\n",
-    "\n",
-    "待实现..."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 词解析\n",
-    "\n",
-    "词解析分为两个主要步骤：  \n",
-    "\n",
-    "（1） 分词：  \n",
-    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \n",
-    "    本项目目前支持的分词工具有：`jieba`   \n",
-    "- 单字解析：按字符划分。\n",
-    "    \n",
-    "      \n",
-    "（2） 筛选：过滤指定的停用词。   \n",
-    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n",
-    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "source": [
-    "# 导入模块\n",
-    "from EduNLP.SIF.tokenization.text import tokenize "
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "source": [
-    "# 输入\n",
-    "text = \"三角函数是基本初等函数之一\""
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 词组解析\n",
-    "\n",
-    "分词粒度参数选择 word： `granularity = \"word\"` "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
-    "tokenize(text, granularity=\"word\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['三角函数', '初等', '函数']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 3
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 单字解析\n",
-    "\n",
-    "分词粒度参数选择 word： `granularity = \"char\"` "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
-    "tokenize(text, granularity=\"char\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['三', '角', '函', '数', '基', '初', '函', '数']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 4
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 停用词表"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "# 获取自己的停用词表\n",
-    "spath = \"test_stopwords.txt\"\n",
-    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\n",
-    "stopwords = get_stopwords(spath)\n",
-    "stopwords"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 5
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "source": [
-    "# 输出：传入停用词表（stopwords）\n",
-    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['三角函数', '是', '基本', '初等', '函数', '之一']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 6
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 公式解析\n",
-    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "source": [
-    "# 导入模块\n",
-    "from EduNLP.SIF.tokenization.formula import tokenize"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 输入"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "source": [
-    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 输出"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "tokenize(formula, method=\"linear\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 9
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
-    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
-    "> 因此，ast 可以看做是公式的语法结构表征。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "source": [
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 10
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
-    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\n",
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['mathord',\n",
-       " '{ }',\n",
-       " 'mathord',\n",
-       " '+',\n",
-       " 'mathord',\n",
-       " '{ }',\n",
-       " '\\\\frac',\n",
-       " '+',\n",
-       " 'textord',\n",
-       " '=',\n",
-       " 'mathord']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 11
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\n",
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['mathord_con',\n",
-       " '{ }',\n",
-       " 'mathord_0',\n",
-       " '+',\n",
-       " 'mathord_1',\n",
-       " '{ }',\n",
-       " '\\\\frac',\n",
-       " '+',\n",
-       " 'textord',\n",
-       " '=',\n",
-       " 'mathord_0']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 12
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 综合解析\n",
-    "\n",
-    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\n",
-    "```python\n",
-    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\n",
-    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\n",
-    "```\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "source": [
-    "# 导入模块\n",
-    "from EduNLP.Tokenizer import get_tokenizer\n",
-    "\n",
-    "# 输入\n",
-    "item = {\n",
-    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
-    "}\n",
-    "\n",
-    "# 输出\n",
-    "tokenizer = get_tokenizer(\"text\")\n",
-    "tokens = tokenizer(item)\n",
-    "next(tokens) "
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图',\n",
-       " '古希腊',\n",
-       " '数学家',\n",
-       " '希波',\n",
-       " '克拉底',\n",
-       " '研究',\n",
-       " '几何图形',\n",
-       " '此图',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '直径',\n",
-       " '直角三角形',\n",
-       " 'ABC',\n",
-       " '斜边',\n",
-       " 'BC',\n",
-       " '直角',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\x08',\n",
-       " 'igtriangleupABC',\n",
-       " '三边',\n",
-       " '围成',\n",
-       " '区域',\n",
-       " '记',\n",
-       " 'I',\n",
-       " '黑色',\n",
-       " '记',\n",
-       " 'II',\n",
-       " '其余部分',\n",
-       " '记',\n",
-       " 'III',\n",
-       " '图形',\n",
-       " '中',\n",
-       " '随机',\n",
-       " '取',\n",
-       " '一点',\n",
-       " '此点',\n",
-       " '取自',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " '概率',\n",
-       " '记',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3',\n",
-       " '[MARK]',\n",
-       " '[FIGURE]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 39
-    }
-   ],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.5",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.5 64-bit"
-  },
-  "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/examples/Tokenizer/tokenizer2.ipynb b/examples/Tokenizer/tokenizer2.ipynb
deleted file mode 100644
index 4819b00d..00000000
--- a/examples/Tokenizer/tokenizer2.ipynb
+++ /dev/null
@@ -1,501 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Tokenizer\n",
-    "\n",
-    "## 概述\n",
-    "\n",
-    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
-    "\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\n",
-    "\n",
-    "### 文本解析\n",
-    "\n",
-    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\n",
-    "\n",
-    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
-    "  \n",
-    "\n",
-    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\n",
-    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\n",
-    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\n",
-    "\n",
-    "### 公式解析\n",
-    "\n",
-    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \n",
-    "  "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 文本解析"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 句解析\n",
-    "\n",
-    "待实现..."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 词解析\n",
-    "\n",
-    "词解析分为两个主要步骤：  \n",
-    "\n",
-    "（1） 分词：  \n",
-    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \n",
-    "    本项目目前支持的分词工具有：`jieba`   \n",
-    "- 单字解析：按字符划分。\n",
-    "    \n",
-    "      \n",
-    "（2） 筛选：过滤指定的停用词。   \n",
-    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n",
-    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "source": [
-    "# 导入模块\n",
-    "from EduNLP.SIF.tokenization.text import tokenize "
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "source": [
-    "# 输入\n",
-    "text = \"三角函数是基本初等函数之一\""
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 词组解析\n",
-    "\n",
-    "分词粒度参数选择 word： `granularity = \"word\"` "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
-    "tokenize(text, granularity=\"word\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['三角函数', '初等', '函数']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 3
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 单字解析\n",
-    "\n",
-    "分词粒度参数选择 word： `granularity = \"char\"` "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
-    "tokenize(text, granularity=\"char\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['三', '角', '函', '数', '基', '初', '函', '数']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 4
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 停用词表"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "# 获取自己的停用词表\n",
-    "spath = \"test_stopwords.txt\"\n",
-    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\n",
-    "stopwords = get_stopwords(spath)\n",
-    "stopwords"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 5
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "source": [
-    "# 输出：传入停用词表（stopwords）\n",
-    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['三角函数', '是', '基本', '初等', '函数', '之一']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 6
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 公式解析\n",
-    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "source": [
-    "# 导入模块\n",
-    "from EduNLP.SIF.tokenization.formula import tokenize"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 输入"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "source": [
-    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 输出"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "tokenize(formula, method=\"linear\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 9
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
-    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
-    "> 因此，ast 可以看做是公式的语法结构表征。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "source": [
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 10
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
-    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\n",
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['mathord',\n",
-       " '{ }',\n",
-       " 'mathord',\n",
-       " '+',\n",
-       " 'mathord',\n",
-       " '{ }',\n",
-       " '\\\\frac',\n",
-       " '+',\n",
-       " 'textord',\n",
-       " '=',\n",
-       " 'mathord']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 11
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\n",
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['mathord_con',\n",
-       " '{ }',\n",
-       " 'mathord_0',\n",
-       " '+',\n",
-       " 'mathord_1',\n",
-       " '{ }',\n",
-       " '\\\\frac',\n",
-       " '+',\n",
-       " 'textord',\n",
-       " '=',\n",
-       " 'mathord_0']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 12
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 综合解析\n",
-    "\n",
-    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\n",
-    "```python\n",
-    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\n",
-    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\n",
-    "```\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "source": [
-    "# 导入模块\n",
-    "from EduNLP.Tokenizer import get_tokenizer\n",
-    "\n",
-    "# 输入\n",
-    "item = {\n",
-    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
-    "}\n",
-    "\n",
-    "# 输出\n",
-    "tokenizer = get_tokenizer(\"text\")\n",
-    "tokens = tokenizer(item)\n",
-    "next(tokens) "
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['如图',\n",
-       " '古希腊',\n",
-       " '数学家',\n",
-       " '希波',\n",
-       " '克拉底',\n",
-       " '研究',\n",
-       " '几何图形',\n",
-       " '此图',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '直径',\n",
-       " '直角三角形',\n",
-       " 'ABC',\n",
-       " '斜边',\n",
-       " 'BC',\n",
-       " '直角',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\x08',\n",
-       " 'igtriangleupABC',\n",
-       " '三边',\n",
-       " '围成',\n",
-       " '区域',\n",
-       " '记',\n",
-       " 'I',\n",
-       " '黑色',\n",
-       " '记',\n",
-       " 'II',\n",
-       " '其余部分',\n",
-       " '记',\n",
-       " 'III',\n",
-       " '图形',\n",
-       " '中',\n",
-       " '随机',\n",
-       " '取',\n",
-       " '一点',\n",
-       " '此点',\n",
-       " '取自',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " '概率',\n",
-       " '记',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3',\n",
-       " '[MARK]',\n",
-       " '[FIGURE]']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 39
-    }
-   ],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.5",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.5 64-bit"
-  },
-  "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/examples/Tokenizer/total_tokenize.ipynb b/examples/Tokenizer/total_tokenize.ipynb
deleted file mode 100644
index a3638389..00000000
--- a/examples/Tokenizer/total_tokenize.ipynb
+++ /dev/null
@@ -1,487 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 令牌化\r\n",
-    "\r\n",
-    "## 概述\r\n",
-    "\r\n",
-    "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## tokenize\r\n",
-    "\r\n",
-    "### 概述\r\n",
-    "\r\n",
-    "此模块为令牌化的基础实现模块，仅需将分词后的item列表传入此函数中即可得到相应的令牌化结果。\r\n",
-    "\r\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\r\n",
-    "\r\n",
-    "#### 文本解析\r\n",
-    "\r\n",
-    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\r\n",
-    "\r\n",
-    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \r\n",
-    "  \r\n",
-    "\r\n",
-    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\r\n",
-    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\r\n",
-    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\r\n",
-    "\r\n",
-    "#### 公式解析\r\n",
-    "\r\n",
-    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \r\n",
-    "  "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 文本解析"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 句解析\r\n",
-    "\r\n",
-    "待实现..."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### 词解析\r\n",
-    "\r\n",
-    "词解析分为两个主要步骤：  \r\n",
-    "\r\n",
-    "（1） 分词：  \r\n",
-    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \r\n",
-    "    本项目目前支持的分词工具有：`jieba`   \r\n",
-    "- 单字解析：按字符划分。\r\n",
-    "    \r\n",
-    "      \r\n",
-    "（2） 筛选：过滤指定的停用词。   \r\n",
-    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \r\n",
-    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\r\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 导入模块\r\n",
-    "from EduNLP.SIF.tokenization.text import tokenize "
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输入\r\n",
-    "text = \"三角函数是基本初等函数之一\""
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 词组解析\r\n",
-    "\r\n",
-    "分词粒度参数选择 word： `granularity = \"word\"` "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
-    "tokenize(text, granularity=\"word\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['三角函数', '初等', '函数']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 单字解析\r\n",
-    "\r\n",
-    "分词粒度参数选择 word： `granularity = \"char\"` "
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
-    "tokenize(text, granularity=\"char\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['三', '角', '函', '数', '基', '初', '函', '数']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "##### 停用词表"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 获取自己的停用词表\r\n",
-    "spath = \"test_stopwords.txt\"\r\n",
-    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\r\n",
-    "stopwords = get_stopwords(spath)\r\n",
-    "stopwords"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出：传入停用词表（stopwords）\r\n",
-    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['三角函数', '是', '基本', '初等', '函数', '之一']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 公式解析\r\n",
-    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 导入模块\r\n",
-    "from EduNLP.SIF.tokenization.formula import tokenize"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 输入"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "- 输出"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokenize(formula, method=\"linear\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
-    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
-    "> 因此，ast 可以看做是公式的语法结构表征。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
-    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\r\n",
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['mathord',\n",
-       " '{ }',\n",
-       " 'mathord',\n",
-       " '+',\n",
-       " 'mathord',\n",
-       " '{ }',\n",
-       " '\\\\frac',\n",
-       " '+',\n",
-       " 'textord',\n",
-       " '=',\n",
-       " 'mathord']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\r\n",
-    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['mathord_con',\n",
-       " '{ }',\n",
-       " 'mathord_0',\n",
-       " '+',\n",
-       " 'mathord_1',\n",
-       " '{ }',\n",
-       " '\\\\frac',\n",
-       " '+',\n",
-       " 'textord',\n",
-       " '=',\n",
-       " 'mathord_0']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 综合解析\r\n",
-    "\r\n",
-    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\r\n",
-    "```python\r\n",
-    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\r\n",
-    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\r\n",
-    "```\r\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# 导入模块\r\n",
-    "from EduNLP.Tokenizer import get_tokenizer\r\n",
-    "\r\n",
-    "# 输入\r\n",
-    "item = {\r\n",
-    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
-    "}\r\n",
-    "\r\n",
-    "# 输出\r\n",
-    "tokenizer = get_tokenizer(\"text\")\r\n",
-    "tokens = tokenizer(item)\r\n",
-    "next(tokens) "
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "['如图',\n",
-       " '古希腊',\n",
-       " '数学家',\n",
-       " '希波',\n",
-       " '克拉底',\n",
-       " '研究',\n",
-       " '几何图形',\n",
-       " '此图',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '三个',\n",
-       " '半圆',\n",
-       " '直径',\n",
-       " '直角三角形',\n",
-       " 'ABC',\n",
-       " '斜边',\n",
-       " 'BC',\n",
-       " '直角',\n",
-       " 'AB',\n",
-       " 'AC',\n",
-       " '\\x08',\n",
-       " 'igtriangleupABC',\n",
-       " '三边',\n",
-       " '围成',\n",
-       " '区域',\n",
-       " '记',\n",
-       " 'I',\n",
-       " '黑色',\n",
-       " '记',\n",
-       " 'II',\n",
-       " '其余部分',\n",
-       " '记',\n",
-       " 'III',\n",
-       " '图形',\n",
-       " '中',\n",
-       " '随机',\n",
-       " '取',\n",
-       " '一点',\n",
-       " '此点',\n",
-       " '取自',\n",
-       " 'I',\n",
-       " ',',\n",
-       " 'II',\n",
-       " ',',\n",
-       " 'III',\n",
-       " '概率',\n",
-       " '记',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '1',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '2',\n",
-       " ',',\n",
-       " 'p',\n",
-       " '_',\n",
-       " '3',\n",
-       " '[MARK]',\n",
-       " '[FIGURE]']"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file

From 9a5bec13194ee11bef68258b2218d5433852395b Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 20 Sep 2021 22:39:46 +0800
Subject: [PATCH 124/149] try fix bug

---
 examples/tokenizer/tokenizer.ipynb  | 159 +++++----
 examples/tokenizer/tokenizer2.ipynb | 501 ++++++++++++++++++++++++++++
 2 files changed, 586 insertions(+), 74 deletions(-)
 create mode 100644 examples/tokenizer/tokenizer2.ipynb

diff --git a/examples/tokenizer/tokenizer.ipynb b/examples/tokenizer/tokenizer.ipynb
index 4819b00d..6f4516ec 100644
--- a/examples/tokenizer/tokenizer.ipynb
+++ b/examples/tokenizer/tokenizer.ipynb
@@ -3,28 +3,39 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# Tokenizer\n",
-    "\n",
-    "## 概述\n",
-    "\n",
-    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
-    "\n",
-    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\n",
-    "\n",
-    "### 文本解析\n",
-    "\n",
-    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\n",
-    "\n",
-    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
-    "  \n",
-    "\n",
-    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\n",
-    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\n",
-    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\n",
-    "\n",
-    "### 公式解析\n",
-    "\n",
-    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \n",
+    "# 令牌化\r\n",
+    "\r\n",
+    "## 概述\r\n",
+    "\r\n",
+    "此模块可以定制化的将文本切分为令牌（token）序列，在此，将展示三种方式来进行令牌化：调用tokenize函数、调用sif4sci函数、调用封装好的tokenizer。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Tokenizer\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
+    "\r\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\r\n",
+    "\r\n",
+    "#### 文本解析\r\n",
+    "\r\n",
+    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\r\n",
+    "\r\n",
+    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \r\n",
+    "  \r\n",
+    "\r\n",
+    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\r\n",
+    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\r\n",
+    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\r\n",
+    "\r\n",
+    "#### 公式解析\r\n",
+    "\r\n",
+    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \r\n",
     "  "
    ],
    "metadata": {}
@@ -32,15 +43,15 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 文本解析"
+    "### 文本解析"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "### 句解析\n",
-    "\n",
+    "#### 句解析\r\n",
+    "\r\n",
     "待实现..."
    ],
    "metadata": {}
@@ -48,19 +59,19 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 词解析\n",
-    "\n",
-    "词解析分为两个主要步骤：  \n",
-    "\n",
-    "（1） 分词：  \n",
-    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \n",
-    "    本项目目前支持的分词工具有：`jieba`   \n",
-    "- 单字解析：按字符划分。\n",
-    "    \n",
-    "      \n",
-    "（2） 筛选：过滤指定的停用词。   \n",
-    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n",
-    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\n"
+    "#### 词解析\r\n",
+    "\r\n",
+    "词解析分为两个主要步骤：  \r\n",
+    "\r\n",
+    "（1） 分词：  \r\n",
+    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \r\n",
+    "    本项目目前支持的分词工具有：`jieba`   \r\n",
+    "- 单字解析：按字符划分。\r\n",
+    "    \r\n",
+    "      \r\n",
+    "（2） 筛选：过滤指定的停用词。   \r\n",
+    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \r\n",
+    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\r\n"
    ],
    "metadata": {}
   },
@@ -68,7 +79,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "source": [
-    "# 导入模块\n",
+    "# 导入模块\r\n",
     "from EduNLP.SIF.tokenization.text import tokenize "
    ],
    "outputs": [],
@@ -78,7 +89,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "source": [
-    "# 输入\n",
+    "# 输入\r\n",
     "text = \"三角函数是基本初等函数之一\""
    ],
    "outputs": [],
@@ -87,8 +98,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "#### 词组解析\n",
-    "\n",
+    "##### 词组解析\r\n",
+    "\r\n",
     "分词粒度参数选择 word： `granularity = \"word\"` "
    ],
    "metadata": {}
@@ -97,7 +108,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
     "tokenize(text, granularity=\"word\")"
    ],
    "outputs": [
@@ -117,8 +128,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "#### 单字解析\n",
-    "\n",
+    "##### 单字解析\r\n",
+    "\r\n",
     "分词粒度参数选择 word： `granularity = \"char\"` "
    ],
    "metadata": {}
@@ -127,7 +138,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "source": [
-    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\r\n",
     "tokenize(text, granularity=\"char\")"
    ],
    "outputs": [
@@ -147,7 +158,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "#### 停用词表"
+    "##### 停用词表"
    ],
    "metadata": {}
   },
@@ -155,10 +166,10 @@
    "cell_type": "code",
    "execution_count": 5,
    "source": [
-    "# 获取自己的停用词表\n",
-    "spath = \"test_stopwords.txt\"\n",
-    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\n",
-    "stopwords = get_stopwords(spath)\n",
+    "# 获取自己的停用词表\r\n",
+    "spath = \"test_stopwords.txt\"\r\n",
+    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\r\n",
+    "stopwords = get_stopwords(spath)\r\n",
     "stopwords"
    ],
    "outputs": [
@@ -179,7 +190,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "source": [
-    "# 输出：传入停用词表（stopwords）\n",
+    "# 输出：传入停用词表（stopwords）\r\n",
     "tokenize(text,granularity=\"word\",stopwords=stopwords)"
    ],
    "outputs": [
@@ -199,7 +210,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 公式解析\n",
+    "### 公式解析\r\n",
     "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
    ],
    "metadata": {}
@@ -208,7 +219,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "source": [
-    "# 导入模块\n",
+    "# 导入模块\r\n",
     "from EduNLP.SIF.tokenization.formula import tokenize"
    ],
    "outputs": [],
@@ -305,7 +316,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\n",
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\r\n",
     "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
    ],
    "outputs": [
@@ -343,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "source": [
-    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\n",
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\r\n",
     "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
    ],
    "outputs": [
@@ -373,13 +384,13 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 综合解析\n",
-    "\n",
-    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\n",
-    "```python\n",
-    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\n",
-    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\n",
-    "```\n"
+    "### 综合解析\r\n",
+    "\r\n",
+    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\r\n",
+    "```python\r\n",
+    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\r\n",
+    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\r\n",
+    "```\r\n"
    ],
    "metadata": {}
   },
@@ -387,17 +398,17 @@
    "cell_type": "code",
    "execution_count": 39,
    "source": [
-    "# 导入模块\n",
-    "from EduNLP.Tokenizer import get_tokenizer\n",
-    "\n",
-    "# 输入\n",
-    "item = {\n",
-    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
-    "}\n",
-    "\n",
-    "# 输出\n",
-    "tokenizer = get_tokenizer(\"text\")\n",
-    "tokens = tokenizer(item)\n",
+    "# 导入模块\r\n",
+    "from EduNLP.Tokenizer import get_tokenizer\r\n",
+    "\r\n",
+    "# 输入\r\n",
+    "item = {\r\n",
+    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n",
+    "}\r\n",
+    "\r\n",
+    "# 输出\r\n",
+    "tokenizer = get_tokenizer(\"text\")\r\n",
+    "tokens = tokenizer(item)\r\n",
     "next(tokens) "
    ],
    "outputs": [
diff --git a/examples/tokenizer/tokenizer2.ipynb b/examples/tokenizer/tokenizer2.ipynb
new file mode 100644
index 00000000..4819b00d
--- /dev/null
+++ b/examples/tokenizer/tokenizer2.ipynb
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Tokenizer\n",
+    "\n",
+    "## 概述\n",
+    "\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \n",
+    "\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。\n",
+    "\n",
+    "### 文本解析\n",
+    "\n",
+    "根据题目文本切分粒度的大小，文本解析又分为 **“句解析”** 和 **“词解析”**。\n",
+    "\n",
+    "（1） 句解析（sentence-tokenization）：将较长的文档切分成若干句子的过程称为“分句”。每个句子为一个“令牌”（token）。（待实现）    \n",
+    "  \n",
+    "\n",
+    "（2） 词解析（text-tokenization）：一个句子（不含公式）是由若干“词”按顺序构成的，将一个句子切分为若干词的过程称为“词解析”。根据词的粒度大小，又可细分为“词组解析”和\"单字解析\"。\n",
+    "- 词组解析 (word-tokenization)：每一个词组为一个“令牌”（token）。\n",
+    "- 单字解析 (char-tokenization)：单个字符即为一个“令牌”（token）。\n",
+    "\n",
+    "### 公式解析\n",
+    "\n",
+    "公式解析（formula-tokenization）：理科类文本中常常含有公式。将一个符合 latex 语法的公式切分为标记字符列表的过程称为“公式解析”。每个标记字符为一个“令牌”（token）。  \n",
+    "  "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 文本解析"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 句解析\n",
+    "\n",
+    "待实现..."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 词解析\n",
+    "\n",
+    "词解析分为两个主要步骤：  \n",
+    "\n",
+    "（1） 分词：  \n",
+    "- 词组解析：使用分词工具切分并提取题目文本中的词。  \n",
+    "    本项目目前支持的分词工具有：`jieba`   \n",
+    "- 单字解析：按字符划分。\n",
+    "    \n",
+    "      \n",
+    "（2） 筛选：过滤指定的停用词。   \n",
+    "- 本项目默认使用的停用词表：[stopwords](https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt)  \n",
+    "- 你也可以使用自己的停用词表，具体使用方法见下面的示例。\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "# 导入模块\n",
+    "from EduNLP.SIF.tokenization.text import tokenize "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "# 输入\n",
+    "text = \"三角函数是基本初等函数之一\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 词组解析\n",
+    "\n",
+    "分词粒度参数选择 word： `granularity = \"word\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
+    "tokenize(text, granularity=\"word\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三角函数', '初等', '函数']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 单字解析\n",
+    "\n",
+    "分词粒度参数选择 word： `granularity = \"char\"` "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# 输出：默认使用 EduNLP 项目提供的停用词表\n",
+    "tokenize(text, granularity=\"char\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三', '角', '函', '数', '基', '初', '函', '数']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 停用词表"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# 获取自己的停用词表\n",
+    "spath = \"test_stopwords.txt\"\n",
+    "from EduNLP.SIF.tokenization.text.stopwords import get_stopwords\n",
+    "stopwords = get_stopwords(spath)\n",
+    "stopwords"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'一旦', '一时', '一来', '一样', '一次', '一片', '一番', '一直', '一致'}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# 输出：传入停用词表（stopwords）\n",
+    "tokenize(text,granularity=\"word\",stopwords=stopwords)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['三角函数', '是', '基本', '初等', '函数', '之一']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 公式解析\n",
+    "切分出 latex 公式的每个标记符号。针对本模块更加详细的解释参见 [formula](../formula/formula.ipynb)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# 导入模块\n",
+    "from EduNLP.SIF.tokenization.formula import tokenize"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输入"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "formula = \"\\\\frac{\\\\pi}{x + y} + 1 = x\""
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 输出"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "tokenize(formula, method=\"linear\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\frac', '{', '\\\\pi', '}', '{', 'x', '+', 'y', '}', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=False)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['\\\\pi', '{ }', 'x', '+', 'y', '{ }', '\\\\frac', '+', '1', '=', 'x']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成 token\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " '{ }',\n",
+       " 'mathord',\n",
+       " '+',\n",
+       " 'mathord',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# 输出形式选择抽象语法分析树（ast）且将公式变量名转换成带编号的 token\n",
+    "tokenize(formula, method=\"ast\", return_type=\"list\", ord2token=True, var_numbering=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord_con',\n",
+       " '{ }',\n",
+       " 'mathord_0',\n",
+       " '+',\n",
+       " 'mathord_1',\n",
+       " '{ }',\n",
+       " '\\\\frac',\n",
+       " '+',\n",
+       " 'textord',\n",
+       " '=',\n",
+       " 'mathord_0']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 综合解析\n",
+    "\n",
+    "综合解析，即综合以上两种解析方式（标记解析 + 公式解析），提供对题目文本的全解析。另外，如遇到特殊符号将转换成常量，例如：\n",
+    "```python\n",
+    "FIGURE_SYMBOL = \"[FIGURE]\" # $\\SIFChoice$\n",
+    "QUES_MARK_SYMBOL = \"[MARK]\" # $\\FigureID{1}$\n",
+    "```\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "source": [
+    "# 导入模块\n",
+    "from EduNLP.Tokenizer import get_tokenizer\n",
+    "\n",
+    "# 输入\n",
+    "item = {\n",
+    "    \"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n",
+    "}\n",
+    "\n",
+    "# 输出\n",
+    "tokenizer = get_tokenizer(\"text\")\n",
+    "tokens = tokenizer(item)\n",
+    "next(tokens) "
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " 'ABC',\n",
+       " '斜边',\n",
+       " 'BC',\n",
+       " '直角',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\x08',\n",
+       " 'igtriangleupABC',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " 'I',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " 'II',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " 'III',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " '概率',\n",
+       " '记',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3',\n",
+       " '[MARK]',\n",
+       " '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.8.5",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file

From 144f7b741b2cd17e27f3b4b7506f510e5181970f Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 20 Sep 2021 22:59:42 +0800
Subject: [PATCH 125/149] try fix bug

---
 docs/source/tutorial/zh/index.rst | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index e2c19611..1dfd2b63 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -35,19 +35,15 @@
 
 为使您快速了解此项目的功能，此部分仅展示常用的函数接口使用方法（如得到令牌化序列、试题对应的向量等），对于其中间函数模块（如parse、formula、segment等）以及更细分的接口方法不做展示，如需深入学习，请查看相关部分的文档。
 
-令牌化
->>>>>>>>>>>>
 
 .. nbgallery::
     :caption: This is a thumbnail gallery:
     :name: tokenize_gallery
     :glob:
     
-    令牌化  <../../build/blitz/tokenizer/t_tokenize.ipynb>
+    令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>
 
 
-向量化
->>>>>>>>>>>>
 
 .. nbgallery::
     :caption: This is a thumbnail gallery:
@@ -55,4 +51,3 @@
     :glob:
     
     向量化  <../../build/blitz/vectorization/total_vector.ipynb>
-    令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>

From 3f49879049628dd2377a93a1aeaf1705a316b687 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 20 Sep 2021 23:12:34 +0800
Subject: [PATCH 126/149] update docs

---
 examples/tokenizer/tokenizer.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/tokenizer/tokenizer.ipynb b/examples/tokenizer/tokenizer.ipynb
index 6f4516ec..c6130164 100644
--- a/examples/tokenizer/tokenizer.ipynb
+++ b/examples/tokenizer/tokenizer.ipynb
@@ -14,7 +14,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Tokenizer\r\n",
+    "## 调用tokenize函数\r\n",
     "\r\n",
     "### 概述\r\n",
     "\r\n",

From 1f583a579e8bb9becd91d4fc05c39e85bdd87bc3 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 20 Sep 2021 23:23:07 +0800
Subject: [PATCH 127/149] update doc

---
 examples/tokenizer/tokenizer.ipynb | 104 +++++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)

diff --git a/examples/tokenizer/tokenizer.ipynb b/examples/tokenizer/tokenizer.ipynb
index c6130164..1f3b62df 100644
--- a/examples/tokenizer/tokenizer.ipynb
+++ b/examples/tokenizer/tokenizer.ipynb
@@ -483,6 +483,110 @@
     }
    ],
    "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Tokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### TextTokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "items = [{\r\n",
+    "        \"stem\": \"已知集合$A=\\\\left\\\\{x \\\\mid x^{2}-3 x-4<0\\\\right\\\\}, \\\\quad B=\\\\{-4,1,3,5\\\\}, \\\\quad$ 则 $A \\\\cap B=$\",\r\n",
+    "        \"options\": [\"1\", \"2\"]\r\n",
+    "        }]\r\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
+    "tokens = tokenizer(items, key=lambda x: x[\"stem\"])\r\n",
+    "print(next(tokens))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['已知', '集合', 'A', '=', '\\\\left', '\\\\{', 'x', '\\\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<', '0', '\\\\right', '\\\\}', ',', '\\\\quad', 'B', '=', '\\\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\\\}', ',', '\\\\quad', 'A', '\\\\cap', 'B', '=']\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "items = [\"有公式$\\\\FormFigureID{wrong1?}$，如图$\\\\FigureID{088f15ea-xxx}$,若$x,y$满足约束条件公式$\\\\FormFigureBase64{wrong2?}$,$\\\\SIFSep$，则$z=x+7 y$的最大值为$\\\\SIFBlank$\"]"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "\r\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\r\n",
+    "tokens = [t for t in tokenizer(items)]\r\n",
+    "tokens"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['公式',\n",
+       "  '[FORMULA]',\n",
+       "  '如图',\n",
+       "  '[FIGURE]',\n",
+       "  'x',\n",
+       "  ',',\n",
+       "  'y',\n",
+       "  '约束条件',\n",
+       "  '公式',\n",
+       "  '[FORMULA]',\n",
+       "  '[SEP]',\n",
+       "  'z',\n",
+       "  '=',\n",
+       "  'x',\n",
+       "  '+',\n",
+       "  '7',\n",
+       "  'y',\n",
+       "  '最大值',\n",
+       "  '[MARK]']]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {

From 1bb1b383cca62b45f73575c949f5739aeeff5929 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Mon, 20 Sep 2021 23:45:38 +0800
Subject: [PATCH 128/149] update doc

---
 examples/tokenizer/tokenizer.ipynb | 1019 +++++++++++++++++++++++++++-
 1 file changed, 1017 insertions(+), 2 deletions(-)

diff --git a/examples/tokenizer/tokenizer.ipynb b/examples/tokenizer/tokenizer.ipynb
index 1f3b62df..a928ebba 100644
--- a/examples/tokenizer/tokenizer.ipynb
+++ b/examples/tokenizer/tokenizer.ipynb
@@ -484,6 +484,977 @@
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 调用sif4sci函数来进行令牌化\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
+    "\n",
+    "![Figure](../../asset/_static/item.png)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "item = {\r\n",
+    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n",
+    "  \"options\": [\"$p_1=p_2$\", \"$p_1=p_3$\", \"$p_2=p_3$\", \"$p_1=p_2+p_3$\"]\r\n",
+    "}\r\n",
+    "item[\"stem\"]"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 加载图片：`$\\\\FigureID{1}$`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from PIL import Image\r\n",
+    "img = Image.open(\"../../asset/_static/item_figure.png\")\r\n",
+    "figures = {\"1\": img}\r\n",
+    "img"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 导入模块"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from EduNLP.SIF import sif4sci, is_sif, to_sif"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 验证题目格式"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "is_sif(item['stem'])"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 若发现题目因为公式没有包含在 `$$` 中而不符合 SIF 格式，则可以使用 `to_sif` 模块转成标准格式。示例如下："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "is_sif(text)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "to_sif(text)\r\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 题目切分及令牌化\r\n",
+    "\r\n",
+    "现在我们得到了符合标准格式的题目文本，接下来可以对题目做进一步的预训练，例如：切分和令牌化。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 题目切分"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 基本切分\r\n",
+    "分离文本、公式、图片和特殊符号。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments = sif4sci(item[\"stem\"], figures=figures, tokenization=False)\r\n",
+    "segments"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\\\SIFChoice', \\FigureID{1}]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.text_segments"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形',\n",
+       " '的斜边',\n",
+       " ', 直角边',\n",
+       " ', ',\n",
+       " '.',\n",
+       " '的三边所围成的区域记为',\n",
+       " ',黑色部分记为',\n",
+       " ', 其余部分记为',\n",
+       " '.在整个图形中随机取一点，此点取自',\n",
+       " '的概率分别记为',\n",
+       " ',则']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 公式部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.formula_segments\r\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I,II,III',\n",
+       " 'p_1,p_2,p_3']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 图片部分"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.figure_segments"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\\FigureID{1}]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.figure_segments[0].figure"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x7F6DF04BBBE0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 特殊符号"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "segments.ques_mark_segments"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\\\\SIFChoice']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 标记化切分  \r\n",
+    "如果您不注重题目文本和公式的具体内容，仅仅是对题目的整体（或部分）构成感兴趣，那么可以通过修改 `symbol` 参数来将不同的成分转化成特定标记，方便您的研究。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    " - symbol:\n",
+    "    - \"t\": text\n",
+    "    - \"f\": formula\n",
+    "    - \"g\": figure\n",
+    "    - \"m\": question mark"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 令牌化\r\n",
+    "\r\n",
+    "为了方便后续向量化表征试题，本模块提供题目文本的令牌化解析（Tokenization），即将题目转换成令牌序列。    \r\n",
+    "\r\n",
+    "根据构成题目的元素类型，解析功能分为 **“文本解析”** 和 **“公式解析”** 两部分。更具体的过程解析参见 [令牌化](../Tokenizer/tokenizer.ipynb)。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 文本解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens.text_tokens"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['如图',\n",
+       " '古希腊',\n",
+       " '数学家',\n",
+       " '希波',\n",
+       " '克拉底',\n",
+       " '研究',\n",
+       " '几何图形',\n",
+       " '此图',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '三个',\n",
+       " '半圆',\n",
+       " '直径',\n",
+       " '直角三角形',\n",
+       " '斜边',\n",
+       " '直角',\n",
+       " '三边',\n",
+       " '围成',\n",
+       " '区域',\n",
+       " '记',\n",
+       " '黑色',\n",
+       " '记',\n",
+       " '其余部分',\n",
+       " '记',\n",
+       " '图形',\n",
+       " '中',\n",
+       " '随机',\n",
+       " '取',\n",
+       " '一点',\n",
+       " '此点',\n",
+       " '取自',\n",
+       " '概率',\n",
+       " '记']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "##### 公式解析结果"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokens.formula_tokens"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 自定义参数，得到定制化解析结果"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（1）如果您想按 latex 语法标记拆分公式的各个部分，并得到顺序序列结果，输出方法（`method`）可以选择：`linear`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\": {\r\n",
+    "            \"method\": \"linear\",\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ABC',\n",
+       " 'BC',\n",
+       " 'AB',\n",
+       " 'AC',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'ABC',\n",
+       " 'I',\n",
+       " 'II',\n",
+       " 'III',\n",
+       " 'I',\n",
+       " ',',\n",
+       " 'II',\n",
+       " ',',\n",
+       " 'III',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '1',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '2',\n",
+       " ',',\n",
+       " 'p',\n",
+       " '_',\n",
+       " '3']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（2） 如果您想得到公式解析出的语法分析树序列，输出方法可以选择：`ast`\n",
+    "> 抽象语法分析树，简称语法树（Syntax tree），是源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。  \n",
+    "> 因此，ast 可以看做是公式的语法结构表征。"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens\r\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<Formula: ABC>,\n",
+       " <Formula: BC>,\n",
+       " <Formula: AB>,\n",
+       " <Formula: AC>,\n",
+       " <Formula: \\bigtriangleup ABC>,\n",
+       " <Formula: I>,\n",
+       " <Formula: II>,\n",
+       " <Formula: III>,\n",
+       " <Formula: I,II,III>,\n",
+       " <Formula: p_1,p_2,p_3>]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 语法树展示："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "f = sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"return_type\": \"ast\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "            \"var_numbering\": True,\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens\r\n",
+    "f\r\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<networkx.classes.digraph.DiGraph at 0x7f6d28ca3940>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cb5a60>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28caba30>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d26b28fd0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4490>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28cd4d90>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c843a0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84ee0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84610>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x7f6d28c84b50>]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "for i in range(0, len(f)):\r\n",
+    "    ForestPlotter().export(\r\n",
+    "        f[i], root_list=[node for node in f[i]],\r\n",
+    "    )\r\n",
+    "# plt.show()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（3）如果您只是关心公式的结构和类型，并不关心变量具体是什么，比如二元二次方程 `x^2 + y = 1` ，它从公式结构和类型上来说，和 `w^2 + z = 1` 没有区别。  \n",
+    "此时，您可以设置如下参数：`ord2token = True`，将公式变量名转换成 token"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"return_type\": \"list\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "（4） 如果您除了 （3） 中提供的功能之外，还需要区分不同的变量。此时可以另外设置参数：`var_numbering=True`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "            \"return_type\": \"list\",\r\n",
+    "            \"var_numbering\": True\r\n",
+    "        }\r\n",
+    "    }\r\n",
+    ").formula_tokens"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_0',\n",
+       " 'mathord_2',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 综合训练\r\n",
+    "\r\n",
+    "综合上述方法，将题目转换成令牌序列，为后续向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\r\n",
+    "        symbol=\"fgm\")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "markdown",
    "source": [
@@ -559,6 +1530,7 @@
    ],
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
       "text/plain": [
        "[['公式',\n",
@@ -582,8 +1554,51 @@
        "  '[MARK]']]"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### PureTextTokenizer"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "tokenizer = get_tokenizer(\"pure_text\") # tokenizer = PureTextTokenizer()\r\n",
+    "tokens = [t for t in tokenizer(items)]\r\n",
+    "tokens"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "[['公式',\n",
+       "  '如图',\n",
+       "  '[FIGURE]',\n",
+       "  'x',\n",
+       "  ',',\n",
+       "  'y',\n",
+       "  '约束条件',\n",
+       "  '公式',\n",
+       "  '[SEP]',\n",
+       "  'z',\n",
+       "  '=',\n",
+       "  'x',\n",
+       "  '+',\n",
+       "  '7',\n",
+       "  'y',\n",
+       "  '最大值',\n",
+       "  '[MARK]']]"
+      ]
+     },
+     "metadata": {}
     }
    ],
    "metadata": {}

From 2e16cdbf6aa5468f26fe40a1ed29aa16c56ff591 Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Wed, 22 Sep 2021 22:51:33 +0800
Subject: [PATCH 129/149] [feat] add bert finetuning

---
 EduNLP/Pretrain/__init__.py |   1 +
 EduNLP/Pretrain/bert_vec.py | 119 ++++++++++++++++++++++++++++++++++++
 EduNLP/SIF/__init__.py      |   1 +
 setup.py                    |   2 +-
 4 files changed, 122 insertions(+), 1 deletion(-)
 create mode 100644 EduNLP/Pretrain/bert_vec.py

diff --git a/EduNLP/Pretrain/__init__.py b/EduNLP/Pretrain/__init__.py
index a6daf797..984379e9 100644
--- a/EduNLP/Pretrain/__init__.py
+++ b/EduNLP/Pretrain/__init__.py
@@ -2,3 +2,4 @@
 # 2021/5/29 @ tongshiwei
 
 from .gensim_vec import train_vector, GensimWordTokenizer, GensimSegTokenizer
+from .bert_vec import BertTokenizer, finetune_bert
diff --git a/EduNLP/Pretrain/bert_vec.py b/EduNLP/Pretrain/bert_vec.py
new file mode 100644
index 00000000..58530e68
--- /dev/null
+++ b/EduNLP/Pretrain/bert_vec.py
@@ -0,0 +1,119 @@
+from EduNLP import logger
+import multiprocessing
+import transformers
+from EduNLP.Tokenizer import PureTextTokenizer
+from copy import deepcopy
+import itertools as it
+from EduNLP.SIF.sif import sif4sci
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+from transformers import DataCollatorForLanguageModeling
+from transformers import Trainer, TrainingArguments
+from torch.utils.data import Dataset
+from EduNLP.SIF import Symbol, FORMULA_SYMBOL, FIGURE_SYMBOL, QUES_MARK_SYMBOL, TAG_SYMBOL, SEP_SYMBOL
+
+
+__all__ = ["BertTokenizer", "finetune_bert"]
+
+
+class BertTokenizer(object):
+    """
+
+    Parameters
+    ----------
+    pretrain_model:
+        used pretrained model
+
+    Returns
+    ----------
+
+    Examples
+    ----------
+    >>> tokenizer = BertTokenizer()
+    >>> item = "有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"
+    >>> token_item = tokenizer(item)
+    >>> print(token_item.input_ids[:10])
+    [101, 1062, 2466, 1963, 1745, 21129, 166, 117, 167, 5276]
+    >>> print(tokenizer.tokenize(item)[:10])
+    ['公', '式', '如', '图', '[FIGURE]', 'x', ',', 'y', '约', '束']
+    """
+    def __init__(self, pretrain_model="bert-base-chinese"):
+        self.tokenizer = AutoTokenizer.from_pretrained(pretrain_model)
+        customize_tokens = []
+        for i in [FORMULA_SYMBOL, FIGURE_SYMBOL, QUES_MARK_SYMBOL, TAG_SYMBOL, SEP_SYMBOL]:
+            if i not in self.tokenizer.additional_special_tokens:
+                customize_tokens.append(Symbol(i))
+        if customize_tokens:
+            self.tokenizer.add_special_tokens({'additional_special_tokens': customize_tokens})
+
+    def __call__(self, item):
+        self.pure_text_tokenizer = PureTextTokenizer()
+        item = ''.join(next(self.pure_text_tokenizer([item])))
+        return self.tokenizer(item, truncation=True, padding=True)
+
+    def tokenize(self, item):
+        self.pure_text_tokenizer = PureTextTokenizer()
+        item = ''.join(next(self.pure_text_tokenizer([item])))
+        return self.tokenizer.tokenize(item)
+
+    def __len__(self):
+        return len(self.tokenizer)
+
+
+class FinetuneDataset(Dataset):
+    def __init__(self, items):
+        self.items = items
+        self.len = len(items)
+
+    def __getitem__(self, index):
+        return self.items[index]
+
+    def __len__(self):
+        return self.len
+
+
+def finetune_bert(items, output_dir, pretrain_model="bert-base-chinese", train_params=None):
+    model = AutoModelForMaskedLM.from_pretrained(pretrain_model)
+    tokenizer = BertTokenizer(pretrain_model)
+    # resize embedding for additional sepecial tokens
+    model.resize_token_embeddings(len(tokenizer))
+
+    # training parameters
+    if train_params:
+        mlm_probability = train_params['mlm_probability'] if 'mlm_probability' in train_params else 0.15
+        epochs = train_params['epochs'] if 'epochs' in train_params else 1
+        batch_size = train_params['batch_size'] if 'batch_size' in train_params else 64
+        save_steps = train_params['save_steps'] if 'save_steps' in train_params else 1000
+        save_total_limit = train_params['save_total_limit'] if 'save_total_limit' in train_params else 2
+    else:
+        # default
+        mlm_probability = 0.15
+        epochs = 1
+        batch_size = 64
+        save_steps = 1000
+        save_total_limit = 2
+
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer.tokenizer, mlm=True, mlm_probability=mlm_probability
+    )
+
+    dataset = FinetuneDataset(items)
+
+    training_args = TrainingArguments(
+        output_dir=output_dir,
+        overwrite_output_dir=True,
+        num_train_epochs=epochs,
+        per_device_train_batch_size=batch_size,
+        save_steps=save_steps,
+        save_total_limit=save_total_limit,
+        prediction_loss_only=True,
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        data_collator=data_collator,
+        train_dataset=dataset,
+    )
+    trainer.train()
+    trainer.save_model(output_dir)
diff --git a/EduNLP/SIF/__init__.py b/EduNLP/SIF/__init__.py
index 418baea5..64720c52 100644
--- a/EduNLP/SIF/__init__.py
+++ b/EduNLP/SIF/__init__.py
@@ -3,3 +3,4 @@
 
 from .sif import is_sif, to_sif, sif4sci
 from .tokenization import link_formulas
+from .constants import *
diff --git a/setup.py b/setup.py
index 1d38042d..0e39d81d 100644
--- a/setup.py
+++ b/setup.py
@@ -35,7 +35,7 @@
         ml_pytorch_deps = []
         logging.warning("Current python version %s is not supported by pytorch", str(sys.version_info[:2]))
 
-vec_deps = ['gensim'] + ml_pytorch_deps
+vec_deps = ['gensim'] + ml_pytorch_deps + ['transformers']
 
 setup(
     name='EduNLP',

From b86336fa871e10aff7642e236512d4e8af877546 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Wed, 22 Sep 2021 23:40:11 +0800
Subject: [PATCH 130/149] update docs, now it follows the list of Attributes

---
 EduNLP/Formula/Formula.py     | 23 +++++++++++------------
 EduNLP/SIF/segment/segment.py | 32 ++++++++++++++++----------------
 2 files changed, 27 insertions(+), 28 deletions(-)

diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py
index 77e5f7d0..6eb80049 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -17,18 +17,6 @@ class Formula(object):
     """
     The part transform a formula to the parsed abstracted syntax tree.
 
-    Attributes
-    ------------
-    ast
-        show all ast details
-    elements
-        just show elements' id, type, text and role
-    ast_graph
-        draw a ast graph
-    to_str
-    resetable
-        return bool
-
     Parameters
     ----------
     formula: str or List[Dict]
@@ -54,6 +42,17 @@ class Formula(object):
     >>> f.elements
     [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
 
+        Attributes
+    ------------
+    ast
+        show all ast details
+    elements
+        just show elements' id, type, text and role
+    ast_graph
+        draw a ast graph
+    to_str
+    resetable
+        return bool
     """
     def __init__(self, formula: (str, List[Dict]), variable_standardization=False, const_mathord=None,
                  init=True, *args, **kwargs):
diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py
index 7e9ed5ed..39e2f869 100644
--- a/EduNLP/SIF/segment/segment.py
+++ b/EduNLP/SIF/segment/segment.py
@@ -82,6 +82,22 @@ class SepSegment(str):
 class SegmentList(object):
     """
 
+    Parameters
+    ----------
+    item
+    figures:dict
+
+    Returns
+    ----------
+    list
+        tokenizated item
+
+    Examples
+    --------
+    >>> test_item = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
+    >>> SegmentList(test_item)
+    ['如图所示，则三角形', 'ABC', '的面积是', '\\\\SIFBlank', '。', \\FigureID{1}]
+
     Attributes
     ----------
     segments
@@ -98,22 +114,6 @@ class SegmentList(object):
         show tag segments
     describe
         show number of each elements
-
-    Parameters
-    ----------
-    item
-    figures:dict
-
-    Returns
-    ----------
-    list
-        tokenizated item
-
-    Examples
-    --------
-    >>> test_item = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
-    >>> SegmentList(test_item)
-    ['如图所示，则三角形', 'ABC', '的面积是', '\\\\SIFBlank', '。', \\FigureID{1}]
     """
     def __init__(self, item, figures: dict = None):
         self._segments = []

From 096be3b68c87d5c7d32ceaf763ceb5d0d1961cdc Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Thu, 23 Sep 2021 21:38:10 +0800
Subject: [PATCH 131/149] [feat] add parameters for finetuning

---
 EduNLP/Pretrain/bert_vec.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/EduNLP/Pretrain/bert_vec.py b/EduNLP/Pretrain/bert_vec.py
index 58530e68..3b73a3bb 100644
--- a/EduNLP/Pretrain/bert_vec.py
+++ b/EduNLP/Pretrain/bert_vec.py
@@ -56,9 +56,6 @@ def tokenize(self, item):
         item = ''.join(next(self.pure_text_tokenizer([item])))
         return self.tokenizer.tokenize(item)
 
-    def __len__(self):
-        return len(self.tokenizer)
-
 
 class FinetuneDataset(Dataset):
     def __init__(self, items):
@@ -76,15 +73,18 @@ def finetune_bert(items, output_dir, pretrain_model="bert-base-chinese", train_p
     model = AutoModelForMaskedLM.from_pretrained(pretrain_model)
     tokenizer = BertTokenizer(pretrain_model)
     # resize embedding for additional sepecial tokens
-    model.resize_token_embeddings(len(tokenizer))
+    model.resize_token_embeddings(len(tokenizer.tokenizer))
 
     # training parameters
     if train_params:
         mlm_probability = train_params['mlm_probability'] if 'mlm_probability' in train_params else 0.15
         epochs = train_params['epochs'] if 'epochs' in train_params else 1
         batch_size = train_params['batch_size'] if 'batch_size' in train_params else 64
-        save_steps = train_params['save_steps'] if 'save_steps' in train_params else 1000
+        save_steps = train_params['save_steps'] if 'save_steps' in train_params else 100
         save_total_limit = train_params['save_total_limit'] if 'save_total_limit' in train_params else 2
+        logging_steps = train_params['logging_steps'] if 'logging_steps' in train_params else 5
+        gradient_accumulation_steps = train_params['gradient_accumulation_steps'] \
+            if 'gradient_accumulation_steps' in train_params else 1
     else:
         # default
         mlm_probability = 0.15
@@ -92,6 +92,8 @@ def finetune_bert(items, output_dir, pretrain_model="bert-base-chinese", train_p
         batch_size = 64
         save_steps = 1000
         save_total_limit = 2
+        logging_steps = 5
+        gradient_accumulation_steps = 1
 
     data_collator = DataCollatorForLanguageModeling(
         tokenizer=tokenizer.tokenizer, mlm=True, mlm_probability=mlm_probability
@@ -106,13 +108,15 @@ def finetune_bert(items, output_dir, pretrain_model="bert-base-chinese", train_p
         per_device_train_batch_size=batch_size,
         save_steps=save_steps,
         save_total_limit=save_total_limit,
-        prediction_loss_only=True,
+        logging_steps=logging_steps,
+        gradient_accumulation_steps=gradient_accumulation_steps,
     )
 
     trainer = Trainer(
         model=model,
         args=training_args,
         data_collator=data_collator,
+        tokenizer=tokenizer.tokenizer,
         train_dataset=dataset,
     )
     trainer.train()

From 5e5cdfb12df5cddaf5249c91c5ad37882eb65932 Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Thu, 23 Sep 2021 21:38:35 +0800
Subject: [PATCH 132/149] [test] add test for bert finetuning

---
 tests/test_vec/test_bert.py | 46 +++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 tests/test_vec/test_bert.py

diff --git a/tests/test_vec/test_bert.py b/tests/test_vec/test_bert.py
new file mode 100644
index 00000000..1547cfc6
--- /dev/null
+++ b/tests/test_vec/test_bert.py
@@ -0,0 +1,46 @@
+import torch
+import numpy as np
+import pytest
+from EduNLP.Pretrain import BertTokenizer, finetune_bert
+
+
+@pytest.fixture(scope="module")
+def stem_data_bert(data):
+    test_items = [
+        {'stem': '有公式$\\FormFigureID{wrong1?}$和公式$\\FormFigureBase64{wrong2?}$，\
+            如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'},
+        {'stem': '如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$, \
+            若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'}
+    ]
+    data = test_items + data
+    _data = []
+    tokenizer = BertTokenizer()
+    for e in data[:10]:
+        d = tokenizer(e["stem"])
+        if d is not None:
+            _data.append(d)
+    assert _data
+    return _data
+
+
+def test_bert_without_param(stem_data_bert, tmpdir):
+    output_dir = str(tmpdir.mkdir('finetuneBert'))
+    finetune_bert(
+        stem_data_bert,
+        output_dir
+    )
+
+
+def test_bert(stem_data_bert, tmpdir):
+    output_dir = str(tmpdir.mkdir('finetuneBert'))
+    train_params = {
+        'epochs': 1,
+        'save_steps': 100,
+        'batch_size': 8,
+        'logging_steps': 3
+    }
+    finetune_bert(
+        stem_data_bert,
+        output_dir,
+        train_params=train_params
+    )

From ddccf29187b393db45b0060e117476b85aafa234 Mon Sep 17 00:00:00 2001
From: BAOOOOOM <82091145+BAOOOOOM@users.noreply.github.com>
Date: Sun, 26 Sep 2021 14:41:18 +0800
Subject: [PATCH 133/149] update index

---
 docs/source/tutorial/zh/index.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 1dfd2b63..5dafba2b 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -3,9 +3,9 @@
 
 *  `标准项目格式 <sif.rst>`_ 
 
-*  `语法解析 <seg.rst>`_ 
+*  `语法解析 <parse.rst>`_ 
 
-*  `成分分解 <parse.rst>`_ 
+*  `成分分解 <seg.rst>`_ 
 
 *  `令牌化 <tokenize.rst>`_ 
 

From 4ea227408cf69bc7712d24d370d882703ad600fe Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Sun, 26 Sep 2021 16:25:05 +0800
Subject: [PATCH 134/149] [BUGFIX] fix the params lose in tokenization

---
 EduNLP/SIF/tokenization/tokenization.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/EduNLP/SIF/tokenization/tokenization.py b/EduNLP/SIF/tokenization/tokenization.py
index 299eaf62..fab96b00 100644
--- a/EduNLP/SIF/tokenization/tokenization.py
+++ b/EduNLP/SIF/tokenization/tokenization.py
@@ -2,6 +2,7 @@
 # 2021/5/18 @ tongshiwei
 
 from contextlib import contextmanager
+from copy import deepcopy
 from EduNLP.Formula import link_formulas as _link_formulas, Formula
 from ..constants import (
     Symbol, TEXT_SYMBOL, FIGURE_SYMBOL, FORMULA_SYMBOL, QUES_MARK_SYMBOL, TAG_SYMBOL, SEP_SYMBOL,
@@ -38,16 +39,17 @@ def __init__(self, segment_list: SegmentList, text_params=None, formula_params=N
             "s": []
         }
         self.text_params = text_params if text_params is not None else {}
+
+        self.formula_params = deepcopy(formula_params) if formula_params is not None else {"method": "linear"}
+
         self.symbolize_figure_formula = False
         self.skip_figure_formula = False
-        if formula_params is not None:
-            if "symbolize_figure_formula" in formula_params:
-                self.symbolize_figure_formula = formula_params.pop("symbolize_figure_formula")
-            if "skip_figure_formula" in formula_params:
-                self.skip_figure_formula = formula_params.pop("skip_figure_formula")
-
-        self.formula_params = formula_params if formula_params is not None else {"method": "linear"}
+        if "symbolize_figure_formula" in self.formula_params:
+            self.symbolize_figure_formula = self.formula_params.pop("symbolize_figure_formula")
+        if "skip_figure_formula" in self.formula_params:
+            self.skip_figure_formula = self.formula_params.pop("skip_figure_formula")
         self.formula_tokenize_method = self.formula_params.get("method")
+
         self.figure_params = figure_params if figure_params is not None else {}
         self.extend(segment_list.segments)
         self._token_idx = None

From 8f2856f7a2ad4fe443ff613bf70bfd1791ffdf28 Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Sun, 26 Sep 2021 17:07:53 +0800
Subject: [PATCH 135/149] [FEATURE] change the param name in sif.py

---
 EduNLP/SIF/sif.py          | 28 ++++++++++++++--------------
 tests/test_sif/test_sif.py | 10 +++++-----
 2 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index 309b25a7..9c9a650e 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -10,7 +10,7 @@
 __all__ = ["is_sif", "to_sif", "sif4sci"]
 
 
-def is_sif(item, check_formula=True, cache=False):
+def is_sif(item, check_formula=True, return_parser=False):
     r"""
     Parameters
     ----------
@@ -20,8 +20,8 @@ def is_sif(item, check_formula=True, cache=False):
 
         True if check the validity of formulas in item
         False if not check the validity of formulas in item, which is faster
-    cache: bool
-        whether to put the parsed item  in return.
+    return_parser: bool
+        whether to put the parsed item in return.
 
         when True, the format of return is (bool, Parser)
         when False, the format of return is bool
@@ -40,7 +40,7 @@ def is_sif(item, check_formula=True, cache=False):
     >>> is_sif(text)
     True
     >>> text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
-    >>> ret = is_sif(text, cache=True)
+    >>> ret = is_sif(text, return_parser=True)
     >>> ret # doctest: +ELLIPSIS
     (False, <EduNLP.SIF.parser.parser.Parser object...>)
     """
@@ -49,7 +49,7 @@ def is_sif(item, check_formula=True, cache=False):
     if item_parser.fomula_illegal_flag:
         raise ValueError(item_parser.fomula_illegal_message)
     ret = True if item_parser.error_flag == 0 and item_parser.modify_flag == 0 else False
-    if cache is True:
+    if return_parser is True:
         return ret, item_parser
     else:
         return ret
@@ -76,7 +76,7 @@ def to_sif(item, check_formula=True, cache_parser: Parser = None):
     >>> siftext = to_sif(text)
     >>> siftext
     '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'
-    >>> ret = is_sif(text, cache=True)
+    >>> ret = is_sif(text, return_parser=True)
     >>> ret # doctest: +ELLIPSIS
     (False, <EduNLP.SIF.parser.parser.Parser object...>)
     >>> to_sif(text, cache_parser=ret[1])
@@ -86,10 +86,10 @@ def to_sif(item, check_formula=True, cache_parser: Parser = None):
     if cache_parser is not None:
         return cache_parser.text
     else:
-        return is_sif(item, check_formula, cache=True)[1].text
+        return is_sif(item, check_formula, return_parser=True)[1].text
 
 
-def sif4sci(item: str, figures: (dict, bool) = None, safe_mode: int = 2, symbol: str = None, tokenization=True,
+def sif4sci(item: str, figures: (dict, bool) = None, mode: int = 2, symbol: str = None, tokenization=True,
             tokenization_params=None, errors="raise"):
     r"""
 
@@ -101,7 +101,7 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe_mode: int = 2, symbol:
     figures:
         when it is a dict, it means the id-to-instance information for figures in 'FormFigureID{...}' format,
         when it is a bool, it means whether to instantiate figures in 'FormFigureBase64{...}' format
-    safe_mode: int
+    mode: int
         when safe = 2, use is_sif and check formula in item
         when safe = 1, use is_sif but don't check formula in item
         when safe = 0, don't use is_sif and don't check anything in item
@@ -235,14 +235,14 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe_mode: int = 2, symbol:
     [['已知'], ['说法', '中', '正确']]
     """
     try:
-        if safe_mode in [1, 2]:
-            check_formula = True if safe_mode == 1 else False
-            sif, item_parser = is_sif(item, check_formula=check_formula, cache=True)
+        if mode in [1, 2]:
+            check_formula = True if mode == 1 else False
+            sif, item_parser = is_sif(item, check_formula=check_formula, return_parser=True)
             if sif is not True:
                 item = to_sif(item, cache_parser=item_parser)
-        elif safe_mode != 0:
+        elif mode != 0:
             raise KeyError(
-                "Unknown safe_mode %s, use only 0 or 1 or 2." % safe_mode
+                "Unknown mode %s, use only 0 or 1 or 2." % mode
             )
 
         ret = seg(item, figures, symbol)
diff --git a/tests/test_sif/test_sif.py b/tests/test_sif/test_sif.py
index b596e927..86606ed4 100644
--- a/tests/test_sif/test_sif.py
+++ b/tests/test_sif/test_sif.py
@@ -31,7 +31,7 @@ def test_to_sif():
     siftext = to_sif(text)
     print(siftext)
 
-    ret = is_sif(text, cache=True)
+    ret = is_sif(text, return_parser=True)
     assert ret[0] == 0
     if ret[0] is not True:
         siftext = to_sif(text, cache_parser=ret[1])
@@ -64,16 +64,16 @@ def test_sci4sif(figure0, figure1, figure0_base64, figure1_base64):
         }
     ))
     repr(sif4sci(
-        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", safe_mode=0
+        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", mode=0
     ))
     repr(sif4sci(
-        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", safe_mode=1
+        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", mode=1
     ))
     repr(sif4sci(
-        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", safe_mode=2
+        r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", mode=2
     ))
 
     with pytest.raises(KeyError):
         repr(sif4sci(
-            r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", safe_mode=3
+            r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", mode=3
         ))

From 24cc80b5d6e4395ca9a4b943bd07d8352b9c1d3f Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Sun, 26 Sep 2021 17:15:58 +0800
Subject: [PATCH 136/149] [examples] fix the sif examples

---
 examples/sif/sif.ipynb | 224 +++++++++++++++++++++++++++++------------
 1 file changed, 159 insertions(+), 65 deletions(-)

diff --git a/examples/sif/sif.ipynb b/examples/sif/sif.ipynb
index 50de8158..5df4f41b 100644
--- a/examples/sif/sif.ipynb
+++ b/examples/sif/sif.ipynb
@@ -83,7 +83,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAACICAYAAADzlJeRAAAe3klEQVR4nO2df1RT9/3/nyE/SWJyBdSUOgs72mKP1XhK24j2CGon4FagbtTMtpZt6rDzR7rjKt+2C+rnHGxdBbr2xB9zgc4j1NMJbpXg2hlsp2DnjrSHVaqcgquWbFqNLUgQkvf3DwgiSSA/7uUm8f04530O3Ny87zu5ed7X+8fr/XoJCCEEFAolrIjhuwEUCsUTKkwKJQyhwqRQwhAR3w2IBJqamuBwOHy+rtFokJKSMo4tokQ7VJgAGhoa0NzcDLvdjn//+99oa2uDw+FAa2srAIBhmFHf73K58O233wIAYmNj8cADDwAAZs+ejblz50Kr1UKn00Emk3H6OSjRg+Bum5Vtbm5Gc3MzrFYrPvnkE7S2toJhGPT09KC3t5f160mlUsTGxqKrqwsMw2D69OlYunQpdDodMjMzWb8eJTqIemHa7XbU19fjT3/6E/72t79BoVDA6XSiq6uL13YxDIOuri48/vjjyM/PR2ZmJpKSknhtEyV8iEph2mw21NbW4tChQzh16hSkUulQVzMckcvliImJgUwmQ3Z2NgwGA7RaLd/NovBIVAmzuroar732Gs6fPw8AuHnzJs8tCg61Wg2FQoFXX30VK1asGHOMS4k+Il6YNpsNf/jDH7Bz504ACGvLGChyuRxOpxOLFy/G+vXr6Zj0LiJihdnU1IQ33ngDdXV1cLlcoy5nRAMMw0AkEmHnzp14/vnn+W4OhWMiTpjNzc0oLCxEa2sr7HY7380Zd5RKJWQyGRVolBMxwrTZbFi/fj0++OAD3Lhxg+/m8A4VaHQT9sK02+347W9/i8rKyqgaP7KFUqmESqXCn//8Z+h0Or6bQ2GJsPaV3bdvH5KTk7Fv3z4qSh90dXXh66+/RlZWFjZs2HBXdu+jEhKGdHZ2Ep1OR9RqNQFAi59FJpORSZMmEYvFwuLdqCNlZRdYrI/iD2FnMfft24dZs2ahqamJjiUDxOFw4MqVK9Dr9XjqqadYsJ5tKJ+fjUNsNI4SGHw/Gdxcv36d5OXlUSvJUhGLxWTSpEmksbEx6HtyoSyNACBr6li80RS/CAuL2dDQgPvvvx/vv/8+tZIs0dfXhytXruBHP/oRSkpKAq+grRw78QrK0thvG2VseJ+VLS0txbZt2+ikBYeoVCrMmzcP1dXVfrr3WbB2/nlsPnk/dgr+Dw9eOImN07lrX0NDA4Db+17/+c9/4uuvv/Z67uzZs5GcnAwASEpKuqNEFXya65/97GeEYRjeu313QxGLxSQ5OZmcO3duzPtSt2YNqSOEkAtlJA2Df7NAT08PsVqtxGg0Ep1OR2JjYwkAwjAMYRiGiESigD6TUqkkDMMQpVJJABCtVkuys7OJ0WgMqQsfDvAizOvXr5NHH3106MbQMn4lLi6OHDlyxOe9cY8rb5fQhHnu3Dny8ssvk+TkZCISicbtQcwwDImNjSUZGRnEZDKR9vb2ED7F+DPuwmxvbycpKSlEIpHw/iO9W8uUKVPIO++843lzLpSRNcOXRurWEKSVkUAXSxobG0lhYSFJSEggKpUqYEvIdpHL5USpVJLExERiMpnI9evXQ/kJjwvjKsyzZ8+SxMRE3n+YtIBMnDiRvP3227dvzoUykjZChBfK0vwWZk9PDzGbzSQxMTGshydyuZwoFAqSnZ3N8novu4ybMM+ePUs0Gg3vN4aW2yUuLm5InAPH0siAwbxAytKGn+u7O9vZ2Uk2bdpEJkyYMDTWi5TCMAxJSEggZrN5nFTgP+MizPb2dmopw7TExcWRjRs3BnxPOzs7ycqVK4lcLue9qxpqUSqVYSdQzoXZ3t5OkpKSeP/yafFdGIbxW5w9PT3EaDSS+Ph4IhaLeW87m8Ut0JqaGk414Q+cCpOKMnIKwzBk165do97PqqoqotFoiEKh4L29XBa1Wk10Oh2vM7mcCfP69eskJSWF9y+ZFv/LlClTvC6luDcVqFQq3ts4noVhGGI0GklPTw9XMvEJJ54/DocDjzzyCFpaWtiuOuqRy+WQSCQecW6Tk5OhVqvvODcnJwcA8NFHH+G///0vrl27ht7eXvT19QUdnnPKlCloaGgYiixfX1+PZ555Bt98802QnyiyUSgUiI+Px5EjR8Y1ciEnwnzqqafw/vvvo6+vj+2qowaGYYYiuLtFl5OTM5RuQavVshodr6GhAXa7Hc3Nzaivr4fNZsNXX30FAIiJiUF/f//QuSqVCrt27cJf/vIXfPTRR9RdEkB8fDxKSkqwevXqcbke68Kkvq+eiEQiKJVKdHV1ITU1FUuXLkV6enpY5Dxxi7WjowN//etfceLECXz33XdwuVx3iJUyEFZ00aJFOHjwIPfpLtjsFzc2NpKEhATexwZ8F7frmUgkIjqdjpSUlESU7+bhw4eJRqMharWayOVyIpVKSUxMDO/fazgUsVhMZsyY4ZfPcSiwJszOzk4ybdo03r84vopMJiNKpZJotdqIE+JwTCYTSU9PJ+3t7aSqqoqsWLGCnDt3jphMJpKZmUkkEknULZMEU6ZMmULOnj3L2X1gRZg9PT0kNTWV9y+Lj6JWq0liYiLZvn17xDlKD6enp4esWLGC/PKXvyQ9PT3k448/Jh9//DHZtGkTKS0tveM8s9k8tDtEKBTyfg/4KhqNhrMHMCvCNBqNUb+2NVKMKpWKPPfcc5w+NceLc+fOEa1WS6qqqoYE6S49PT0kMzOTWK1Wj/e1t7eT7du3k8TERCKXy3m/L3yUhISEUXfrBEvIwmxvbw9rp2U2i1KpJNnZ2WHhGcIWVVVVRKfTkXPnznmI0l06OzuJVqslnZ2dPus5e/Ysee6554hSqSRSqZT3ezWexdf6byiEPCs7b948NDU1hVJFWCMSiSCXy5Gbm4utW7dGzU55h8OBoqIi2Gw2mEymMdecRSIRioqKYLFYRp2RdDgceOutt1BSUoLu7m5Oco6GI1OmTMHevXvx5JNPslJfSMLct28fNm/eHJVxemQyGWQyGX7yk59g27Zt0Gg0fDeJNWw2G/R6PXJycpCamur3+86cOYOLFy+itLR0zHMdDgcqKirw6quvoru7Gz09PaE0OSKIj4/Hhx9+yIojQtDCtNlsmDt3Lmw2W8iNCCcUCgViYmKwbt06bNmyJepS4NXX18NgMMBsNge1Trl//34sXLgwoLQMFRUV2Lx5M3p6etDd3R3wNSOJpKQkWK3WkHtWQUfJW7duXVS5aYnFYjAMg+LiYvzvf//Djh07ok6UxcXFKC8vR2NjY9DOAyaTCZWVlQENX55//nlcuXIFBw4cQHJyMmJjY4O6diTQ0dGBH/zgByFnnwvKYjY3NyM9PT1qurAMw2DZsmV46623ok6MwIB3T15eHhYuXIglS5aEXN/UqVORl5cHq9Ua1Pf1u9/9Djt27IDdbofT6Qy5PeGGRCKBVqvF6dOng64jKGEuWrQIVqs16IuGCxMmTMC9994Ls9kctQl5mpqaUFBQAJPJBJFIxFq9/f392Lp1a9C/A5vNhnXr1uH48eNR84AfjkqlwpYtW1BUVBTU+wMWZjRYS7FYDIVCgW3btmH9+vV8N4czysrKcOTIEVRVVaGtrY31+v/xj3/gxo0bwQWUHqShoQErV67E9evXo26CKJTJoIDHmC+++GJEi5JhGOTn56O9vT1qRWm326HX63Hx4kVYLBZORAkACxYsQGtrK2pra4OuIz09HZcvX8ZvfvMbxMfHs9e4MOCbb75BTk5OUOPNgCxmJFtLsViMyZMn47333ovabisAtLa2oqCgABs3bsTUqVM5v96sWbOQlZUFs9kc8k4Zm82GvLw8fPbZZ7h58yZLLeQXsViM/Px8HDhwIKD3BWQxI9VaMgyDRYsWoaWlJapFWVFRAb1eD7PZPC6iBICWlhaYzWbo9fqQZyI1Gg0aGxuxefPmqJmE6+vrw9GjR1FfXx/Q+/y2mE1NTcjKyoq4fZZxcXF45ZVXYDAY+G4KZzgcDhgMBjgcDphMJpw5c2bc23D16lVUVlaipqaGlfqamprw5JNP4sqVK6zUxzfJycn48ssv/T7fb4tpMpkiSpRisRjTpk3DyZMno1qUHR0dyMjIwJw5c/Dzn/+cF1ECQEJCAlJSUrBjxw5W6tPpdDh//jx0Oh3kcjkrdfLJ1atX8fvf/97v8/0SpsPhwKFDkZO+dOLEifjhD3+IL774gvcIAVxSW1uLvLw8mEwmzJo1i+/mYNmyZTh27NhQ9q5QYRgmarq23333HYqLi/3u7vslzOrqalbXwLgkISEB5eXlOHz4MPfhH3ikqKgIlZWVsFqtQQfe4oKamhoYDAZ0dHSwVmdxcTEsFgsSExNZq5MPent7/e5R+DXGnDlzJlpbW0NuGNdoNBpUVVUhPT2d76ZwhtsBfenSpViwYAHfzfGKvztRAqWjowNZWVkR8Vv0hUqlwhdffDHmpogxLWZra6vPJKLhRGJiIiwWS1SL0j0BZzQaw1aUwIBX0KpVq1BYWMhqvUlJSWhsbERqamrE9OBG4nQ6/Vo6GVOYe/bsCfs1pXvvvRcnT54c17if482OHTtgMBhgsVgi4kc5ffp0yGQy7N69m9V6GYbBxx9/jLS0tIh0hu/u7kZJScmYY80xhXngwIGwDWMokUiQkpKClpaWqNnAPBK3A/qNGzewc+dOzrx4uKC0tBTvvvsu6xvpZTIZTpw4gSeeeMIjCHYk4HQ6x1zXHHWM2dHRgYceeiisJhfcyOVyzJkzB3V1dRE/Y+eL5uZmFBQUwGg0IiEhge/mBMXUqVOh1+tRU1PDyWbzTZs2oaKiIuIcX8Za1xzVYtbX18PlcrHeqFCRSCSYM2cOjh8/HrWi3L17NwoLC1FTUxOxogSAS5cuoaSkBAUFBSF7BnmjrKwMRqMREydOZL1uLrl27Rqam5t9vj6qMA8dOhSW48v7778fx48fj8rlEIfDgYKCAnz66aewWq24dOkS300KGZFIhKVLlwa9BWosDAYDnnvuuYjq1nZ3d6OystLn6z67sg6HAxMnTuTkKRcKKSkpaGxsjEpL6XZAX7t2LaZPn853c1jn7bffRk5ODlasWMFJ/Xq9HrW1tWH3m/VFQkKCT5dDnxazqakp7CzStGnTgt41H+7U1tYObWiORlECgNlsxmuvvcbZOmRVVRUeffTRiJi1BgaWlXxNjPkU5ocffhhWkz6JiYk4ceJEVEWrc2MwGFBZWQmLxRJW3znbnDlzBjU1NdDr9Zz5XR87dgyPPPIIxGIxJ/WzSVdXF959912vr/nsyoZT+BCNRgOLxRJ165Tu/YdPP/10QGEkIx22d6KMxG63Y968eRHhIeSrO+vTYv7rX//itEH+kpCQgJqamqgTZUNDAzIyMlBaWnpXiRIYuKdz5sxBcXExJ/UzDAOr1Ypp06ZxUj+bOBwOr37FXoVpt9vDIoK2SqXCiy++GHWbm4uLi7F169aQwkhGOkuWLMHp06cD3kDsLxqNBseOHUNcXBwn9bOJt904XoXZ3NzMu7uTWCyGVqvlbIqdD+x2O7KysgAA27dvHzMtQbRTVVWFoqIiVneiDMe9P1SlUnFSPxt0dXXhk08+8TjuU5h8RyybPHkyjhw5wmsb2KSpqQkZGRnYuHEjK7Fdo4GWlhZUVVUhLy+PsyWO1atXY/HixWE9U+ut1+BVmGfPnuW1K5uQkID33nsvapZFdu/ejaKiItTU1ECpVPLdnLDi6tWreOmll1BQUMDZNQ4ePBjWezm/+uorjweTV2F+9tln49Igb0TTuNLhcECv1+PTTz+FxWKJCi8eLpg6dSo0Gg3Kyso4qV8mk4X1eFOpVHq453kVJl8/ILFYjMWLF0fFuLK1tRXz5s1DTk4OVq5cyVssnkihpKSE1bAkIwnn8abL5fIYZ/ucleWD+Ph4HDx4kJdrs0l1dTX0ej2qqqrGLYxkpHPmzBmYzWYYDAbOMsi5x5tCoZCT+oOlt7fX4zPfIUzi6se3Xd9BoIoF1ApgHD3yGIaB2WwOOzfAQHA4HCgsLMSRI0fQ2NiIq1ev8t2kiKKtrQ0mk4mVGLW++OMf/xh2Xdre3l6cP3/+jmN3CFMQI8K1K/+DTCwGbnYD4+QLLBaL8dhjjyEzM3N8LsgB7ng0c+bMwQsvvEC7rkHS39+PnJwczoYzDMPgjTfeCLsu7UgvJY+ubMfFi4iJCTptZlCoVCpUVFSM6zXZpL6+Hnl5eSgpKQmLMJKRTmpqKux2O2e/iWeffRbf//73Oak7WDo7O+/432Nxx27/dvAvMaCWDPzpugV81wfIZIBUCPQOWlOxGJAPntM7zMIOPw4ncGOY6R3x2gSXGEaj0dM5nThxy+kCAQBBDCRCIQQjj0MAsUiEGBD09/fDCcHA+MHZDycAxIggFbhunx8jgjRGENAXNhbFxcU4ffo0rFbrXe8wwCYmkwlZWVlISUnhZIbebDZj8eLFuHbtGut1B8PIYY+HabTbrw9GLegDbt4C4BwQJQA4nED/rUEBigfGoDe6gRu3AKkMEA8elwsHusI3uoF+IaBwe/p7vjbpnslesm4R9DsJYoRiSEUiCOGCk4w8LoY4hqDPRQAIIBLGQAACp9OJGJEYUmEMBK5+9LoA8dD/TrAVj8FutyMjIwMA8PLLL1NRsox7MqiwsJCTyUitVouVK1eG7S6U0fusfX1AL24LSyYEnH23/46RDEwSqSUAhIBw8LjLCQyehlu3AJHw9nuGvaaWSlFd9a6nWIgTzhghRAJgQHTigb+JE06BAMJBoxcjGCk2AYRC0bAPJYAwZtDSDuIKOE2vJ01NTZg3bx6MRiP14uGQS5cuobS0FHl5eZzU//rrr2PChAmc1B0qvoXpFpbDOSAsMQCh884Jof5bgxZzsLhfixllOnrwNZFIhPzlyzFn9hzv5xF3d3XkceL9+DhRVlYGg8EAq9Ua1m5e0QKXYUlkMhn2798flt5YQ8J0ufrQ6yIDyxXEOeyUQaspV9y2lsCgYCXDllQGu7YOJwDh7eNCIdDvvP2ewddUKhXeLC8FiRF6eToIICDu7isAkAFLJxBCCIL+IbNHfLyffdxhJC9evBhxYSQjHTYS5PoiNzc3LCfshn7TAggAVz/iJ09GTP+ICRKHE8AIa4k+oNcJSBWD3Vnh4OuDY1P3cbET6O67/Z6bt6BQxWHz/9sCgUQGydBkDEF/fx/6BwUoEQrgdPaht78Pvf1OYKhbGwO4+geOuwSD7yfod7pAQOB09sM15v+B0dzcjKysLKxatQrLly8P8N0UNuAyLInJZAo7q+kRwaChoQF5eXkjBtxiQNbH2rpmXFwcLl++HBHOBBUVFdizZw/MZjN1GOCZhIQE6PV6NDY2sv7b4Ttix8hIBv71AmVCwDn2af6gUChQVFQU9qJ0e/GcOHECVquVijIMuHr1KoxGI/R6Pet179q1i1erOdJ100OYdwhGMdgdheP2ZFCISKVS/OpXv2KnMo4Il2SwFE/YTpDrRqvVYtKkSazWGQgjDZWHMDUaze1wF90jZltDJBKsZbglg6V4wnaCXDe7du3iLXv1mBYzKSmJsxCK4W4twzUZLMUTLhLk5ubmYsqUKazVFwgjrbXXMSYXDr7hbC1tNhvmzZsHtVqNX//619SLJwJoaWmByWRiPScKH1ZTLpdj9uzZdxzzKsz4+HjWLy4UCvHMM8+wXm+oNDQ0ICsrC6WlpWGdDJbiCRcJcnNzczF58mTW6vMHiUTiEZ7VqzC5MOcPP/xw2EVR37FjB7Zu3QqLxXLXhpGMdLhIkPvSSy+Nq1dXT08PUlJS7jjmVZhs77pXq9XYsGEDq3WGwvBksNu3b6dePBEO2wlyc3NzIZFIxj6RJaRSqUfgOa/CfOCBB1h9YgiFwrDZBN3c3IyMjAysWrUKy5Yt47s5FBZgOyyJRqPBY489xkLL/OPBBx/0OOZVmAsWLGBtsVUkEmH58uVhMekTLclgKZ6wnSB3w4YNkEqlLLRsdEQiERYvXuxx3KswdTodawGfFQoF1q1bx0pdwRKNyWApnrC5EyUzM3NcjIlSqfS6ddCrMBmGYW1m9p577uE1IZA7jOTChQtpGMm7gNTUVNhsNlRXV4dUj0wmQ05ODkut8o3D4fAaocGnrywbYpLL5di4cWPI9QRLdXU1CgoKUFVVFbXJYCmesLUTpbCwkHOrqdVqvV7DpzCfeOKJkPvYTqeTs7Teo+FwOGAwGHDkyBFYLBbqgH6XwVaCXJ1Ox2mEA6lUiqefftrraz6FqdPpQs74NXPmzHHPP2Kz2ZCVlYX77rsPL7zwAvXiuUu5dOkSjEZjyDlR1qxZw1KLPJFKpT5XK0btyjqdwe/1EolE474c4U4GW1JSctclg6V4wkaC3CVLlnAWsCs+Pt7DscCNT2HKZDI8/vjjQV/U12wTV9BksBRvhJogV6fTcZJSYazVilE3Sj/77LNBO7Q7nc5xydhFk8FSxiKUBLkymQxz585lv1EAfvGLX/h8bVRhZmZmBt2dTU1N5XxGyx1GkiaDpYxGqAly8/PzIRCwGyh84cKFo86/jCpMhmHw0EMPBXxRqVSKJ598MuD3BUJZWRmKiopgtVrDLpASJfwIJUFueno6q+NMhmG8BDm/kzFj/qxatSrg/WmjzTaFit1uh16vx8WLF2GxWKgDOsVvgk2Qy7aDzMSJE8fUx5jCzM3NDfjCEonE52xTKLS2tiIrKws5OTlYvnw59eKhBEywCXLZGmeq1Wrs2rVrzPPGFGYwnvaPPvpoQOf7gzsZrNlspslgKUET7E6UH//4x6xcPy4uzi9j51f4yg0bNkCtVvt1YZFIFNIyy0hoMlgK2wSTIHfJkiUhp6f011oCXgI+++Lee+/F119/7dfFKyoqguoCj6SjowMFBQV4+umnacQ6CuucOXMGFy9eRGlp6ZjnOhwOyOVy+CkXryQnJ+PLL7/061y/HwEGgwEKhWLM8wghrIwvaTJYCtcEkiBXJpOFtPynVqtx+PBhv8/3W5ijLYYO5+bNmyELs6ioCOXl5bBardSLh8IpJpMJlZWVfoUlCTZmlUgkQn5+fkCzu34Lk2EY/PSnPx0z5Mj3vvc9vy8+EpvNhoyMDKjVapoMljIuBJIgN9iZWZVKhTfffDOg9wQ0ml23bt2Y3dmZM2cG1AA3TU1NyMrKgtFopGEkKeOKvwlytVptwB5AarUa+/fvD7gbHJAwtVot8vPzfVrNYGdk3clgLRYLTQZL4QV/wpI88sgjAdUpFouxaNGioCZCA57/3bZtm09PIIVCEdD4kiaDpYQTYyXITUlJCchi3nPPPTh48GBQbQlYmBqNBqtWrfJqmgOZkaXJYCnhyGhhSZKSkvxeLomLi8OxY8eCnskNasX09ddf92o1XS6XXw2pqKhAYWEhzGYzDSNJCStaWlpgNpt9Oh/4EwharVZjx44dIa1OBCVMmUyGoqIij4kgh8Mx6pSyO4wkTQZLCWdGS5A7Vhws97hy9erVIbXBb8+fkTgcDsyePRsXLly447iv6lpbW1FQUIC1a9fSiHWUiODo0aNQq9XYsmXL0LH4+Hhcu3bN6/kSiQQPP/wwjh8/HvJe5KCd/2QyGd555507Nnv6inZQW1uLgoICmEwmKkpKxOAtQe5oET1mz57NiiiBEIQJDMRDefbZZ4ca4q3/bTAYUFlZCYvFQpPBUiKOkQlyfW2YTklJwQcffMBa1I7Q3OUxMBHk9vYZvh3LnQz2vvvuo8lgKRHLyAS53nZZpaSkoLGxkdVQrSELUyaT4dChQ3eY+OHJYGkYSUqkMzxB7vAJT3dAALZFCbAgTGDAIygtLQ0zZsygyWApUYk7Qa5UKoVQKERsbCy0Wi0nogQCEKZlrQACwYgyvxxuX50lS5bAarWitbUVf//736kXDyXqKC0txX/+8x/I5XLk5OTg9OnTnGUaCGi5pK18PmZ8/grIniwAFqwVZGPvmjqQPVk4cOAAtmzZgsuXL7PeSK1Wy8oXkJ6ePvR3RUVFUHFGKRSRSIS+vj5OrxGAMNtQPn8GPn+FYM9AfGVY1gqQjbpBoVIo0U9HRwc6OjrueMgDACxrIcjeO/TvmjqCPViLtdiDYOTh/xiz7SgOnVqD3MGLtJXPR/beNJRtpqKk3D0kJSWNEGUbyucLIMgG6ggBGSy5tQIIslvw4IzgruO3MNuOHsIp7EX24PhyFSpByElspP4ClLuWgV7kJpThAtmD4SYqa08d1mAW7g9SH34Ksw1HD53CmrrBJ8KFMmDTDMwvpxM8lLuXtvJV2HQqDWWVG+Gpvxl4sCwXwfYn/RtjtpVj/ozP8cqwp4JlrQDZe9egbsSTgkK5Oxi0lrO4mWPxy2K2HT2EU2uGq78N51sApD2IILvQFEqEcwGfnwLSgh1EjoEfcTwGu7Gv3JalZe0MbDoFrKnzZsIplLuAtvPg0sl0TIspEAyIcG/2bceC7L1pKLtAgpoGplCigun3YxaAU59f8Pqypfy2800wBL0fk0K56xlcu0wru4CTQ8sTnuv9wUCFSaGEQls55s/YhFNDB9JQdiH0ZUQqTAolDGFldwmFQmEXKkwKJQz5/6GFNae7ZI7lAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x228D534C5F8>"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x1B2ED7C31D0>"
       ]
      },
      "metadata": {},
@@ -147,8 +147,7 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "(True,\n",
-       " '如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\\\SIFChoice$$\\\\FigureID{1}$')"
+       "True"
       ]
      },
      "metadata": {},
@@ -176,7 +175,7 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "(False, '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...')"
+       "False"
       ]
      },
      "metadata": {},
@@ -373,7 +372,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x228D534C5F8>"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x1B2ED7C31D0>"
       ]
      },
      "metadata": {},
@@ -768,16 +767,16 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[<networkx.classes.digraph.DiGraph at 0x228d54e4a20>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085ea208>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085f25f8>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085f2eb8>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085ef208>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085f3d68>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085f3f60>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085f39e8>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085f3c50>,\n",
-       " <networkx.classes.digraph.DiGraph at 0x229085f3828>]"
+       "[<networkx.classes.digraph.DiGraph at 0x1b308559c88>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b30c827710>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b30c827748>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b307f09748>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b308558c88>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b308558a58>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b308558da0>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b308558080>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b307fd6b00>,\n",
+       " <networkx.classes.digraph.DiGraph at 0x1b308556ac8>]"
       ]
      },
      "metadata": {},
@@ -790,25 +789,13 @@
    "cell_type": "code",
    "execution_count": 20,
    "source": [
-    "for i in range(0, len(f)):\n",
-    "    ForestPlotter().export(\n",
-    "        f[i], root_list=[node for node in f[i]],\n",
-    "    )\n",
-    "# plt.show()\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "error",
-     "ename": "NameError",
-     "evalue": "name 'ForestPlotter' is not defined",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-20-b34613665c62>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     ForestPlotter().export(\n\u001b[0m\u001b[0;32m      3\u001b[0m         \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroot_list\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnode\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     )\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# plt.show()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'ForestPlotter' is not defined"
-     ]
-    }
+    "# for i in range(0, len(f)):\r\n",
+    "#     ForestPlotter().export(\r\n",
+    "#         f[i], root_list=[node for node in f[i]],\r\n",
+    "#     )\r\n",
+    "# plt.show()\r\n"
    ],
+   "outputs": [],
    "metadata": {}
   },
   {
@@ -821,22 +808,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\":{\n",
-    "            \"method\": \"ast\",\n",
-    "            \"return_type\": \"list\",\n",
-    "            \"ord2token\": True,\n",
-    "        }\n",
-    "    }\n",
+   "execution_count": 21,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"return_type\": \"list\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "        }\r\n",
+    "    }\r\n",
     ").formula_tokens"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -853,23 +888,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "sif4sci(\n",
-    "    item[\"stem\"],\n",
-    "    figures=figures,\n",
-    "    tokenization=True,\n",
-    "    tokenization_params={\n",
-    "        \"formula_params\":{\n",
-    "            \"method\": \"ast\",\n",
-    "            \"ord2token\": True,\n",
-    "            \"return_type\": \"list\",\n",
-    "            \"var_numbering\": True\n",
-    "        }\n",
-    "    }\n",
+   "execution_count": 22,
+   "source": [
+    "sif4sci(\r\n",
+    "    item[\"stem\"],\r\n",
+    "    figures=figures,\r\n",
+    "    tokenization=True,\r\n",
+    "    tokenization_params={\r\n",
+    "        \"formula_params\":{\r\n",
+    "            \"method\": \"ast\",\r\n",
+    "            \"ord2token\": True,\r\n",
+    "            \"return_type\": \"list\",\r\n",
+    "            \"var_numbering\": True\r\n",
+    "        }\r\n",
+    "    }\r\n",
     ").formula_tokens"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_0',\n",
+       " 'mathord_2',\n",
+       " '\\\\bigtriangleup',\n",
+       " 'mathord_0',\n",
+       " 'mathord_1',\n",
+       " 'mathord_2',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " ',',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_3',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub',\n",
+       " ',',\n",
+       " 'mathord_4',\n",
+       " 'textord',\n",
+       " '\\\\supsub']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -893,12 +976,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\n",
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\r\n",
     "        symbol=\"fgm\")"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['如图', '古希腊', '数学家', '希波', '克拉底', '研究', '几何图形', '此图', '三个', '半圆', '三个', '半圆', '直径', '直角三角形', '[FORMULA]', '斜边', '[FORMULA]', '直角', '[FORMULA]', '[FORMULA]', '[FORMULA]', '三边', '围成', '区域', '记', '[FORMULA]', '黑色', '记', '[FORMULA]', '其余部分', '记', '[FORMULA]', '图形', '中', '随机', '取', '一点', '此点', '取自', '[FORMULA]', '概率', '记', '[FORMULA]', '[MARK]', '[FIGURE]']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {

From d1fbd7f9e959cd84115e6313f1a6fe01980da6b0 Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Sun, 26 Sep 2021 20:08:03 +0800
Subject: [PATCH 137/149] [feat] add bert model

---
 EduNLP/Pretrain/bert_vec.py |  6 +++--
 EduNLP/Vector/__init__.py   |  1 +
 EduNLP/Vector/bert_vec.py   | 54 +++++++++++++++++++++++++++++++++++++
 EduNLP/Vector/t2v.py        |  4 ++-
 tests/test_vec/test_bert.py | 13 +++++++++
 5 files changed, 75 insertions(+), 3 deletions(-)
 create mode 100644 EduNLP/Vector/bert_vec.py

diff --git a/EduNLP/Pretrain/bert_vec.py b/EduNLP/Pretrain/bert_vec.py
index 3b73a3bb..0e243090 100644
--- a/EduNLP/Pretrain/bert_vec.py
+++ b/EduNLP/Pretrain/bert_vec.py
@@ -3,11 +3,13 @@
 import transformers
 from EduNLP.Tokenizer import PureTextTokenizer
 from copy import deepcopy
+from typing import Optional, Union
 import itertools as it
 from EduNLP.SIF.sif import sif4sci
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 from transformers import DataCollatorForLanguageModeling
 from transformers import Trainer, TrainingArguments
+from transformers.file_utils import TensorType
 from torch.utils.data import Dataset
 from EduNLP.SIF import Symbol, FORMULA_SYMBOL, FIGURE_SYMBOL, QUES_MARK_SYMBOL, TAG_SYMBOL, SEP_SYMBOL
 
@@ -46,10 +48,10 @@ def __init__(self, pretrain_model="bert-base-chinese"):
         if customize_tokens:
             self.tokenizer.add_special_tokens({'additional_special_tokens': customize_tokens})
 
-    def __call__(self, item):
+    def __call__(self, item, return_tensors: Optional[Union[str, TensorType]] = None):
         self.pure_text_tokenizer = PureTextTokenizer()
         item = ''.join(next(self.pure_text_tokenizer([item])))
-        return self.tokenizer(item, truncation=True, padding=True)
+        return self.tokenizer(item, truncation=True, padding=True, return_tensors=return_tensors)
 
     def tokenize(self, item):
         self.pure_text_tokenizer = PureTextTokenizer()
diff --git a/EduNLP/Vector/__init__.py b/EduNLP/Vector/__init__.py
index efccc5ad..01a7ba05 100644
--- a/EduNLP/Vector/__init__.py
+++ b/EduNLP/Vector/__init__.py
@@ -6,3 +6,4 @@
 from .rnn import RNNModel
 from .t2v import T2V, get_pretrained_t2v
 from .embedding import Embedding
+from .bert_vec import BertModel
diff --git a/EduNLP/Vector/bert_vec.py b/EduNLP/Vector/bert_vec.py
new file mode 100644
index 00000000..9a8a02c8
--- /dev/null
+++ b/EduNLP/Vector/bert_vec.py
@@ -0,0 +1,54 @@
+import numpy as np
+from pathlib import PurePath
+from transformers import AutoModel
+from EduNLP.Pretrain import BertTokenizer
+from .const import UNK, PAD
+from .meta import Vector
+import torch
+
+
+class BertModel(Vector):
+    """
+    Examples
+    --------
+    >>> tokenizer = BertTokenizer("bert-base-chinese")
+    >>> model = BertModel("bert-base-chinese", tokenizer=tokenizer)
+    >>> item = "有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$，若$x,y$满足约束"
+    >>> inputs = tokenizer(item, return_tensors='pt')
+    >>> output = model(inputs)
+    >>> output.shape
+    torch.Size([1, 12, 768])
+    >>> tokens = model.infer_tokens(inputs)
+    >>> tokens.shape
+    torch.Size([1, 10, 768])
+    >>> tokens = model.infer_tokens(inputs, return_special_tokens=True)
+    >>> tokens.shape
+    torch.Size([1, 12, 768])
+    >>> item = model.infer_vector(inputs)
+    >>> item.shape
+    torch.Size([1, 768])
+    """
+    def __init__(self, pretrained_model, tokenizer=None):
+        self.model = AutoModel.from_pretrained(pretrained_model)
+        if tokenizer:
+            self.model.resize_token_embeddings(len(tokenizer.tokenizer))
+
+    def __call__(self, items):
+        # 1, sent_len, embedding_size
+        tokens = self.model(**items).last_hidden_state
+        return tokens
+
+    def infer_vector(self, items) -> torch.Tensor:
+        vector = self(items)
+        return vector[:, 0, :]
+
+    def infer_tokens(self, items, return_special_tokens=False) -> torch.Tensor:
+        tokens = self(items)
+        if return_special_tokens:
+            return tokens
+        else:
+            return tokens[:, 1:-1, :]
+
+    @property
+    def vector_size(self):
+        return self.model.config.hidden_size
diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index ec0887ef..b698f756 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -6,6 +6,7 @@
 from EduData import get_data
 from .rnn import RNNModel
 from .gensim_vec import W2V, D2V
+from .bert_vec import BertModel
 from .meta import Vector
 from EduNLP.constant import MODEL_DIR
 
@@ -15,7 +16,8 @@
     "rnn": RNNModel,
     "lstm": RNNModel,
     "gru": RNNModel,
-    "elmo": RNNModel
+    "elmo": RNNModel,
+    'bert': BertModel
 }
 
 
diff --git a/tests/test_vec/test_bert.py b/tests/test_vec/test_bert.py
index 1547cfc6..28e7c34e 100644
--- a/tests/test_vec/test_bert.py
+++ b/tests/test_vec/test_bert.py
@@ -2,6 +2,7 @@
 import numpy as np
 import pytest
 from EduNLP.Pretrain import BertTokenizer, finetune_bert
+from EduNLP.Vector import BertModel, T2V
 
 
 @pytest.fixture(scope="module")
@@ -29,6 +30,18 @@ def test_bert_without_param(stem_data_bert, tmpdir):
         stem_data_bert,
         output_dir
     )
+    tokenizer = BertTokenizer(output_dir)
+    model = BertModel(output_dir)
+    item = {'stem': '如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$, \
+            若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'}
+    inputs = tokenizer(item['stem'], return_tensors='pt')
+    output = model(inputs)
+    assert model.vector_size > 0
+    assert output.shape[-1] == model.vector_size
+    t2v = T2V('bert', output_dir)
+    assert t2v(inputs).shape[-1] == t2v.vector_size
+    assert t2v.infer_vector(inputs).shape[-1] == t2v.vector_size
+    assert t2v.infer_tokens(inputs).shape[-1] == t2v.vector_size
 
 
 def test_bert(stem_data_bert, tmpdir):

From 92329b6003d05cdbbdacab3b03c9386f1aee65cf Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Tue, 28 Sep 2021 21:12:48 +0800
Subject: [PATCH 138/149] [feat] support multiple samples

---
 EduNLP/Pretrain/bert_vec.py | 29 ++++++++++++++++++++++-------
 EduNLP/Vector/bert_vec.py   | 19 +++++++++++--------
 2 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/EduNLP/Pretrain/bert_vec.py b/EduNLP/Pretrain/bert_vec.py
index 0e243090..4ebbc70e 100644
--- a/EduNLP/Pretrain/bert_vec.py
+++ b/EduNLP/Pretrain/bert_vec.py
@@ -38,6 +38,12 @@ class BertTokenizer(object):
     [101, 1062, 2466, 1963, 1745, 21129, 166, 117, 167, 5276]
     >>> print(tokenizer.tokenize(item)[:10])
     ['公', '式', '如', '图', '[FIGURE]', 'x', ',', 'y', '约', '束']
+    >>> items = [item, item]
+    >>> token_items = tokenizer(items, return_tensors='pt')
+    >>> print(token_items.input_ids.shape)
+    torch.Size([2, 27])
+    >>> print(len(tokenizer.tokenize(items)))
+    2
     """
     def __init__(self, pretrain_model="bert-base-chinese"):
         self.tokenizer = AutoTokenizer.from_pretrained(pretrain_model)
@@ -47,16 +53,25 @@ def __init__(self, pretrain_model="bert-base-chinese"):
                 customize_tokens.append(Symbol(i))
         if customize_tokens:
             self.tokenizer.add_special_tokens({'additional_special_tokens': customize_tokens})
-
-    def __call__(self, item, return_tensors: Optional[Union[str, TensorType]] = None):
         self.pure_text_tokenizer = PureTextTokenizer()
-        item = ''.join(next(self.pure_text_tokenizer([item])))
+
+    def __call__(self, item: (list, str), return_tensors: Optional[Union[str, TensorType]] = None):
+        if isinstance(item, str):
+            item = ''.join(next(self.pure_text_tokenizer([item])))
+        else:
+            token_generation = self.pure_text_tokenizer(item)
+            item = [''.join(next(token_generation)) for i in range(len(item))]
         return self.tokenizer(item, truncation=True, padding=True, return_tensors=return_tensors)
 
-    def tokenize(self, item):
-        self.pure_text_tokenizer = PureTextTokenizer()
-        item = ''.join(next(self.pure_text_tokenizer([item])))
-        return self.tokenizer.tokenize(item)
+    def tokenize(self, item: (list, str)):
+        if isinstance(item, str):
+            item = ''.join(next(self.pure_text_tokenizer([item])))
+            return self.tokenizer.tokenize(item)
+        else:
+            token_generation = self.pure_text_tokenizer(item)
+            item = [''.join(next(token_generation)) for i in range(len(item))]
+            item = [self.tokenizer.tokenize(i) for i in item]
+            return item
 
 
 class FinetuneDataset(Dataset):
diff --git a/EduNLP/Vector/bert_vec.py b/EduNLP/Vector/bert_vec.py
index 9a8a02c8..ea8a242d 100644
--- a/EduNLP/Vector/bert_vec.py
+++ b/EduNLP/Vector/bert_vec.py
@@ -13,40 +13,43 @@ class BertModel(Vector):
     --------
     >>> tokenizer = BertTokenizer("bert-base-chinese")
     >>> model = BertModel("bert-base-chinese", tokenizer=tokenizer)
-    >>> item = "有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$，若$x,y$满足约束"
+    >>> item = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$，若$x,y$满足约束",
+    ... "有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$，若$x,y$满足约束"]
     >>> inputs = tokenizer(item, return_tensors='pt')
     >>> output = model(inputs)
     >>> output.shape
-    torch.Size([1, 12, 768])
+    torch.Size([2, 12, 768])
     >>> tokens = model.infer_tokens(inputs)
     >>> tokens.shape
-    torch.Size([1, 10, 768])
+    torch.Size([2, 10, 768])
     >>> tokens = model.infer_tokens(inputs, return_special_tokens=True)
     >>> tokens.shape
-    torch.Size([1, 12, 768])
+    torch.Size([2, 12, 768])
     >>> item = model.infer_vector(inputs)
     >>> item.shape
-    torch.Size([1, 768])
+    torch.Size([2, 768])
     """
     def __init__(self, pretrained_model, tokenizer=None):
         self.model = AutoModel.from_pretrained(pretrained_model)
         if tokenizer:
             self.model.resize_token_embeddings(len(tokenizer.tokenizer))
 
-    def __call__(self, items):
+    def __call__(self, items: dict):
         # 1, sent_len, embedding_size
         tokens = self.model(**items).last_hidden_state
         return tokens
 
-    def infer_vector(self, items) -> torch.Tensor:
+    def infer_vector(self, items: dict) -> torch.Tensor:
         vector = self(items)
         return vector[:, 0, :]
 
-    def infer_tokens(self, items, return_special_tokens=False) -> torch.Tensor:
+    def infer_tokens(self, items: dict, return_special_tokens=False) -> torch.Tensor:
         tokens = self(items)
         if return_special_tokens:
+            # include embedding of [CLS] and [SEP]
             return tokens
         else:
+            # ignore embedding of [CLS] and [SEP]
             return tokens[:, 1:-1, :]
 
     @property

From 1d3889b7625b69305d6024ce5cf8f6590c50e2e9 Mon Sep 17 00:00:00 2001
From: QinLonghu <1097824882@qq.com>
Date: Sun, 10 Oct 2021 12:44:50 +0800
Subject: [PATCH 139/149] change the param name in sif.py and update examples

---
 EduNLP/SIF/sif.py               |  16 +-
 examples/sif/sif_addition.ipynb | 617 +++++++++++++++++---------------
 tests/test_sif/test_sif.py      |   2 +-
 3 files changed, 344 insertions(+), 291 deletions(-)

diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index 9c9a650e..ed5c99cd 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -55,15 +55,15 @@ def is_sif(item, check_formula=True, return_parser=False):
         return ret
 
 
-def to_sif(item, check_formula=True, cache_parser: Parser = None):
+def to_sif(item, check_formula=True, parser: Parser = None):
     r"""
     Parameters
     ----------
     item: str
     check_formula: bool
-        whether to check the formulas when parsing item (only work when cache_parser=None).
-    cache_parser: Parser
-        the saved parser of item from is_sif.
+        whether to check the formulas when parsing item (only work when parser=None).
+    parser: Parser
+        the parser of item returned from is_sif.
 
     Returns
     -------
@@ -79,12 +79,12 @@ def to_sif(item, check_formula=True, cache_parser: Parser = None):
     >>> ret = is_sif(text, return_parser=True)
     >>> ret # doctest: +ELLIPSIS
     (False, <EduNLP.SIF.parser.parser.Parser object...>)
-    >>> to_sif(text, cache_parser=ret[1])
+    >>> to_sif(text, parser=ret[1])
     '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...
 
     """
-    if cache_parser is not None:
-        return cache_parser.text
+    if parser is not None:
+        return parser.text
     else:
         return is_sif(item, check_formula, return_parser=True)[1].text
 
@@ -239,7 +239,7 @@ def sif4sci(item: str, figures: (dict, bool) = None, mode: int = 2, symbol: str
             check_formula = True if mode == 1 else False
             sif, item_parser = is_sif(item, check_formula=check_formula, return_parser=True)
             if sif is not True:
-                item = to_sif(item, cache_parser=item_parser)
+                item = to_sif(item, parser=item_parser)
         elif mode != 0:
             raise KeyError(
                 "Unknown mode %s, use only 0 or 1 or 2." % mode
diff --git a/examples/sif/sif_addition.ipynb b/examples/sif/sif_addition.ipynb
index c9f5424e..7a2a1b20 100644
--- a/examples/sif/sif_addition.ipynb
+++ b/examples/sif/sif_addition.ipynb
@@ -2,112 +2,166 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# sif_addition"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "source": [
-    "from EduNLP.SIF import is_sif, to_sif,sif4sci"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stderr",
+     "output_type": "stream",
      "text": [
       "D:\\MySoftwares\\Anaconda\\envs\\data\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
       "  warnings.warn(msg)\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from EduNLP.SIF import is_sif, to_sif,sif4sci"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## is_sif"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "source": [
-    "text = '若$x,y$满足约束条件' \\\r\n",
-    "     '$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，' \\\r\n",
-    "    '则$z=x+7 y$的最大值$\\\\SIFUnderline$'\r\n",
-    "    \r\n",
-    "is_sif(text)\r\n"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "(True,\n",
-       " '若$x,y$满足约束条件$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，则$z=x+7 y$的最大值$\\\\SIFUnderline$')"
+       "True"
       ]
      },
+     "execution_count": 2,
      "metadata": {},
-     "execution_count": 2
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "text = '若$x,y$满足约束条件' \\\n",
+    "     '$\\\\left\\\\{\\\\begin{array}{c}2 x+y-2 \\\\leq 0 \\\\\\\\ x-y-1 \\\\geq 0 \\\\\\\\ y+1 \\\\geq 0\\\\end{array}\\\\right.$，' \\\n",
+    "    '则$z=x+7 y$的最大值$\\\\SIFUnderline$'\n",
+    "    \n",
+    "is_sif(text)\n"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
     "is_sif(text)"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "(False, '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...')"
+       "(False, <EduNLP.SIF.parser.parser.Parser at 0x2a3083fa978>)"
       ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "execution_count": 3
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "is_sif(text, return_parser=True)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## to_sif"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\r\n",
-    "to_sif(text)"
-   ],
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "'某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "execution_count": 4
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'\n",
+    "to_sif(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]siftext : 某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位... ,consume time [0.018142223358154297s]\n",
+      "[2]return :  (False, <EduNLP.SIF.parser.parser.Parser object at 0x000002A30840FC88>)\n",
+      "[2]siftext : 某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位... ,consume time [0.008990764617919922s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "# ------------不使用‘加速’机制--------------- #\n",
+    "text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'*150\n",
+    "start = time.time()\n",
+    "if not is_sif(text):\n",
+    "    siftext = to_sif(text)\n",
+    "print(\"[1]siftext : {} ,consume time [{}s]\".format(siftext[:35], time.time() - start))\n",
+    "\n",
+    "# ------------使用‘加速’机制--------------- #\n",
+    "start = time.time()\n",
+    "ret = is_sif(text, return_parser=True)\n",
+    "print(\"[2]return : \", ret)\n",
+    "if ret[0] is not True:\n",
+    "    siftext = to_sif(text, parser=ret[1])\n",
+    "print(\"[2]siftext : {} ,consume time [{}s]\".format(siftext[:35], time.time() - start))"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## sif4sci\n",
     " to_symbolize:\n",
@@ -115,283 +169,279 @@
     " - \"f\": formula\n",
     " - \"g\": figure\n",
     " - \"m\": question mark"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "test_item = r\"如图所示，则$\\bigtriangleup ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$\"\r\n",
-    "t1 = sif4sci(test_item)\r\n",
-    "t1"
-   ],
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['如图所示', '\\\\bigtriangleup', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "execution_count": 5
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "test_item = r\"如图所示，则$\\bigtriangleup ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$\"\n",
+    "t1 = sif4sci(test_item)\n",
+    "t1"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "source": [
-    "t1.describe()"
-   ],
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "{'t': 2, 'f': 2, 'g': 1, 'm': 1}"
       ]
      },
+     "execution_count": 8,
      "metadata": {},
-     "execution_count": 6
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.describe()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "source": [
-    "with t1.filter('fgm'):\r\n",
-    "    print(t1)"
-   ],
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "['如图所示', '面积']\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "with t1.filter('fgm'):\n",
+    "    print(t1)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "source": [
-    "with t1.filter(keep='t'):\r\n",
-    "    print(t1)"
-   ],
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "['如图所示', '面积']\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "with t1.filter(keep='t'):\n",
+    "    print(t1)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "with t1.filter():\r\n",
-    "    print(t1)"
-   ],
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "['如图所示', '\\\\bigtriangleup', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "with t1.filter():\n",
+    "    print(t1)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "source": [
-    "t1.text_tokens"
-   ],
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['如图所示', '面积']"
       ]
      },
+     "execution_count": 12,
      "metadata": {},
-     "execution_count": 10
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.text_tokens"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "source": [
-    "t1.formula_tokens"
-   ],
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['\\\\bigtriangleup', 'ABC']"
       ]
      },
+     "execution_count": 13,
      "metadata": {},
-     "execution_count": 11
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.formula_tokens"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "t1.figure_tokens"
-   ],
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[\\FigureID{1}]"
       ]
      },
+     "execution_count": 14,
      "metadata": {},
-     "execution_count": 12
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.figure_tokens"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "t1.ques_mark_tokens"
-   ],
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['\\\\SIFBlank']"
       ]
      },
+     "execution_count": 15,
      "metadata": {},
-     "execution_count": 13
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.ques_mark_tokens"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "source": [
-    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\"}})"
-   ],
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['如图所示', <Formula: \\bigtriangleup ABC>, '面积', '[MARK]', '[FIGURE]']"
       ]
      },
+     "execution_count": 16,
      "metadata": {},
-     "execution_count": 14
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\"}})"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "source": [
-    "sif4sci(test_item, symbol=\"tfgm\")"
-   ],
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']"
       ]
      },
+     "execution_count": 17,
      "metadata": {},
-     "execution_count": 15
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "sif4sci(test_item, symbol=\"tfgm\")"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "source": [
-    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})"
-   ],
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['如图所示', '\\\\bigtriangleup', 'A', 'B', 'C', '面积', '[MARK]', '[FIGURE]']"
       ]
      },
+     "execution_count": 18,
      "metadata": {},
-     "execution_count": 16
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "test_item_1 = {\r\n",
-    "    \"stem\": r\"若$x=2$, $y=\\sqrt{x}$，则下列说法正确的是$\\SIFChoice$\",\r\n",
-    "    \"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]\r\n",
+    "test_item_1 = {\n",
+    "    \"stem\": r\"若$x=2$, $y=\\sqrt{x}$，则下列说法正确的是$\\SIFChoice$\",\n",
+    "    \"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]\n",
     "}"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "tls = [\r\n",
-    "        sif4sci(e, symbol=\"gm\",\r\n",
-    "        tokenization_params={\r\n",
-    "        \"formula_params\": {\r\n",
-    "                \"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True, \"var_numbering\": True,\r\n",
-    "                \"link_variable\": False}\r\n",
-    "        })\r\n",
-    "        for e in ([test_item_1[\"stem\"]] + test_item_1[\"options\"])\r\n",
+    "tls = [\n",
+    "        sif4sci(e, symbol=\"gm\",\n",
+    "        tokenization_params={\n",
+    "        \"formula_params\": {\n",
+    "                \"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True, \"var_numbering\": True,\n",
+    "                \"link_variable\": False}\n",
+    "        })\n",
+    "        for e in ([test_item_1[\"stem\"]] + test_item_1[\"options\"])\n",
     "]"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "source": [
-    "tls"
-   ],
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['mathord_0', '=', 'textord', 'mathord_1', '=', 'mathord_0', '{ }', '\\\\sqrt', '说法', '正确', '[MARK]'],\n",
@@ -400,21 +450,21 @@
        " ['mathord_0', '<', 'mathord_1']]"
       ]
      },
+     "execution_count": 21,
      "metadata": {},
-     "execution_count": 19
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tls"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "tls[1:]"
-   ],
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['mathord_0', '<', 'mathord_1'],\n",
@@ -422,100 +472,100 @@
        " ['mathord_0', '<', 'mathord_1']]"
       ]
      },
+     "execution_count": 22,
      "metadata": {},
-     "execution_count": 20
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tls[1:]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "source": [
-    "from EduNLP.utils import dict2str4sif\r\n",
-    "\r\n",
-    "test_item_1_str = dict2str4sif(test_item_1, tag_mode=\"head\", add_list_no_tag=False)\r\n",
-    "test_item_1_str "
-   ],
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "'$\\\\SIFTag{stem}$若$x=2$, $y=\\\\sqrt{x}$，则下列说法正确的是$\\\\SIFChoice$$\\\\SIFTag{options}$$x < y$$\\\\SIFSep$$y = x$$\\\\SIFSep$$y < x$'"
       ]
      },
+     "execution_count": 23,
      "metadata": {},
-     "execution_count": 21
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "from EduNLP.utils import dict2str4sif\n",
+    "\n",
+    "test_item_1_str = dict2str4sif(test_item_1, tag_mode=\"head\", add_list_no_tag=False)\n",
+    "test_item_1_str "
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "tl1 = sif4sci(\r\n",
-    "    test_item_1_str, \r\n",
-    "    symbol=\"gm\", \r\n",
-    "    tokenization_params={\r\n",
-    "        \"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True}\r\n",
-    "    })\r\n",
+    "tl1 = sif4sci(\n",
+    "    test_item_1_str, \n",
+    "    symbol=\"gm\", \n",
+    "    tokenization_params={\n",
+    "        \"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True}\n",
+    "    })\n",
     "    "
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "source": [
-    "tl1.get_segments()[0]"
-   ],
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['\\\\SIFTag{stem}']"
       ]
      },
+     "execution_count": 25,
      "metadata": {},
-     "execution_count": 23
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl1.get_segments()[0]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "source": [
-    "tl1.get_segments()[1:3]"
-   ],
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['[TEXT_BEGIN]', '[TEXT_END]'],\n",
        " ['[FORMULA_BEGIN]', 'mathord', '=', 'textord', '[FORMULA_END]']]"
       ]
      },
+     "execution_count": 26,
      "metadata": {},
-     "execution_count": 24
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl1.get_segments()[1:3]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "source": [
-    "tl1.get_segments(add_seg_type=False)[0:3]"
-   ],
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['\\\\SIFTag{stem}'],\n",
@@ -523,81 +573,81 @@
        " ['mathord', '=', 'mathord', '{ }', '\\\\sqrt']]"
       ]
      },
+     "execution_count": 27,
      "metadata": {},
-     "execution_count": 25
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl1.get_segments(add_seg_type=False)[0:3]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "test_item_2 = {\"options\": [r\"$x < y$\", r\"$y = x$\", r\"$y < x$\"]}"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "test_item_2_str = dict2str4sif(test_item_2, tag_mode=\"head\", add_list_no_tag=False)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "source": [
-    "test_item_2_str"
-   ],
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "'$\\\\SIFTag{options}$$x < y$$\\\\SIFSep$$y = x$$\\\\SIFSep$$y < x$'"
       ]
      },
+     "execution_count": 30,
      "metadata": {},
-     "execution_count": 28
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "test_item_2_str"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "source": [
-    "tl2 = sif4sci(test_item_2_str, symbol=\"gms\",\r\n",
-    "     tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})\r\n",
-    "tl2 "
-   ],
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['\\\\SIFTag{options}', 'x', '<', 'y', '[SEP]', 'y', '=', 'x', '[SEP]', 'y', '<', 'x']"
       ]
      },
+     "execution_count": 31,
      "metadata": {},
-     "execution_count": 29
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl2 = sif4sci(test_item_2_str, symbol=\"gms\",\n",
+    "     tokenization_params={\"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\"}})\n",
+    "tl2 "
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "source": [
-    "tl2.get_segments(add_seg_type=False)"
-   ],
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['\\\\SIFTag{options}'],\n",
@@ -608,143 +658,146 @@
        " ['y', '<', 'x']]"
       ]
      },
+     "execution_count": 32,
      "metadata": {},
-     "execution_count": 30
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl2.get_segments(add_seg_type=False)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "source": [
-    "tl2.get_segments(add_seg_type=False, drop=\"s\")"
-   ],
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['\\\\SIFTag{options}'], ['x', '<', 'y'], ['y', '=', 'x'], ['y', '<', 'x']]"
       ]
      },
+     "execution_count": 33,
      "metadata": {},
-     "execution_count": 31
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl2.get_segments(add_seg_type=False, drop=\"s\")"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "source": [
-    "tl3 = sif4sci(test_item_1[\"stem\"], symbol=\"gs\")\r\n",
-    "tl3.text_segments"
-   ],
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['说法', '正确']]"
       ]
      },
+     "execution_count": 34,
      "metadata": {},
-     "execution_count": 32
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl3 = sif4sci(test_item_1[\"stem\"], symbol=\"gs\")\n",
+    "tl3.text_segments"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "source": [
-    "tl3.formula_segments"
-   ],
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['x', '=', '2'], ['y', '=', '\\\\sqrt', '{', 'x', '}']]"
       ]
      },
+     "execution_count": 35,
      "metadata": {},
-     "execution_count": 33
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl3.formula_segments"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "source": [
-    "tl3.figure_segments"
-   ],
+   "execution_count": 36,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[]"
       ]
      },
+     "execution_count": 36,
      "metadata": {},
-     "execution_count": 34
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl3.figure_segments"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "source": [
-    "tl3.ques_mark_segments"
-   ],
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['\\\\SIFChoice']]"
       ]
      },
+     "execution_count": 37,
      "metadata": {},
-     "execution_count": 35
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl3.ques_mark_segments"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   }
  ],
  "metadata": {
-  "orig_nbformat": 4,
+  "interpreter": {
+   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.6.13 64-bit ('data': conda)",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python",
-   "version": "3.6.13",
-   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
-   "pygments_lexer": "ipython3",
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
    "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.6.13 64-bit ('data': conda)"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
   },
-  "interpreter": {
-   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/tests/test_sif/test_sif.py b/tests/test_sif/test_sif.py
index 86606ed4..02d30132 100644
--- a/tests/test_sif/test_sif.py
+++ b/tests/test_sif/test_sif.py
@@ -34,7 +34,7 @@ def test_to_sif():
     ret = is_sif(text, return_parser=True)
     assert ret[0] == 0
     if ret[0] is not True:
-        siftext = to_sif(text, cache_parser=ret[1])
+        siftext = to_sif(text, parser=ret[1])
     print(siftext)
 
 

From f82e1860368490f00c5038a07cfea60fa2179b10 Mon Sep 17 00:00:00 2001
From: tswsxk <tongsw@mail.ustc.edu.cn>
Date: Sun, 10 Oct 2021 19:16:19 +0800
Subject: [PATCH 140/149] Update sif.py

---
 EduNLP/SIF/sif.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/EduNLP/SIF/sif.py b/EduNLP/SIF/sif.py
index ed5c99cd..93489d0f 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -28,7 +28,7 @@ def is_sif(item, check_formula=True, return_parser=False):
 
     Returns
     -------
-    when item can not be parsed correctly, raise Error;
+    when item can not be parsed correctly, raise ValueError;
     when item is in stardarded format originally, return Ture (and the Parser of item);
     when item isn't in stardarded format originally, return False (and the Parser of item);
 

From c26676e9ebf0e07c0dbf82b3fcd782256c496fa8 Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Sat, 16 Oct 2021 18:00:58 +0800
Subject: [PATCH 141/149] [feat] modify i2v&t2v for bert

---
 EduNLP/I2V/__init__.py            |  2 +-
 EduNLP/I2V/i2v.py                 | 29 ++++++++++++++++++++++++++---
 EduNLP/Pretrain/bert_vec.py       |  5 ++---
 EduNLP/Vector/__init__.py         |  2 +-
 EduNLP/Vector/bert_vec.py         |  2 +-
 EduNLP/Vector/t2v.py              |  2 ++
 tests/test_i2v/test_pretrained.py |  2 ++
 tests/test_vec/test_bert.py       | 22 +++++++++++++++++++++-
 8 files changed, 56 insertions(+), 10 deletions(-)

diff --git a/EduNLP/I2V/__init__.py b/EduNLP/I2V/__init__.py
index db5cb958..34eeb8cc 100644
--- a/EduNLP/I2V/__init__.py
+++ b/EduNLP/I2V/__init__.py
@@ -2,4 +2,4 @@
 # 2021/8/1 @ tongshiwei
 
 from .i2v import I2V, get_pretrained_i2v
-from .i2v import D2V, W2V
+from .i2v import D2V, W2V, Bert
diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 4254fe1e..0176dbef 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -4,10 +4,13 @@
 import json
 from EduNLP.constant import MODEL_DIR
 from ..Vector import T2V, get_pretrained_t2v as get_t2v_pretrained_model
+from ..Vector import PRETRAINED_MODELS
+from longling import path_append
 from ..Tokenizer import Tokenizer, get_tokenizer
+from EduNLP.Pretrain import BertTokenizer
 from EduNLP import logger
 
-__all__ = ["I2V", "D2V", "W2V", "get_pretrained_i2v"]
+__all__ = ["I2V", "D2V", "W2V", "Bert", "get_pretrained_i2v"]
 
 
 class I2V(object):
@@ -29,13 +32,16 @@ class I2V(object):
     """
 
     def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrained_t2v=False, **kwargs):
-
-        self.tokenizer: Tokenizer = get_tokenizer(tokenizer, **tokenizer_kwargs if tokenizer_kwargs is not None else {})
         if pretrained_t2v:
             logger.info("Use pretrained t2v model %s" % t2v)
             self.t2v = get_t2v_pretrained_model(t2v, kwargs.get("model_dir", MODEL_DIR))
         else:
             self.t2v = T2V(t2v, *args, **kwargs)
+        if tokenizer == 'bert':
+            self.tokenizer = BertTokenizer(**tokenizer_kwargs if tokenizer_kwargs is not None else {})
+        else:
+            self.tokenizer: Tokenizer = get_tokenizer(tokenizer, **tokenizer_kwargs
+                                                      if tokenizer_kwargs is not None else {})
         self.params = {
             "tokenizer": tokenizer,
             "tokenizer_kwargs": tokenizer_kwargs,
@@ -109,6 +115,22 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
         return cls("pure_text", name, pretrained_t2v=True, model_dir=model_dir)
 
 
+class Bert(I2V):
+    def infer_vector(self, items, tokenize=True, return_tensors='pt', *args, **kwargs) -> tuple:
+        inputs = self.tokenize(items, return_tensors=return_tensors) if tokenize is True else items
+        return self.t2v(inputs, *args, **kwargs), self.t2v.infer_tokens(inputs, *args, **kwargs)
+
+    @classmethod
+    def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
+        model_path = path_append(model_dir, PRETRAINED_MODELS[name][0].split('/')[-1], to_str=True)
+        for i in [".tar.gz", ".tar.bz2", ".tar.bz", ".tar.tgz", ".tar", ".tgz", ".zip", ".rar"]:
+            model_path = model_path.replace(i, "")
+        logger.info("model_path: %s" % model_path)
+        tokenizer_kwargs = {"pretrain_model": model_path}
+        return cls("bert", name, pretrained_t2v=True, model_dir=model_dir,
+                   tokenizer_kwargs=tokenizer_kwargs)
+
+
 MODELS = {
     "d2v_all_256": [D2V, "d2v_all_256"],
     "d2v_sci_256": [D2V, "d2v_sci_256"],
@@ -116,6 +138,7 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
     "d2v_lit_256": [D2V, "d2v_lit_256"],
     "w2v_sci_300": [W2V, "w2v_sci_300"],
     "w2v_lit_300": [W2V, "w2v_lit_300"],
+    'luna_bert': [Bert, 'luna_bert']
 }
 
 
diff --git a/EduNLP/Pretrain/bert_vec.py b/EduNLP/Pretrain/bert_vec.py
index 4ebbc70e..3da52cdb 100644
--- a/EduNLP/Pretrain/bert_vec.py
+++ b/EduNLP/Pretrain/bert_vec.py
@@ -5,7 +5,6 @@
 from copy import deepcopy
 from typing import Optional, Union
 import itertools as it
-from EduNLP.SIF.sif import sif4sci
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 from transformers import DataCollatorForLanguageModeling
 from transformers import Trainer, TrainingArguments
@@ -55,7 +54,7 @@ def __init__(self, pretrain_model="bert-base-chinese"):
             self.tokenizer.add_special_tokens({'additional_special_tokens': customize_tokens})
         self.pure_text_tokenizer = PureTextTokenizer()
 
-    def __call__(self, item: (list, str), return_tensors: Optional[Union[str, TensorType]] = None):
+    def __call__(self, item: (list, str), return_tensors: Optional[Union[str, TensorType]] = None, *args, **kwargs):
         if isinstance(item, str):
             item = ''.join(next(self.pure_text_tokenizer([item])))
         else:
@@ -63,7 +62,7 @@ def __call__(self, item: (list, str), return_tensors: Optional[Union[str, Tensor
             item = [''.join(next(token_generation)) for i in range(len(item))]
         return self.tokenizer(item, truncation=True, padding=True, return_tensors=return_tensors)
 
-    def tokenize(self, item: (list, str)):
+    def tokenize(self, item: (list, str), *args, **kwargs):
         if isinstance(item, str):
             item = ''.join(next(self.pure_text_tokenizer([item])))
             return self.tokenizer.tokenize(item)
diff --git a/EduNLP/Vector/__init__.py b/EduNLP/Vector/__init__.py
index 01a7ba05..ba4618dd 100644
--- a/EduNLP/Vector/__init__.py
+++ b/EduNLP/Vector/__init__.py
@@ -4,6 +4,6 @@
 from .gensim_vec import W2V, D2V, BowLoader, TfidfLoader
 from .const import *
 from .rnn import RNNModel
-from .t2v import T2V, get_pretrained_t2v
+from .t2v import T2V, get_pretrained_t2v, PRETRAINED_MODELS
 from .embedding import Embedding
 from .bert_vec import BertModel
diff --git a/EduNLP/Vector/bert_vec.py b/EduNLP/Vector/bert_vec.py
index ea8a242d..6f0fef5a 100644
--- a/EduNLP/Vector/bert_vec.py
+++ b/EduNLP/Vector/bert_vec.py
@@ -1,7 +1,6 @@
 import numpy as np
 from pathlib import PurePath
 from transformers import AutoModel
-from EduNLP.Pretrain import BertTokenizer
 from .const import UNK, PAD
 from .meta import Vector
 import torch
@@ -11,6 +10,7 @@ class BertModel(Vector):
     """
     Examples
     --------
+    >>> from EduNLP.Pretrain import BertTokenizer
     >>> tokenizer = BertTokenizer("bert-base-chinese")
     >>> model = BertModel("bert-base-chinese", tokenizer=tokenizer)
     >>> item = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$，若$x,y$满足约束",
diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index b698f756..19318807 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -10,6 +10,7 @@
 from .meta import Vector
 from EduNLP.constant import MODEL_DIR
 
+
 MODELS = {
     "w2v": W2V,
     "d2v": D2V,
@@ -51,6 +52,7 @@ def vector_size(self) -> int:
     "d2v_lit_256": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_literal_256.zip", "d2v"],
     "w2v_eng_300": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/w2v/general_english_300.zip", "w2v"],
     "w2v_lit_300": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/w2v/general_literal_300.zip", "w2v"],
+    "luna_bert": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/LUNABert.zip", "bert"]
 }
 
 
diff --git a/tests/test_i2v/test_pretrained.py b/tests/test_i2v/test_pretrained.py
index 53825ba8..30e60bae 100644
--- a/tests/test_i2v/test_pretrained.py
+++ b/tests/test_i2v/test_pretrained.py
@@ -24,5 +24,7 @@ def test_pretrained_i2v(tmp_path):
 
     get_pretrained_i2v("test_w2v", d)
 
+    get_pretrained_i2v("luna_bert", d)
+
     with pytest.raises(KeyError):
         get_pretrained_i2v("error")
diff --git a/tests/test_vec/test_bert.py b/tests/test_vec/test_bert.py
index 28e7c34e..c3392cf1 100644
--- a/tests/test_vec/test_bert.py
+++ b/tests/test_vec/test_bert.py
@@ -3,6 +3,7 @@
 import pytest
 from EduNLP.Pretrain import BertTokenizer, finetune_bert
 from EduNLP.Vector import BertModel, T2V
+from EduNLP.I2V import Bert, get_pretrained_i2v
 
 
 @pytest.fixture(scope="module")
@@ -44,7 +45,7 @@ def test_bert_without_param(stem_data_bert, tmpdir):
     assert t2v.infer_tokens(inputs).shape[-1] == t2v.vector_size
 
 
-def test_bert(stem_data_bert, tmpdir):
+def test_bert_i2v(stem_data_bert, tmpdir):
     output_dir = str(tmpdir.mkdir('finetuneBert'))
     train_params = {
         'epochs': 1,
@@ -57,3 +58,22 @@ def test_bert(stem_data_bert, tmpdir):
         output_dir,
         train_params=train_params
     )
+    i2v = Bert("bert", "bert", output_dir)
+    item = {'stem': '如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$, \
+            若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'}
+    i_vec, t_vec = i2v([item['stem'], item['stem']])
+    assert len(i_vec[0]) == i2v.vector_size
+    assert len(t_vec[0][0]) == i2v.vector_size
+
+    i_vec = i2v.infer_item_vector([item['stem'], item['stem']])
+    assert len(i_vec[0]) == i2v.vector_size
+
+    t_vec = i2v.infer_token_vector([item['stem'], item['stem']])
+    assert len(t_vec[0][0]) == i2v.vector_size
+
+    tokenizer_kwargs = {"pretrain_model": output_dir}
+    i2v = Bert('bert', 'bert', output_dir, tokenizer_kwargs=tokenizer_kwargs)
+    # i2v = get_pretrained_i2v('luna_bert', output_dir)
+    i_vec, t_vec = i2v([item['stem'], item['stem']])
+    assert len(i_vec[0]) == i2v.vector_size
+    assert len(t_vec[0][0]) == i2v.vector_size

From 3d9581df662ffced0a79dd46c196b806b127c7f6 Mon Sep 17 00:00:00 2001
From: nnnyt <793313994@qq.com>
Date: Thu, 21 Oct 2021 17:14:12 +0800
Subject: [PATCH 142/149] [doc] add comments

---
 EduNLP/I2V/i2v.py           | 47 +++++++++++++++++++++++++++++++++++++
 EduNLP/Pretrain/bert_vec.py | 25 ++++++++++++++++++++
 tests/test_vec/test_bert.py | 11 +++------
 3 files changed, 75 insertions(+), 8 deletions(-)

diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py
index 6fb9ad2a..261bdefd 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -255,7 +255,54 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
 
 
 class Bert(I2V):
+    """
+    The model aims to transfer item and tokens to vector with Bert.
+
+    Bases
+    -------
+    I2V
+
+    Parameters
+    -----------
+    tokenizer: str
+        the tokenizer name
+    t2v: str
+        the name of token2vector model
+    args:
+        the parameters passed to t2v
+    tokenizer_kwargs: dict
+        the parameters passed to tokenizer
+    pretrained_t2v: bool
+        True: use pretrained t2v model
+        False: use your own t2v model
+    kwargs:
+        the parameters passed to t2v
+
+    Returns
+    -------
+    i2v model: Bert
+    """
     def infer_vector(self, items, tokenize=True, return_tensors='pt', *args, **kwargs) -> tuple:
+        '''
+        It is a function to switch item to vector. And before using the function, it is nesseary to load model.
+
+        Parameters
+        -----------
+        items: str or list
+            the text of question
+        tokenize:bool
+            True: tokenize the item
+        return_tensors: str
+            tensor type used in tokenizer
+        args:
+            the parameters passed to t2v
+        kwargs:
+            the parameters passed to t2v
+
+        Returns
+        --------
+        vector:list
+        '''
         inputs = self.tokenize(items, return_tensors=return_tensors) if tokenize is True else items
         return self.t2v(inputs, *args, **kwargs), self.t2v.infer_tokens(inputs, *args, **kwargs)
 
diff --git a/EduNLP/Pretrain/bert_vec.py b/EduNLP/Pretrain/bert_vec.py
index 3da52cdb..f4cf167d 100644
--- a/EduNLP/Pretrain/bert_vec.py
+++ b/EduNLP/Pretrain/bert_vec.py
@@ -86,6 +86,31 @@ def __len__(self):
 
 
 def finetune_bert(items, output_dir, pretrain_model="bert-base-chinese", train_params=None):
+    """
+
+    Parameters
+    ----------
+    items：dict
+        the tokenization results of questions
+    output_dir: str
+        the path to save the model
+    pretrain_model: str
+        the name or path of pre-trained model
+        vector_size
+    train_params: dict
+        the training parameters passed to Trainer
+
+    Examples
+    ----------
+    >>> tokenizer = BertTokenizer()
+    >>> stems = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$",
+    ... "有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$"]
+    >>> token_item = [tokenizer(i) for i in stems]
+    >>> print(token_item[0].keys())
+    dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])
+    >>> finetune_bert(token_item, "examples/test_model/data/bert") #doctest: +ELLIPSIS
+    {'train_runtime': ..., ..., 'epoch': 1.0}
+    """
     model = AutoModelForMaskedLM.from_pretrained(pretrain_model)
     tokenizer = BertTokenizer(pretrain_model)
     # resize embedding for additional sepecial tokens
diff --git a/tests/test_vec/test_bert.py b/tests/test_vec/test_bert.py
index c3392cf1..3046a9ea 100644
--- a/tests/test_vec/test_bert.py
+++ b/tests/test_vec/test_bert.py
@@ -58,9 +58,11 @@ def test_bert_i2v(stem_data_bert, tmpdir):
         output_dir,
         train_params=train_params
     )
-    i2v = Bert("bert", "bert", output_dir)
+
     item = {'stem': '如图$\\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$, \
             若$x,y$满足约束条件$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$'}
+    tokenizer_kwargs = {"pretrain_model": output_dir}
+    i2v = Bert('bert', 'bert', output_dir, tokenizer_kwargs=tokenizer_kwargs)
     i_vec, t_vec = i2v([item['stem'], item['stem']])
     assert len(i_vec[0]) == i2v.vector_size
     assert len(t_vec[0][0]) == i2v.vector_size
@@ -70,10 +72,3 @@ def test_bert_i2v(stem_data_bert, tmpdir):
 
     t_vec = i2v.infer_token_vector([item['stem'], item['stem']])
     assert len(t_vec[0][0]) == i2v.vector_size
-
-    tokenizer_kwargs = {"pretrain_model": output_dir}
-    i2v = Bert('bert', 'bert', output_dir, tokenizer_kwargs=tokenizer_kwargs)
-    # i2v = get_pretrained_i2v('luna_bert', output_dir)
-    i_vec, t_vec = i2v([item['stem'], item['stem']])
-    assert len(i_vec[0]) == i2v.vector_size
-    assert len(t_vec[0][0]) == i2v.vector_size

From 96fc9d935839b1a3caba3ec67b1e5bb71d11818f Mon Sep 17 00:00:00 2001
From: KenelmQLH <46173279+KenelmQLH@users.noreply.github.com>
Date: Thu, 21 Oct 2021 19:50:30 +0800
Subject: [PATCH 143/149] Update bert_vec.py

---
 EduNLP/Pretrain/bert_vec.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/EduNLP/Pretrain/bert_vec.py b/EduNLP/Pretrain/bert_vec.py
index f4cf167d..40bdc16f 100644
--- a/EduNLP/Pretrain/bert_vec.py
+++ b/EduNLP/Pretrain/bert_vec.py
@@ -96,7 +96,6 @@ def finetune_bert(items, output_dir, pretrain_model="bert-base-chinese", train_p
         the path to save the model
     pretrain_model: str
         the name or path of pre-trained model
-        vector_size
     train_params: dict
         the training parameters passed to Trainer
 

From d3e44223596f12c4821a733bfb8d528800856b3f Mon Sep 17 00:00:00 2001
From: fannazya <bfsudcs_fanzy@163.com>
Date: Sat, 23 Oct 2021 20:15:21 +0800
Subject: [PATCH 144/149] Add English version tutorial

---
 docs/source/tutorial/en/index.rst             |  52 +++-
 docs/source/tutorial/en/parse.rst             | 290 ++++++++++++++++++
 .../parse/FormulaSyntaxStructureParsing.rst   | 168 ++++++++++
 .../en/parse/TextSyntaxStructureParsing.rst   |  72 +++++
 docs/source/tutorial/en/pretrain.rst          | 130 ++++++++
 docs/source/tutorial/en/pretrain/loading.rst  |  11 +
 docs/source/tutorial/en/pretrain/pub.rst      |  74 +++++
 docs/source/tutorial/en/pretrain/start.rst    |  24 ++
 docs/source/tutorial/en/seg.rst               | 187 +++++++++++
 .../en/seg/SemanticComponentSegmentation.rst  |  47 +++
 .../seg/StructuralComponentSegmentation.rst   |  67 ++++
 docs/source/tutorial/en/sif.rst               | 145 ++++++++-
 .../en/tokenization/GensimSegTokenizer.rst    |   9 +
 .../en/tokenization/GensimWordTokenizer.rst   |  23 ++
 .../en/tokenization/PureTextTokenizer.rst     |  31 ++
 .../en/tokenization/TextTokenizer.rst         |  27 ++
 docs/source/tutorial/en/tokenize.rst          | 172 +++++++++++
 .../en/tokenize/Sentence Segmentation.rst     |   3 +
 .../tutorial/en/tokenize/Tokenization.rst     |  29 ++
 .../tutorial/en/tokenize/WordSegmentation.rst |  36 +++
 docs/source/tutorial/en/vectorization.rst     | 157 ++++++++++
 .../en/vectorization/WithPre-trainedModel.rst |  42 +++
 .../vectorization/WithoutPre-trainedModel.rst |  21 ++
 23 files changed, 1815 insertions(+), 2 deletions(-)
 create mode 100644 docs/source/tutorial/en/parse.rst
 create mode 100644 docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
 create mode 100644 docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
 create mode 100644 docs/source/tutorial/en/pretrain.rst
 create mode 100644 docs/source/tutorial/en/pretrain/loading.rst
 create mode 100644 docs/source/tutorial/en/pretrain/pub.rst
 create mode 100644 docs/source/tutorial/en/pretrain/start.rst
 create mode 100644 docs/source/tutorial/en/seg.rst
 create mode 100644 docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
 create mode 100644 docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
 create mode 100644 docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
 create mode 100644 docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
 create mode 100644 docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
 create mode 100644 docs/source/tutorial/en/tokenization/TextTokenizer.rst
 create mode 100644 docs/source/tutorial/en/tokenize.rst
 create mode 100644 docs/source/tutorial/en/tokenize/Sentence Segmentation.rst
 create mode 100644 docs/source/tutorial/en/tokenize/Tokenization.rst
 create mode 100644 docs/source/tutorial/en/tokenize/WordSegmentation.rst
 create mode 100644 docs/source/tutorial/en/vectorization.rst
 create mode 100644 docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
 create mode 100644 docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst

diff --git a/docs/source/tutorial/en/index.rst b/docs/source/tutorial/en/index.rst
index 108a9487..4c8cc040 100644
--- a/docs/source/tutorial/en/index.rst
+++ b/docs/source/tutorial/en/index.rst
@@ -1,2 +1,52 @@
 Get Started
-===========
+=====
+
+*  `Standard Item Format <sif.rst>`_
+
+*  `Syntax Parsing <parse.rst>`_
+
+*  `Component Segmentation <seg.rst>`_
+
+*  `Tokenization <tokenize.rst>`_
+
+*  `Pre-training <pretrain.rst>`_
+
+*  `Vectorization <vectorization.rst>`_
+
+Main process
+----------
+
+.. figure:: ../../_static/new_flow.png
+
+* `Syntax Parsing <parse.rst>`_ : Its function is to convert the incoming item into SIF format, which means letters and numbers should be between ``$...$`` and the brackets and underlines of the choice questions should be converted to special symbols we defined in SIF)
+
+* `Component Segmentation <seg.rst>`_ : Its function is to segment items in SIF format according to the types of items, so as to serve the later tokenization module.(that is, elements in different types can be tokenized using their corresponding methods)。
+
+* `Tokenization <tokenize.rst>`_: Its function is to tokenize segmented items, so as to serve the later tokenization module.
+  Generally, the tokenization method in the text form can be used directly. For formulas, the ast method can also be used for parsing(call the formula module)；
+
+* `Vectorization <vectorization.rst>`_: This part mainly calls I2V class and its subclasses. Its function is to vectorize the list of tokenized items, so as to get the corresponding static vectors.
+  For vectorization module, You can call your own trained model or directly call the provided pre-training model(call get_ pretrained_ I2V module).
+
+* **Downstream Model**：Process the obtained vectors to get the desired results。
+
+Examples
+--------
+
+To help you quickly understand the functions of this project, this section only shows the usages of common function interface. Intermediate function modules (such as parse, formula, segment, etc.) and more subdivided interface methods are not shown. For further study, please refer to relevant documents.
+
+.. nbgallery::
+    :caption: This is a thumbnail gallery:
+    :name: tokenize_gallery
+    :glob:
+    
+    Tokenization  <../../build/blitz/tokenizer/tokenizer.ipynb>
+
+
+
+.. nbgallery::
+    :caption: This is a thumbnail gallery:
+    :name: vectorization_gallery
+    :glob:
+    
+    Vectorization  <../../build/blitz/vectorization/total_vector.ipynb>
diff --git a/docs/source/tutorial/en/parse.rst b/docs/source/tutorial/en/parse.rst
new file mode 100644
index 00000000..69608c5e
--- /dev/null
+++ b/docs/source/tutorial/en/parse.rst
@@ -0,0 +1,290 @@
+Syntax Parsing
+=========
+
+In educational resources, texts and formulas have internal implicit or explicit syntax structures. It is of great benefit for further processing to extract these structures.
+* Text syntax structure parsing
+
+* Formula syntax structure parsing
+
+The purpose is as follows:
+
+
+1. Represent underlines of blanks and brackets of choices with special identifiers. And the alphabets and formulas should be wrapped with $$, so that items of different types can be cut accurately through the symbol $.
+2. Determine whether the current item is legal and report the error type.
+
+Specific processing content
+--------------------
+
+1.Its function is to match alphabets and numbers other than formulas. Only the alphabets and numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+
+2.Match brackets like "( )" (both English format and Chinese format), that is, brackets with no content or spaces, which should be replaced with ``$\\SIFChoice$``
+
+3.Match continuous underscores or underscores with spaces to replace them with ``$\\SIFBlank$``.
+
+4.Match latex formulas，check the completeness and analyzability of latex formulas, and report an error for illegal formula.
+
+Formula syntax structure parsing
+--------------------
+
+This section is mainly realized by EduNLP.Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
+
+Introduction of Main Introduction
++++++++++++++++
+
+1.Formula: determine whether the single formula passed in is in str form. If so, use the ast method for processing, otherwise an error will be reported. In addition, parameter variable_standardization is given. If this parameter is true, the variable standardization method will be used to make sure the same variable has the same variable number.
+
+2.FormulaGroup: If you need to pass in a formula set, you can call this interface to get an ast forest. The tree structure in the forest is the same as that of Formula.
+
+Formula
+>>>>>>>>>>>>
+
+Formula: firstly, in the word segmentation function, the formula of the original text is segmented. In addition, ``Formula parse tree`` function is provided, which can represent the abstract syntax analysis tree of mathematical formula in the form of text or picture.
+
+This module also provides the function of formula variable standardization, such as determining that 'x' in several sub formulas is the same variable.
+
+Call the library
++++++++++
+
+::
+
+   import matplotlib.pyplot as plt
+   from EduNLP.Formula import Formula
+   from EduNLP.Formula.viz import ForestPlotter
+
+Initialization
++++++++++
+
+Incoming parameters: item
+
+Item is the latex formula or the abstract syntax parse tree generated after the formula is parsed and its type is str or List[Dict].
+
+::
+
+   >>> f=Formula("x^2 + x+1 = y")
+   >>> f
+   <Formula: x^2 + x+1 = y>
+
+View the specific content after formula segmentation
+++++++++++++++++++++++++++++
+
+- View node elements after formula segmentation
+
+::
+
+   >>> f.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]
+
+- View the abstract parse tree of formulas
+
+::
+
+   >>> f.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],'child': [1, 2],'father': None,'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [7, None],'child': None,'father': None,'forest': None}}]
+
+   >>> print('nodes: ',f.ast_graph.nodes)
+   nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]
+   >>> print('edges: ' ,f.ast_graph.edges)
+   edges:  [(0, 1), (0, 2)]
+
+- show the abstract parse tree by a picture
+
+::
+
+   >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
+   >>> plt.show()
+
+
+.. figure:: ../../_static/formula.png
+
+
+Variable standardization
++++++++++++
+
+This parameter makes the same variable have the same variable number.
+
+For example: the number of variable ``x`` is ``0`` and the number of variable ``y`` is ``1``.
+
+::
+
+   >>> f.variable_standardization().elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+
+FormulaGroup
+>>>>>>>>>>>>>>>
+
+Call ``FormulaGroup`` class to parse the equations. The related attributes and functions are the same as those above.
+
+::
+
+   import matplotlib.pyplot as plt
+   from EduNLP.Formula import Formula
+   from EduNLP.Formula import FormulaGroup
+   from EduNLP.Formula.viz import ForestPlotter
+   >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
+   >>> fs
+   <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
+   >>> fs.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},
+   {'id': 8, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 9, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},
+   {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 13, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 15, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 16, 'type': 'mathord', 'text': '\\pi', 'role': None}]
+   >>> fs.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],
+      'child': [1, 2],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2],
+      'child': None,
+      'father': 0,
+      'forest': [6, 12]}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [3, None],
+      'child': None,
+      'father': None,
+      'forest': [10, 14]}},
+   {'val': {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 8],
+      'child': [6, 7],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   show more (open the raw output data in a text editor) ...
+   >>> fs.variable_standardization()[0]
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+   >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
+
+.. figure:: ../../_static/formulagroup.png
+
+
+Text syntax structure parsing
+--------------------
+
+This section is mainly realized by EduNLP.SIF.Parse module. Its main function is to extract letters and numbers in the text and convert them into standard format.
+
+This module is mainly used as an *middle module* to parse the input text. Users generally do not call this module directly.
+
+Introduction of main content
++++++++++++++++
+
+1. Judge the type of the incoming text in the following order
+
+* is_chinese: its function is to match Chinese characters[\u4e00-\u9fa5].
+ 
+* is_alphabet: its function is to match alphabets other than formulas. Only the alphabets between two Chinese characters will be corrected (wrapped with $$), and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+ 
+* is_number: its function is to match numbers other than formulas. Only the numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+ 
+2. Match latex formula
+
+* If Chinese characters appear in latex, print warning only once.
+ 
+* Use _is_formula_legal function, check the completeness and analyzability of latex formula, and report an error for formulas that do not conform to latex syntax.
+
+Call the library
+>>>>>>>>>>>>
+
+::
+
+   from EduNLP.SIF.Parser import Parser
+
+Input
+>>>>>>>
+
+Types: str
+
+Content: question text
+
+::
+
+   >>> text1 = '生产某种零件的A工厂25名工人的日加工零件数_   _'
+   >>> text2 = 'X的分布列为(   )'
+   >>> text3 = '① AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．AC的中点为D'
+   >>> text4 = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
+
+Parsing
+>>>>>>>>>>>>>>>>>>>>
+
+::
+
+   >>> text_parser1 = Parser(text1)
+   >>> text_parser2 = Parser(text2)
+   >>> text_parser3 = Parser(text3)
+   >>> text_parser4 = Parser(text4)
+
+Related parameters description(?)
+>>>>>>>>>>>>
+
+- Try to convert text to standard format
+
+::
+
+   >>> text_parser1.description_list()
+   >>> print('text_parser1.text:',text_parser1.text)
+   text_parser1.text: 生产某种零件的$A$工厂$25$名工人的日加工零件数$\SIFBlank$
+   >>> text_parser2.description_list()
+   >>> print('text_parser2.text:',text_parser2.text)
+   text_parser2.text: $X$的分布列为$\SIFChoice$
+
+- Determine if the text has syntax errors
+
+::
+
+   >>> text_parser3.description_list()
+   >>> print('text_parser3.error_flag: ',text_parser3.error_flag)
+   text_parser3.error_flag:  1
+   >>> text_parser4.description_list()
+   >>> print('text_parser4.fomula_illegal_flag: ',text_parser4.fomula_illegal_flag)
+   text_parser4.fomula_illegal_flag:  1
+
diff --git a/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst b/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
new file mode 100644
index 00000000..2fc479c5
--- /dev/null
+++ b/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
@@ -0,0 +1,168 @@
+Formula syntax structure parsing
+--------------------
+
+This section is mainly realized by EduNLP.Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
+
+Introduction of Main Content
++++++++++++++++
+
+1.Formula: determine whether the single formula passed in is in str form. If so, use the ast method for processing, otherwise an error will be reported. In addition, parameter variable_standardization is given. If this parameter is true, the variable standardization method will be used to make sure the same variable has the same variable number.
+
+2.FormulaGroup: If you need to pass in a formula set, you can call this interface to get an ast forest. The tree structure in the forest is the same as that of Formula.
+
+Formula
+>>>>>>>>>>>>
+
+Formula: firstly, in the word segmentation function, the formula of the original text is segmented. In addition, ``Formula parse tree`` function is provided, which can represent the abstract syntax analysis tree of mathematical formula in the form of text or picture.
+
+This module also provides the function of formula variable standardization, such as determining that 'x' in several sub formulas is the same variable.
+
+Initialization
++++++++++
+
+Incoming parameters: item
+
+Item is the latex formula or the abstract syntax parse tree generated after the formula is parsed and its type is str or List[Dict].
+
+::
+
+   >>> f=Formula("x^2 + x+1 = y")
+   >>> f
+   <Formula: x^2 + x+1 = y>
+
+View the specific content after formula segmentation
+++++++++++++++++++++++++++++
+
+- View node elements after formula segmentation
+
+::
+
+   >>> f.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]
+
+- View the abstract parsing tree of formulas
+
+::
+
+   >>> f.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],'child': [1, 2],'father': None,'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [7, None],'child': None,'father': None,'forest': None}}]
+
+   >>> print('nodes: ',f.ast_graph.nodes)
+   nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]
+   >>> print('edges: ' ,f.ast_graph.edges)
+   edges:  [(0, 1), (0, 2)]
+
+- show the abstract parse tree by a picture
+
+::
+
+   >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
+   >>> plt.show()
+
+.. figure:: ../../../_static/formula.png
+
+Variable Standardization
++++++++++++
+
+This parameter makes the same variable have the same variable number.
+
+For example: the number of variable ``x`` is ``0`` and the number of variable ``y`` is ``1``.
+
+::
+
+   >>> f.variable_standardization().elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+
+FormulaGroup
+>>>>>>>>>>>>>>>
+
+Call ``FormulaGroup`` class to parse the equations. The related attributes and functions are the same as those above.
+
+::
+
+   >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
+   >>> fs
+   <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
+   >>> fs.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},
+   {'id': 8, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 9, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},
+   {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 13, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 15, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 16, 'type': 'mathord', 'text': '\\pi', 'role': None}]
+   >>> fs.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],
+      'child': [1, 2],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2],
+      'child': None,
+      'father': 0,
+      'forest': [6, 12]}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [3, None],
+      'child': None,
+      'father': None,
+      'forest': [10, 14]}},
+   {'val': {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 8],
+      'child': [6, 7],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   show more (open the raw output data in a text editor) ...
+   >>> fs.variable_standardization()[0]
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+   >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
+
+.. figure:: ../../../_static/formulagroup.png
diff --git a/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst b/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
new file mode 100644
index 00000000..bdfe6848
--- /dev/null
+++ b/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
@@ -0,0 +1,72 @@
+Text syntax structure parsing
+--------------------
+
+This section is mainly realized by EduNLP.SIF.Parse module. Its main function is to extract letters and numbers in the text and convert them into standard format.
+
+This module is mainly used as an *middle module* to parse the input text. Users generally do not call this module directly.
+
+Introduction of Main Content
++++++++++++++++
+
+1. Judge the type of the incoming text in the following order
+
+* is_chinese: its function is to match Chinese characters[\u4e00-\u9fa5].
+
+* is_alphabet: its function is to match alphabets other than formulas. Only the alphabets between two Chinese characters will be corrected (wrapped with $$), and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+
+* is_number: its function is to match numbers other than formulas. Only the numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+
+2. Match latex formula
+
+* If Chinese characters appear in latex, print warning only once.
+
+* Use _is_formula_legal function, check the completeness and analyzability of latex formula, and report an error for formulas that do not conform to latex syntax.
+
+Input
+>>>>>>>
+
+Type: str
+
+Content：question text
+
+::
+
+   >>> text1 = '生产某种零件的A工厂25名工人的日加工零件数_   _'
+   >>> text2 = 'X的分布列为(   )'
+   >>> text3 = '① AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．AC的中点为D'
+   >>> text4 = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
+
+Parsing
+>>>>>>>>>>>>>>>>>>>>
+
+::
+
+   >>> text_parser1 = Parser(text1)
+   >>> text_parser2 = Parser(text2)
+   >>> text_parser3 = Parser(text3)
+   >>> text_parser4 = Parser(text4)
+
+Related parameters description(?)
+>>>>>>>>>>>>
+
+- Try to convert text to standard format
+
+::
+
+   >>> text_parser1.description_list()
+   >>> print('text_parser1.text:',text_parser1.text)
+   text_parser1.text: 生产某种零件的$A$工厂$25$名工人的日加工零件数$\SIFBlank$
+   >>> text_parser2.description_list()
+   >>> print('text_parser2.text:',text_parser2.text)
+   text_parser2.text: $X$的分布列为$\SIFChoice$
+
+- Determine if the text has syntax errors
+
+::
+
+   >>> text_parser3.description_list()
+   >>> print('text_parser3.error_flag: ',text_parser3.error_flag)
+   text_parser3.error_flag:  1
+   >>> text_parser4.description_list()
+   >>> print('text_parser4.fomula_illegal_flag: ',text_parser4.fomula_illegal_flag)
+   text_parser4.fomula_illegal_flag:  1
diff --git a/docs/source/tutorial/en/pretrain.rst b/docs/source/tutorial/en/pretrain.rst
new file mode 100644
index 00000000..9319b87d
--- /dev/null
+++ b/docs/source/tutorial/en/pretrain.rst
@@ -0,0 +1,130 @@
+Pre-training
+=======
+
+In the field of NLP, Pre-trained Language Models has become a very important basic technology.
+In this chapter, we will introduce the pre training tools in EduNLP:
+
+* How to train with a corpus to get a pre-trained model
+* How to load the pre-trained model
+* Public pre-trained models
+
+Import modules
+----------
+
+::
+
+   from EduNLP.I2V import get_pretrained_i2v
+   from EduNLP.Vector import get_pretrained_t2v
+
+Train the Model
+------------
+
+Call train_Vector function interface directly to make the training model easier. This section calls the relevant training models in the gensim library. At present, the training methods of "sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf" are provided. Parameter embedding_dim is also provided for users to determine vector dimension according to their needs.
+
+Basic Steps
+##################
+
+1.Determine the type of model and select the appropriate tokenizer (GensimWordTokenizer、 GensimSegTokenizer) to finish tokenization.
+
+2.Call train_vector function to get the required pre-trained model。
+
+Examples：
+
+::
+
+   >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+   
+   # 10 dimension with fasstext method
+   train_vector(sif_items, "../../../data/w2v/gensim_luna_stem_tf_", 10, method="d2v")
+
+
+Load models
+--------
+
+Transfer the obtained model to the I2V module to load the model.
+ 
+Examples：
+
+::
+
+   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+
+The overview of our public model
+------------
+
+Version description
+##################
+
+First level version:
+
+* Public version 1 (luna_pub): college entrance examination
+* Public version 2 (luna_pub_large): college entrance examination + regional examination
+
+Second level version:
+
+* Minor subjects(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
+* Major subjects(science, arts and all subject)
+
+Third level version【to be finished】:
+
+* Don't use third-party initializers
+* Use third-party initializers
+
+Description of train data in models
+##################
+
+* Currently, the data used in w2v and d2v models are the subjects of senior high school.
+* test data:`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
+
+At present, the following models are provided. More models of different subjects and question types are being trained. Please look forward to it.
+    "d2v_all_256" (all subject), "d2v_sci_256" (Science), "d2v_eng_256" (English)，"d2v_lit_256" (Arts)
+
+
+Examples of Model Training
+------------
+
+Get the dataset
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   prepare_dataset  <../../build/blitz/pretrain/prepare_dataset.ipynb>
+
+An example of d2v in gensim model
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v_bow_tfidf  <../../build/blitz/pretrain/gensim/d2v_bow_tfidf.ipynb>
+   d2v_general  <../../build/blitz/pretrain/gensim/d2v_general.ipynb>
+   d2v_stem_tf  <../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
+
+An example of w2v in gensim model
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   w2v_stem_text  <../../build/blitz/pretrain/gensim/w2v_stem_text.ipynb>
+   w2v_stem_tf  <../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
+
+An example of seg_token
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v.ipynb  <../../build/blitz/pretrain/seg_token/d2v.ipynb>
+   d2v_d1  <../../build/blitz/pretrain/seg_token/d2v_d1.ipynb>
+   d2v_d2  <../../build/blitz/pretrain/seg_token/d2v_d2.ipynb>
\ No newline at end of file
diff --git a/docs/source/tutorial/en/pretrain/loading.rst b/docs/source/tutorial/en/pretrain/loading.rst
new file mode 100644
index 00000000..31fa3ea8
--- /dev/null
+++ b/docs/source/tutorial/en/pretrain/loading.rst
@@ -0,0 +1,11 @@
+Load models
+--------
+
+Transfer the obtained model to the I2V module to load the model.
+ 
+Examples：
+
+::
+
+        >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+        >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
diff --git a/docs/source/tutorial/en/pretrain/pub.rst b/docs/source/tutorial/en/pretrain/pub.rst
new file mode 100644
index 00000000..34407745
--- /dev/null
+++ b/docs/source/tutorial/en/pretrain/pub.rst
@@ -0,0 +1,74 @@
+The overview of our public model
+------------
+
+
+Version Description
+##################
+
+First level version:
+
+* Public version 1 (luna_pub): college entrance examination
+* Public version 2 (luna_pub_large): college entrance examination + regional examination
+
+Second level version:
+
+* Minor subjects(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
+* Major subjects(science, arts and all subject)
+
+Third level version【to be finished】:
+
+* Don't use third-party initializers
+* Use third-party initializers
+
+Description of train data in models
+##################
+
+* Currently, the data used in w2v and d2v models are the subjects of senior high school.
+* test data:`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
+
+At present, the following models are provided. More models of different subjects and question types are being trained. Please look forward to it.
+    "d2v_all_256" (all subject), "d2v_sci_256" (Science), "d2v_eng_256" (English)，"d2v_lit_256" (Arts)
+
+Examples of model training
+------------
+
+Get the dataset
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   prepare_dataset  <../../../build/blitz/pretrain/prepare_dataset.ipynb>
+
+An example of d2v in gensim model
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v_bow_tfidf  <../../../build/blitz/pretrain/gensim/d2v_bow_tfidf.ipynb>
+   d2v_general  <../../../build/blitz/pretrain/gensim/d2v_general.ipynb>
+   d2v_stem_tf  <../../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
+
+An example of w2v in gensim model
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   w2v_stem_text  <../../../build/blitz/pretrain/gensim/w2v_stem_text.ipynb>
+   w2v_stem_tf  <../../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
+
+An example of seg_token
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v.ipynb  <../../../build/blitz/pretrain/seg_token/d2v.ipynb>
+   d2v_d1  <../../../build/blitz/pretrain/seg_token/d2v_d1.ipynb>
+   d2v_d2  <../../../build/blitz/pretrain/seg_token/d2v_d2.ipynb>
diff --git a/docs/source/tutorial/en/pretrain/start.rst b/docs/source/tutorial/en/pretrain/start.rst
new file mode 100644
index 00000000..9c5bc241
--- /dev/null
+++ b/docs/source/tutorial/en/pretrain/start.rst
@@ -0,0 +1,24 @@
+Train the model
+------------
+
+Call train_Vector function interface directly to make the training model easier. This section calls the relevant training models in the gensim library. At present, the training methods of "sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf" are provided. Parameter embedding_dim is also provided for users to determine vector dimension according to their needs.
+
+Basic Steps
+##################
+
+1.Determine the type of model and select the appropriate tokenizer (GensimWordTokenizer、 GensimSegTokenizer) to finish tokenization.
+
+2.Call train_vector function to get the required pre-trained model。
+
+Examples：
+
+::
+
+        >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+        >>> print(token_item.tokens[:10])
+        ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+        
+        # 10 dimension with fasstext method
+        train_vector(sif_items, "../../../data/w2v/gensim_luna_stem_tf_", 10, method="d2v")
diff --git a/docs/source/tutorial/en/seg.rst b/docs/source/tutorial/en/seg.rst
new file mode 100644
index 00000000..ad2696a2
--- /dev/null
+++ b/docs/source/tutorial/en/seg.rst
@@ -0,0 +1,187 @@
+Component Segmentation
+=========
+
+Educational resource is a kind of multimodal data, including data such as text, picture, formula and so on.
+At the same time, it may also contain different components semantically, such as question stems, options, etc. Therefore, we first need to identify and segment the different components of educational resources:
+
+* Semantic Component Segmentation
+* Structural Component Segmentation
+
+Main Processing Contents
+--------------------
+
+1. Convert multiple-choice questions in the form of dict to qualified item by `Syntax parsing <parse.rst>`_;
+
+2. The input items are segmented and grouped according to the element type.
+
+Semantic Component Segmentation
+------------
+
+Because multiple-choice questions are given in the form of dict, it is necessary to convert them into text format while retaining their data relationship. This function can be realized by dict2str4sif function which can convert multiple-choice question items into character format and identify question stem and options。
+
+Import Modules
++++++++++
+
+::
+
+ from EduNLP.utils import dict2str4sif
+
+Basic Usage
+++++++++++++++++++
+
+::
+
+ >>> item = {
+ ...     "stem": r"若复数$z=1+2 i+i^{3}$，则$|z|=$",
+ ...     "options": ['0', '1', r'$\sqrt{2}$', '2'],
+ ... }
+ >>> dict2str4sif(item) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+
+Optional additional parameters / interfaces
+++++++++++++++++++++++
+
+1.add_list_no_tag: if this parameter is true, it means that you need to count the labels in the options section.
+
+::
+
+ >>> dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+ 
+ >>> dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2$\\SIFTag{options_end}$'
+
+2.tag_mode: The location for the label can be selected using this parameter. 'delimiter' is to label both the beginning and the end,'head' is to label only the head, and 'tail' is to label only the tail.
+
+::
+
+ >>> dict2str4sif(item, tag_mode="head") # doctest: +ELLIPSIS
+ '$\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{options}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2'
+ 
+ >>> dict2str4sif(item, tag_mode="tail") # doctest: +ELLIPSIS
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options}$'
+
+3.key_as_tag: If this parameter is false, this process will only adds $\SIFSep$ between the options without distinguishing the type of segmentation label.
+
+::
+
+ >>> dict2str4sif(item, key_as_tag=False)
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
+
+Structural Component Segmentation
+------------
+
+This step is to segment sliced items. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
+
+
+There are two modes:
+
+* linear mode: it is used for text processing (word segmentation using jieba library);
+
+* ast mode: it is used to parse the formula.
+
+Basic Segmentation process:
+
+- Match components with regular expression matching
+
+- Process the components with special structures, such as converting the base64 encoded picture to numpy form
+
+- Classify the elements into each element group
+
+- Enter the corresponding parameters as required to get the filtered results
+
+Import Modules
++++++++++
+
+::
+
+ from EduNLP.SIF.segment import seg
+ from EduNLP.SIF import sif4sci
+
+Basic Usage
+++++++++++++++++++
+
+::
+
+ >>> test_item = r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$"
+ >>> seg(test_item)
+ >>> ['如图所示，则', '\\bigtriangleup ABC', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+
+Optional additional parameters/interfaces
+++++++++++++++++++++++
+
+1.describe: count the number of elements of different types
+
+::
+
+ >>> s.describe()
+ {'t': 3, 'f': 1, 'g': 1, 'm': 1}
+
+2.filter: this interface can screen out one or more types of elements.
+
+Using this interface, you can pass in a "keep" parameter or a special character directly to choose what type of elements to retain.
+
+Element type represented by symbol:
+
+-   "t": text
+-   "f": formula
+-   "g": figure
+-   "m": question mark
+-   "a": tag
+-   "s": sep tag
+
+::
+
+ >>> with s.filter("f"):
+ ...     s
+ ['如图所示，则', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+ >>> with s.filter(keep="t"):
+ ...     s
+ ['如图所示，则', '的面积是', '。']
+
+3.symbol: this interface can convert some types of data into special symbols
+
+Element type represented by symbol:
+
+-   "t": text
+-   "f": formula
+-   "g": figure
+-   "m": question mark
+
+::
+
+ >>> seg(test_item, symbol="fgm")
+ ['如图所示，则', '[FORMULA]', '的面积是', '[MARK]', '。', '[FIGURE]']
+ >>> seg(test_item, symbol="tfgm")
+ ['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']
+
+In addition，sif4sci function is also provided, which can easily convert items into the result processed by Structural Component Segmentation
+
+::
+
+ >>> segments = sif4sci(item["stem"], figures=figures, tokenization=False)
+ >>> segments
+ ['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\SIFChoice', \FigureID{1}]
+
+- When calling this function, you can selectively output a certain type of data according to your needs
+
+::
+
+ >>> segments.formula_segments
+ ['ABC',
+ 'BC',
+ 'AB',
+ 'AC',
+ '\\bigtriangleup ABC',
+ 'I',
+ 'II',
+ 'III',
+ 'I,II,III',
+ 'p_1,p_2,p_3']
+
+- Similar to seg function, sif4sci function also provides depth options to help with your research ----- By modifying the ``symbol`` parameter, different components can be transformed into specific markers.
+
+::
+
+ >>> sif4sci(item["stem"], figures=figures, tokenization=False, symbol="tfgm")
+ ['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']
diff --git a/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst b/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
new file mode 100644
index 00000000..3901f4cb
--- /dev/null
+++ b/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
@@ -0,0 +1,47 @@
+Semantic Component Segmentation
+------------
+
+Because multiple-choice questions are given in the form of dict, it is necessary to convert them into text format while retaining their data relationship. This function can be realized by dict2str4sif function which can convert multiple-choice question items into character format and identify question stem and options。
+
+
+Basic Usage
+++++++++++++++++++
+
+::
+
+ >>> item = {
+ ...     "stem": r"若复数$z=1+2 i+i^{3}$，则$|z|=$",
+ ...     "options": ['0', '1', r'$\sqrt{2}$', '2'],
+ ... }
+ >>> dict2str4sif(item) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+
+Optional additional parameters / interfaces
+++++++++++++++++++++++
+
+1.add_list_no_tag: if this parameter is true, it means that you need to count the labels in the options section.
+
+::
+
+ >>> dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+ 
+ >>> dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2$\\SIFTag{options_end}$'
+
+2.tag_mode: The location for the label can be selected using this parameter. 'delimiter' is to label both the beginning and the end,'head' is to label only the head, and 'tail' is to label only the tail.
+
+::
+
+ >>> dict2str4sif(item, tag_mode="head") # doctest: +ELLIPSIS
+ '$\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{options}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2'
+ 
+ >>> dict2str4sif(item, tag_mode="tail") # doctest: +ELLIPSIS
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options}$'
+
+3.key_as_tag: If this parameter is false, this process will only adds $\SIFSep$ between the options without distinguishing the type of segmentation label.
+
+::
+
+ >>> dict2str4sif(item, key_as_tag=False)
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
\ No newline at end of file
diff --git a/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst b/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
new file mode 100644
index 00000000..8661c3d6
--- /dev/null
+++ b/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
@@ -0,0 +1,67 @@
+Structural Component Segmentation
+------------
+
+This step is to segment sliced items. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
+
+
+There are two modes:
+
+* linear mode: it is used for text processing (word segmentation using jieba library);
+
+* ast mode: it is used to parse the formula.
+
+Basic Usage
+++++++++++++++++++
+
+::
+
+ >>> test_item = r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$"
+ >>> seg(test_item)
+ >>> ['如图所示，则', '\\bigtriangleup ABC', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+
+Optional additional parameters/interfaces
+++++++++++++++++++++++
+
+1.describe: count the number of elements of different types
+
+::
+
+ >>> s.describe()
+ {'t': 3, 'f': 1, 'g': 1, 'm': 1}
+
+2.filter: this interface can screen out one or more types of elements.
+
+Using this interface, you can pass in a "keep" parameter or a special character directly to choose what type of elements to retain.
+
+Element type represented by symbol:
+   "t": text
+   "f": formula
+   "g": figure
+   "m": question mark
+   "a": tag
+   "s": sep tag
+
+::
+
+ >>> with s.filter("f"):
+ ...     s
+ ['如图所示，则', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+ >>> with s.filter(keep="t"):
+ ...     s
+ ['如图所示，则', '的面积是', '。']
+
+3.symbol: this interface can convert some types of data into special symbols
+
+Element type represented by symbol:
+
+-   "t": text
+-   "f": formula
+-   "g": figure
+-   "m": question mark
+
+::
+
+ >>> seg(test_item, symbol="fgm")
+ ['如图所示，则', '[FORMULA]', '的面积是', '[MARK]', '。', '[FIGURE]']
+ >>> seg(test_item, symbol="tfgm")
+ ['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']
diff --git a/docs/source/tutorial/en/sif.rst b/docs/source/tutorial/en/sif.rst
index 0cbe7cf3..877cb503 100644
--- a/docs/source/tutorial/en/sif.rst
+++ b/docs/source/tutorial/en/sif.rst
@@ -1,2 +1,145 @@
 Standard Item Format
-====================
+===============
+
+version: 0.2
+
+For the convenience of follow-up research and use, we need a unified test question grammar standard.
+
+Grammar Rules
+-----------
+
+1. Only Chinese characters, Chinese and English punctuation and line breaks are allowed in the question text.
+
+2. Represent underlines of blanks and brackets of choices with ``\$\SIFBlank\$`` and ``\$\SIFChoice\$`` respectively.
+
+3. We use ``$\FigureID{ uuid }$`` or Base64 to represent pictures. Especially, ``$\FormFigureID{ uuid }$`` is used to represent formulas pictures.
+
+4. Text format description: we represent text in different styles with ``$\textf{item,CHAR_EN}$``. Currently, we have defined some styles: b-bold, i-italic, u-underline, w-wave, d-dotted, t-title. CHAR_EN Labels can be mixed and sorted alphabetically. An example: $\textf{EduNLP, b}$ looks **EduNLP**
+
+5. Other mathematical symbols like English letters, Roman characters and numbers need to be expressed in latex format, that is, embedded in `$$`.
+
+6. For the entry standard of molecular formula, please refer to `INCHI <https://zh.wikipedia.org/wiki/%E5%9B%BD%E9%99%85%E5%8C%96%E5%90%88%E7%89%A9%E6%A0%87%E8%AF%86>`_ for the time being.
+
+7. Currently, there are no requirements for latex internal syntax.
+
+::
+
+  1. Item -> CHARACTER|EN_PUN_LIST|CH_PUN_LIST|FORMULA|QUES_MARK
+  2. EN_PUN_LIST -> [',', '.', '?', '!', ':', ';', '\'', '\"', '(', ')', ' ','_','/','|','\\','<','>','[',']','-']
+  3. CH_PUN_LIST -> ['，', '。', '！', '？', '：','；', '‘', '’', '“', '”', '（', '）', ' ', '、','《','》','—','．']
+  4. FORMULA -> $latex formula$ | $\FormFigureID{UUID}$ | $\FormFigureBase64{BASE64}$
+  5. FIGURE -> $\FigureID{UUID}$ | $\FigureBase64{BASE64}$
+  6. UUID -> [a-zA-Z\-0-9]+
+  7. CHARACTER -> CHAR_EN | CHAR_CH
+  8. CHAR_EN -> [a-zA-Z]+
+  9. CHAR_CH -> [\u4e00-\u9fa5]+
+  10. DIGITAL -> [0-9]+
+  11. QUES_MARK -> $\SIFBlank$ | $\SIFChoice$
+
+
+Tips
++++++++++++++++
+
+1. Reserved characters and escape characters.
+
+2. Numbers.
+
+3. Choices and blanks.
+
+4. A single number or letter is also required to be between `$$` (automatic verification could already realize it).
+
+5. Try to make sure Chinese is not included in the latex formula such as ``\text{CHAR_CH}``.
+
+6. When importing data using MySQL database, an ``\`` is automatically ignored which needs to be further processed as``\\``.
+
+Examples
+-----------------
+
+Standard Format:
+
+::
+
+ 1. 若$x,y$满足约束条件$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，则$z=x+7 y$的最大值$\\SIFUnderline$'
+ 
+ 2. 已知函数$f(x)=|3 x+1|-2|x|$画出$y=f(x)$的图像求不等式$f(x)>f(x+1)$的解集$\\PictureID{3bf2ddf4-8af1-11eb-b750-b46bfc50aa29}$$\\PictureID{59b8bd14-8af1-11eb-93a5-b46bfc50aa29}$$\\PictureID{63118b3a-8b75-11eb-a5c0-b46bfc50aa29}$$\\PictureID{6a006179-8b76-11eb-b386-b46bfc50aa29}$$\\PictureID{088f15eb-8b7c-11eb-a86f-b46bfc50aa29}$
+
+Non-standard Format:
+
+1. Letters, numbers and mathematical symbols are mixed:
+
+    For example:
+    
+    ``完成下面的2x2列联表，``
+    
+    ``（单位：m3）``
+    
+    ``则输出的n=``
+    
+2. Some special mathematical symbols are not represented by the latex formula:
+
+    For example:
+    
+    ``命题中真命题的序号是 ①``
+    
+    ``AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．若D为AC的中点``
+    
+3. There are unicode encoded characters in the text:
+
+    For example:
+    ``则$a$的取值范围是（\u3000\u3000）``
+
+Functions for judging whether text is in SIF format and converting to SIF format
+--------------------------------------------
+
+Call the Library
+++++++++
+::
+
+    from EduNLP.SIF import is_sif, to_sif
+
+is_sif
++++++++++++
+
+::
+
+    >>> text1 = '若$x,y$满足约束条件' 
+    >>> text2 = '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' 
+    >>> text3 = '则$z=x+7 y$的最大值$\\SIFUnderline$'
+    >>> text4 = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
+    >>> is_sif(text1)
+    True
+    >>> is_sif(text2)
+    True
+    >>> is_sif(text3)
+    True
+    >>> is_sif(text4)
+    False
+
+to_sif
++++++++++++
+
+::
+
+    >>> text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
+    >>> to_sif(text)
+    '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'
+
+
+Change Log
+----------------
+
+2021-05-18
+
+Changed
+
+1. Originally, we use ``\$\SIFUnderline\$`` and ``\$\SIFBracket\$`` to represent underlines of blanks and brackets of choices. Now we represent them with ``\$\SIFBlank\$`` and ``\$\SIFChoice\$``.
+
+2. Originally, we used ``$\PictureID{ uuid }$`` to represent pictures, but now we use ``$\FigureID{ uuid }$`` instead. Especially, ``$\FormFigureID{ uuid }$`` is used to represent formulas pictures.
+
+2021-06-28 
+  
+Added:
+
+1. There should not be line breaks between the notation ``$$``.
+
+2. Add text format description.
diff --git a/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst b/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
new file mode 100644
index 00000000..eb624e94
--- /dev/null
+++ b/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
@@ -0,0 +1,9 @@
+GensimSegTokenizer
+=====================
+
+By default, the pictures, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text, formulas and labels. Also, the tokenizer uses linear analysis method for text and abstract analysis method of syntax tree for formulas.
+
+Compared to GensimWordTokenizer, the main differences are:
+
+* It provides the depth option for segmentation position, such as \SIFSep and \SIFTag.
+* By default, labels are inserted in the header of item components (such as text and formula).
\ No newline at end of file
diff --git a/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst b/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
new file mode 100644
index 00000000..98d4b10a
--- /dev/null
+++ b/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
@@ -0,0 +1,23 @@
+GensimWordTokenizer
+=====================
+
+By default, the pictures, blanks in the question text and other parts of the incoming item are converted into special characters for data security and the tokenization of text, formulas, labels and separators. Also, the tokenizer uses linear analysis method for text and abstract syntax tree method for formulas respectively. You can choose each of them by ``general`` parameter:
+
+-true, it means that the incoming item conforms to SIF and the linear analysis method should be used.
+-false, it means that the incoming item doesn't conform to SIF and the abstract syntax tree method should be used.
+
+Examples
+----------
+        
+::
+
+        >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+        >>> print(token_item.tokens[:10])
+        ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+        >>> tokenizer = GensimWordTokenizer(symbol="fgmas", general=False)
+        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+        >>> print(token_item.tokens[:10])
+        ['公式', '[FORMULA]', '如图', '[FIGURE]', '[FORMULA]', '约束条件', '公式', '[FORMULA]', '[SEP]', '[FORMULA]']
diff --git a/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst b/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
new file mode 100644
index 00000000..8c36e67c
--- /dev/null
+++ b/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
@@ -0,0 +1,31 @@
+PureTextTokenizer
+================
+
+By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security. At the same time, special formulas such as $\\FormFigureID{...}$ and $\\FormFigureBase64{...}$ are screened out to facilitate the tokenization of text and plain text formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
+
+Examples
+----------
+
+::
+
+    >>> tokenizer = PureTextTokenizer()
+    >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)[:10]
+    ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
+    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
+    >>> items = [{
+    ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+    ... "options": ["1", "2"]
+    ... }]
+    >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
diff --git a/docs/source/tutorial/en/tokenization/TextTokenizer.rst b/docs/source/tutorial/en/tokenization/TextTokenizer.rst
new file mode 100644
index 00000000..08991be6
--- /dev/null
+++ b/docs/source/tutorial/en/tokenization/TextTokenizer.rst
@@ -0,0 +1,27 @@
+TextTokenizer
+================
+
+By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text and formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
+
+
+Examples
+----------
+
+::
+
+    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+    >>> tokenizer = TextTokenizer()
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
+    >>> items = [{
+    ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+    ... "options": ["1", "2"]
+    ... }]
+    >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
diff --git a/docs/source/tutorial/en/tokenize.rst b/docs/source/tutorial/en/tokenize.rst
new file mode 100644
index 00000000..3411b74b
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize.rst
@@ -0,0 +1,172 @@
+Tokenization
+=======
+
+Tokenization, known as word segmentation and sentence segmentation, is a basic but very important step in the field of NLP.
+In EduNLP, we divided Tokenization into different levels according to different granularity. To avoid ambiguity, we define as follows:
+
+* Word/char level: word segmentation
+
+* Sentence level: sentence segmentation
+
+* Resource level: tokenization
+
+This module provides tokenization function of question text, converting questions into token sequences to facilitate the vectorization of questions. After that, each element in the sliced item needs word segmentation. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
+
+There are two modes: one is linear mode, which is used for text processing (word segmentation using jieba library). The other one is ast mode, which is used to parse the formula.
+
+Word Segmentation
+-------
+
+Text-tokenization: A sentence (without formulas) consists of several "words" in order. The process of dividing a sentence into several words is called "Text-tokenization". According to the granularity of "words", it can be subdivided into "Word-tokenization" and "Char-tokenization".
+
+::
+
+   - Word-tokenization: each phrase is a token.
+   
+   - Char-tokenization: each character is a token.
+    
+
+Text-tokenization is divided into two main steps:
+
+1. Text-tokenization:
+
+   - Word-tokenization: use the word segmentation tool to segment and extract words from the question text. Our project supports `jieba`.
+
+   - Char-tokenization: process text by character.
+
+2. Filter: filter the specified stopwords.
+
+   The default stopwords used in this project:`[stopwords] <https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt>`_
+   You can also use your own stopwords. The following example demonstrates how to use.
+
+Examples:
+
+::
+
+   from EduNLP.SIF.tokenization.text import tokenize 
+   >>> text = "三角函数是基本初等函数之一"
+   >>> tokenize(text, granularity="word")
+   ['三角函数', '初等', '函数']
+   
+   >>> tokenize(text, granularity="char")
+   ['三', '角', '函', '数', '基', '初', '函', '数']
+    
+Sentence Segmentation
+-------
+
+During the process of sentence segmentation, a long document is divided into several sentences. Each sentence is a "token" (to be realized).
+
+Tokenization
+-------
+Tokenization is comprehensive analysis. In this process, sentences with formulas are segmented into several markers. Each marker is a "token".
+
+The implementation of this function is tokenize function. The required results can be obtained by passing in items after Structural Component Segmentation.
+
+::
+
+   from EduNLP.Tokenizer import get_tokenizer
+   >>> items = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
+   >>> tokenize(SegmentList(items))
+   ['如图所示', '三角形', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]
+   >>> tokenize(SegmentList(items),formula_params={"method": "ast"})
+   ['如图所示', '三角形', <Formula: ABC>, '面积', '\\\\SIFBlank', \\FigureID{1}]
+
+
+
+You can view ``./EduNLP/Tokenizer/tokenizer.py`` and ``./EduNLP/Pretrain/gensim_vec.py`` for more tokenizers. We provide some encapsulated tokenizers for users to call them conveniently. Following is a complete list of tokenizers:
+
+- TextTokenizer
+
+- PureTextTokenizer
+
+- GensimSegTokenizer
+
+- GensimWordTokenizer
+
+
+TextTokenizer
++++++++++++++++++++++
+
+By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text and formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
+
+::
+
+   >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+   >>> tokenizer = TextTokenizer()
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+   >>> items = [{
+   ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+   ... "options": ["1", "2"]
+   ... }]
+   >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+
+PureTextTokenizer
++++++++++++++++++++++
+
+By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security. At the same time, special formulas such as $\\FormFigureID{...}$ and $\\FormFigureBase64{...}$ are screened out to facilitate the tokenization of text and plain text formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
+
+::
+
+   >>> tokenizer = PureTextTokenizer()
+   >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)[:10]
+   ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
+   >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+   >>> items = [{
+   ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+   ... "options": ["1", "2"]
+   ... }]
+   >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+
+GensimWordTokenizer
++++++++++++++++++++++++
+
+By default, the pictures, blanks in the question text and other parts of the incoming item are converted into special characters for data security and the tokenization of text, formulas, labels and separators. Also, the tokenizer uses linear analysis method for text and abstract syntax tree method for formulas respectively. You can choose each of them by ``general`` parameter:
+
+-true, it means that the incoming item conforms to SIF and the linear analysis method should be used.
+-false, it means that the incoming item doesn't conform to SIF and the abstract syntax tree method should be used.
+
+GensimSegTokenizer
+++++++++++++++++++++
+
+By default, the pictures, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text, formulas and labels. Also, the tokenizer uses linear analysis method for text and abstract analysis method of syntax tree for formulas.
+
+Compared to GensimWordTokenizer, the main differences are:
+
+* It provides the depth option for segmentation position, such as \SIFSep and \SIFTag.
+* By default, labels are inserted in the header of item components (such as text and formulas).
+
+Examples
+----------
+        
+::
+
+   >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+   >>> tokenizer = GensimWordTokenizer(symbol="fgmas", general=False)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', '[FORMULA]', '约束条件', '公式', '[FORMULA]', '[SEP]', '[FORMULA]']
diff --git a/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst b/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst
new file mode 100644
index 00000000..1a8d4950
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst	
@@ -0,0 +1,3 @@
+Sentence Segmentation
+-------
+During the process of sentence segmentation, a long document is divided into several sentences. Each sentence is a "token" (to be realized).
diff --git a/docs/source/tutorial/en/tokenize/Tokenization.rst b/docs/source/tutorial/en/tokenize/Tokenization.rst
new file mode 100644
index 00000000..fad25912
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize/Tokenization.rst
@@ -0,0 +1,29 @@
+Tokenization
+-------
+Tokenization is comprehensive analysis. In this process, sentences with formulas are segmented into several markers. Each marker is a "token".
+We provide some encapsulated tokenizers for users to call them conveniently. The following is a complete list of tokenizers.
+
+Examples
+    
+::
+
+    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+    >>> tokenizer = TextTokenizer()
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
+
+
+
+You can view ``./EduNLP/Tokenizer/tokenizer.py`` and ``./EduNLP/Pretrain/gensim_vec.py`` for more tokenizers. Following is a complete list of tokenizers:
+
+.. toctree::
+  :maxdepth: 1
+  :titlesonly:
+
+  ../tokenization/TextTokenizer
+  ../tokenization/PureTextTokenizer
+  ../tokenization/GensimSegTokenizer
+  ../tokenization/GensimWordTokenizer
diff --git a/docs/source/tutorial/en/tokenize/WordSegmentation.rst b/docs/source/tutorial/en/tokenize/WordSegmentation.rst
new file mode 100644
index 00000000..a85f4dae
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize/WordSegmentation.rst
@@ -0,0 +1,36 @@
+Word segmentation
+-------
+
+Text-tokenization: A sentence (without formulas) consists of several "words" in order. The process of dividing a sentence into several words is called "Text-tokenization". According to the granularity of "words", it can be subdivided into "Word-tokenization" and "Char-tokenization".
+
+::
+
+   - Word-tokenization: each phrase is a token.
+
+   - Char-tokenization: each character is a token.
+
+
+Text-tokenization is divided into two main steps:
+
+1. Text-tokenization:
+
+   - Word-tokenization: use the word segmentation tool to segment and extract words from the question text. Our project supports `jieba`.
+
+   - Char-tokenization: process text by character.
+
+2. Filter: filter the specified stopwords.
+
+   The default stopwords used in this project:`[stopwords] <https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt>`_
+   You can also use your own stopwords. The following example demonstrates how to use.
+
+Examples:
+
+::
+    
+    >>> text = "三角函数是基本初等函数之一"
+    >>> tokenize(text, granularity="word")
+    ['三角函数', '初等', '函数']
+    
+    >>> tokenize(text, granularity="char")
+    ['三', '角', '函', '数', '基', '初', '函', '数']
+    
diff --git a/docs/source/tutorial/en/vectorization.rst b/docs/source/tutorial/en/vectorization.rst
new file mode 100644
index 00000000..5b744eeb
--- /dev/null
+++ b/docs/source/tutorial/en/vectorization.rst
@@ -0,0 +1,157 @@
+Vectorization
+=========
+
+This section provides a simple interface to convert the incoming items into vectors directly. Currently, the option of whether to use the pre training model is provided. You can choose according to your needs. If you don't want to use the pre-trained model, you can call D2V directly, or call get_pretrained_i2v function if you want to use the pre-trained model.
+
+- Don't use the pre-trained model
+
+- Use the pre-trained model
+
+Overview Flow
+---------------------------
+
+1.Perform `syntax parsing <parse.rst>`_ on incoming items to get items in SIF format；
+
+2.Perform `component segmentation <seg.rst>`_ on sif_items;
+
+3.Perform `tokenization <tokenize.rst>`_ on segmented items;
+
+4.Use the existing or pre-trained model we provided to convert the tokenized items into vectors.
+
+
+Don't use the pre-trained model: call existing models directly
+------------------------------------
+
+You can use any pre-trained model provided by yourself (just give the storage path of the model) to convert the given question text into vectors.
+
+* Advantages: it is flexible to use your own model and its parameters can be adjusted freely.
+
+Import modules
+++++++++++
+
+::
+
+   from EduNLP.I2V import D2V,W2V,get_pretrained_i2v
+   from EduNLP.Vector import T2V,get_pretrained_t2v
+
+Models provided
+++++++++++++++++++++
+
+- W2V
+
+- D2V
+
+- T2V
+
+W2V
+<<<<<<<<<
+
+This model directly uses the relevant model methods in the gensim library to convert words into vectors. Currently, there are four methods:
+
+ - FastText
+
+ - Word2Vec
+
+ - KeyedVectors
+
+::
+
+   >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") # doctest: +ELLIPSIS
+   >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
+   >>> item_vector # doctest: +ELLIPSIS
+   array([[...]], dtype=float32)
+
+D2V
+<<<<<<<<<<<<
+
+This model is a comprehensive processing method which can convert items into vectors. Currently, the following methods are provided:
+
+- d2v: call doc2vec module in gensim library to convert items into vectors.
+
+- BowLoader: call corpora module in gensim library to convert docs into bows.
+
+- TfidfLoader: call TfidfModel module in gensim library to convert docs into bows.
+
+::
+
+   >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$"}
+   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+   >>> i2v(item)
+   ([array([ 4.76559885e-02, -1.60574958e-01,  1.94614579e-03,  2.40295693e-01,
+   2.24517003e-01, -3.24351490e-02,  4.35789041e-02, -1.65670961e-02,...
+
+T2V
+<<<<<<<<<<
+
+You can use any pre-trained model provided by yourself to represent the segmentation sequences of a group of questions as vectors (just give the storage path of the model).
+
+- Advantages: the model and its parameters can be adjusted independently and has strong flexibility.
+
+Input
+^^^^^^^^^^
+
+Types: list
+Contents: the combination of each question segmentation sequences in one question group.
+>You can transfer question text (`str` type) to tokens using ``GensimWordTokenizer`` model
+
+::
+
+   >>> token_items=['公式','[FORMULA]','公式','[FORMULA]','如图','[FIGURE]','x',',','y','约束条件','[SEP]','z','=','x','+','7','y','最大值','[MARK]']
+   >>> path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> t2v = T2V('d2v',filepath=path)
+   >>> t2v(token_items)
+   [array([ 0.0256574 ,  0.06061139, -0.00121044, -0.0167674 , -0.0111706 ,
+   0.05325712, -0.02097339, -0.01613594,  0.02904145,  0.0185046 ,...
+
+Specific process of processing
+++++++++++++++++++++
+
+1.Call get_tokenizer function to get the result after word segmentation;
+
+2.Select the model provided for vectorization depending on the model used.
+
+
+Use the pre-training model: call get_pretrained_i2v directly
+---------------------------------------------
+
+Use the pre-training model provided by EduNLP to convert the given question text into vectors.
+
+* Advantages: Simple and convenient.
+
+* Disadvantages: Only the model given in the project can be used, which has great limitations.
+
+* Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
+
+Selection and Use of Models
+##################
+
+Select the pre-training model according to the subject:
+
++--------------------+------------------------+
+|   Pre-training model name  | Subject of model training data |
++====================+========================+
+|    d2v_all_256     |        all subject          |
++--------------------+------------------------+
+|    d2v_sci_256     |         Science           |
++--------------------+------------------------+
+|    d2v_lit_256     |         Arts           |
++--------------------+------------------------+
+|    d2v_eng_256     |         English           |
++--------------------+------------------------+
+
+
+The concrete process of processing
+##################
+
+1.Download the corresponding preprocessing model
+
+2.Transfer the obtained model to D2V and process it with D2V
+  Convert the obtained model into D2V and process it through D2V
+
+Examples:
+
+::
+
+  >>> i2v = get_pretrained_i2v("d2v_sci_256")
+  >>> i2v(item)
diff --git a/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst b/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
new file mode 100644
index 00000000..41dcab64
--- /dev/null
+++ b/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
@@ -0,0 +1,42 @@
+Use the pre-training model: call get_pretrained_i2v directly
+---------------------------------------------
+
+Use the pre-training model provided by EduNLP to convert the given question text into vectors.
+
+* Advantages: Simple and convenient.
+
+* Disadvantages: Only the model given in the project can be used, which has great limitations.
+
+* Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
+
+Selection and use of models
+##################
+
+Select the pre-training model according to the subject:
+
++--------------------+------------------------+
+|   Pre-training model name  | Subject of model training data |
++====================+========================+
+|    d2v_all_256     |        all subject          |
++--------------------+------------------------+
+|    d2v_sci_256     |         Science           |
++--------------------+------------------------+
+|    d2v_lit_256     |         Arts           |
++--------------------+------------------------+
+|    d2v_eng_256     |         English           |
++--------------------+------------------------+
+
+The concrete process of processing
+##################
+
+1.Download the corresponding preprocessing model
+
+2.Transfer the obtained model to D2V and process it with D2V
+  Convert the obtained model into D2V and process it through D2V
+
+Examples：
+
+::
+
+  >>> i2v = get_pretrained_i2v("d2v_sci_256")
+  >>> i2v(item)
diff --git a/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst b/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
new file mode 100644
index 00000000..2989f8ba
--- /dev/null
+++ b/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
@@ -0,0 +1,21 @@
+Don't use the pre-trained model: call existing models directly
+------------------------------------
+
+You can use any pre-trained model provided by yourself (just give the storage path of the model) to convert the given question text into vectors.
+
+* Advantages: it is flexible to use your own model and its parameters can be adjusted freely.
+
+Specific process of processing
+++++++++++++++++++++
+
+1.Call get_tokenizer function to get the result after word segmentation;
+
+2.Select the model provided for vectorization depending on the model used.
+
+Examples：
+
+::
+
+  >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+  >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+  >>> i2v(item)

From c84f5ac96d29b4f2c444336634785f27163d4d6f Mon Sep 17 00:00:00 2001
From: fannazya <bfsudcs_fanzy@163.com>
Date: Sat, 23 Oct 2021 20:24:15 +0800
Subject: [PATCH 145/149] Revert "Add English version tutorial"

This reverts commit d3e44223596f12c4821a733bfb8d528800856b3f.
---
 docs/source/tutorial/en/index.rst             |  52 +---
 docs/source/tutorial/en/parse.rst             | 290 ------------------
 .../parse/FormulaSyntaxStructureParsing.rst   | 168 ----------
 .../en/parse/TextSyntaxStructureParsing.rst   |  72 -----
 docs/source/tutorial/en/pretrain.rst          | 130 --------
 docs/source/tutorial/en/pretrain/loading.rst  |  11 -
 docs/source/tutorial/en/pretrain/pub.rst      |  74 -----
 docs/source/tutorial/en/pretrain/start.rst    |  24 --
 docs/source/tutorial/en/seg.rst               | 187 -----------
 .../en/seg/SemanticComponentSegmentation.rst  |  47 ---
 .../seg/StructuralComponentSegmentation.rst   |  67 ----
 docs/source/tutorial/en/sif.rst               | 145 +--------
 .../en/tokenization/GensimSegTokenizer.rst    |   9 -
 .../en/tokenization/GensimWordTokenizer.rst   |  23 --
 .../en/tokenization/PureTextTokenizer.rst     |  31 --
 .../en/tokenization/TextTokenizer.rst         |  27 --
 docs/source/tutorial/en/tokenize.rst          | 172 -----------
 .../en/tokenize/Sentence Segmentation.rst     |   3 -
 .../tutorial/en/tokenize/Tokenization.rst     |  29 --
 .../tutorial/en/tokenize/WordSegmentation.rst |  36 ---
 docs/source/tutorial/en/vectorization.rst     | 157 ----------
 .../en/vectorization/WithPre-trainedModel.rst |  42 ---
 .../vectorization/WithoutPre-trainedModel.rst |  21 --
 23 files changed, 2 insertions(+), 1815 deletions(-)
 delete mode 100644 docs/source/tutorial/en/parse.rst
 delete mode 100644 docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
 delete mode 100644 docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
 delete mode 100644 docs/source/tutorial/en/pretrain.rst
 delete mode 100644 docs/source/tutorial/en/pretrain/loading.rst
 delete mode 100644 docs/source/tutorial/en/pretrain/pub.rst
 delete mode 100644 docs/source/tutorial/en/pretrain/start.rst
 delete mode 100644 docs/source/tutorial/en/seg.rst
 delete mode 100644 docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
 delete mode 100644 docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
 delete mode 100644 docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
 delete mode 100644 docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
 delete mode 100644 docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
 delete mode 100644 docs/source/tutorial/en/tokenization/TextTokenizer.rst
 delete mode 100644 docs/source/tutorial/en/tokenize.rst
 delete mode 100644 docs/source/tutorial/en/tokenize/Sentence Segmentation.rst
 delete mode 100644 docs/source/tutorial/en/tokenize/Tokenization.rst
 delete mode 100644 docs/source/tutorial/en/tokenize/WordSegmentation.rst
 delete mode 100644 docs/source/tutorial/en/vectorization.rst
 delete mode 100644 docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
 delete mode 100644 docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst

diff --git a/docs/source/tutorial/en/index.rst b/docs/source/tutorial/en/index.rst
index 4c8cc040..108a9487 100644
--- a/docs/source/tutorial/en/index.rst
+++ b/docs/source/tutorial/en/index.rst
@@ -1,52 +1,2 @@
 Get Started
-=====
-
-*  `Standard Item Format <sif.rst>`_
-
-*  `Syntax Parsing <parse.rst>`_
-
-*  `Component Segmentation <seg.rst>`_
-
-*  `Tokenization <tokenize.rst>`_
-
-*  `Pre-training <pretrain.rst>`_
-
-*  `Vectorization <vectorization.rst>`_
-
-Main process
-----------
-
-.. figure:: ../../_static/new_flow.png
-
-* `Syntax Parsing <parse.rst>`_ : Its function is to convert the incoming item into SIF format, which means letters and numbers should be between ``$...$`` and the brackets and underlines of the choice questions should be converted to special symbols we defined in SIF)
-
-* `Component Segmentation <seg.rst>`_ : Its function is to segment items in SIF format according to the types of items, so as to serve the later tokenization module.(that is, elements in different types can be tokenized using their corresponding methods)。
-
-* `Tokenization <tokenize.rst>`_: Its function is to tokenize segmented items, so as to serve the later tokenization module.
-  Generally, the tokenization method in the text form can be used directly. For formulas, the ast method can also be used for parsing(call the formula module)；
-
-* `Vectorization <vectorization.rst>`_: This part mainly calls I2V class and its subclasses. Its function is to vectorize the list of tokenized items, so as to get the corresponding static vectors.
-  For vectorization module, You can call your own trained model or directly call the provided pre-training model(call get_ pretrained_ I2V module).
-
-* **Downstream Model**：Process the obtained vectors to get the desired results。
-
-Examples
---------
-
-To help you quickly understand the functions of this project, this section only shows the usages of common function interface. Intermediate function modules (such as parse, formula, segment, etc.) and more subdivided interface methods are not shown. For further study, please refer to relevant documents.
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: tokenize_gallery
-    :glob:
-    
-    Tokenization  <../../build/blitz/tokenizer/tokenizer.ipynb>
-
-
-
-.. nbgallery::
-    :caption: This is a thumbnail gallery:
-    :name: vectorization_gallery
-    :glob:
-    
-    Vectorization  <../../build/blitz/vectorization/total_vector.ipynb>
+===========
diff --git a/docs/source/tutorial/en/parse.rst b/docs/source/tutorial/en/parse.rst
deleted file mode 100644
index 69608c5e..00000000
--- a/docs/source/tutorial/en/parse.rst
+++ /dev/null
@@ -1,290 +0,0 @@
-Syntax Parsing
-=========
-
-In educational resources, texts and formulas have internal implicit or explicit syntax structures. It is of great benefit for further processing to extract these structures.
-* Text syntax structure parsing
-
-* Formula syntax structure parsing
-
-The purpose is as follows:
-
-
-1. Represent underlines of blanks and brackets of choices with special identifiers. And the alphabets and formulas should be wrapped with $$, so that items of different types can be cut accurately through the symbol $.
-2. Determine whether the current item is legal and report the error type.
-
-Specific processing content
---------------------
-
-1.Its function is to match alphabets and numbers other than formulas. Only the alphabets and numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
-
-2.Match brackets like "( )" (both English format and Chinese format), that is, brackets with no content or spaces, which should be replaced with ``$\\SIFChoice$``
-
-3.Match continuous underscores or underscores with spaces to replace them with ``$\\SIFBlank$``.
-
-4.Match latex formulas，check the completeness and analyzability of latex formulas, and report an error for illegal formula.
-
-Formula syntax structure parsing
---------------------
-
-This section is mainly realized by EduNLP.Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
-
-Introduction of Main Introduction
-+++++++++++++++
-
-1.Formula: determine whether the single formula passed in is in str form. If so, use the ast method for processing, otherwise an error will be reported. In addition, parameter variable_standardization is given. If this parameter is true, the variable standardization method will be used to make sure the same variable has the same variable number.
-
-2.FormulaGroup: If you need to pass in a formula set, you can call this interface to get an ast forest. The tree structure in the forest is the same as that of Formula.
-
-Formula
->>>>>>>>>>>>
-
-Formula: firstly, in the word segmentation function, the formula of the original text is segmented. In addition, ``Formula parse tree`` function is provided, which can represent the abstract syntax analysis tree of mathematical formula in the form of text or picture.
-
-This module also provides the function of formula variable standardization, such as determining that 'x' in several sub formulas is the same variable.
-
-Call the library
-+++++++++
-
-::
-
-   import matplotlib.pyplot as plt
-   from EduNLP.Formula import Formula
-   from EduNLP.Formula.viz import ForestPlotter
-
-Initialization
-+++++++++
-
-Incoming parameters: item
-
-Item is the latex formula or the abstract syntax parse tree generated after the formula is parsed and its type is str or List[Dict].
-
-::
-
-   >>> f=Formula("x^2 + x+1 = y")
-   >>> f
-   <Formula: x^2 + x+1 = y>
-
-View the specific content after formula segmentation
-++++++++++++++++++++++++++++
-
-- View node elements after formula segmentation
-
-::
-
-   >>> f.elements
-   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
-   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
-   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]
-
-- View the abstract parse tree of formulas
-
-::
-
-   >>> f.ast
-   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   'structure': {'bro': [None, 3],'child': [1, 2],'father': None,'forest': None}},
-   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},
-   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
-   {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
-   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
-   'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
-   'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
-   'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
-   'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},
-   'structure': {'bro': [7, None],'child': None,'father': None,'forest': None}}]
-
-   >>> print('nodes: ',f.ast_graph.nodes)
-   nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]
-   >>> print('edges: ' ,f.ast_graph.edges)
-   edges:  [(0, 1), (0, 2)]
-
-- show the abstract parse tree by a picture
-
-::
-
-   >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
-   >>> plt.show()
-
-
-.. figure:: ../../_static/formula.png
-
-
-Variable standardization
-+++++++++++
-
-This parameter makes the same variable have the same variable number.
-
-For example: the number of variable ``x`` is ``0`` and the number of variable ``y`` is ``1``.
-
-::
-
-   >>> f.variable_standardization().elements
-   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},
-   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},
-   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
-   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
-
-FormulaGroup
->>>>>>>>>>>>>>>
-
-Call ``FormulaGroup`` class to parse the equations. The related attributes and functions are the same as those above.
-
-::
-
-   import matplotlib.pyplot as plt
-   from EduNLP.Formula import Formula
-   from EduNLP.Formula import FormulaGroup
-   from EduNLP.Formula.viz import ForestPlotter
-   >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
-   >>> fs
-   <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
-   >>> fs.elements
-   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
-   {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},
-   {'id': 8, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 9, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},
-   {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},
-   {'id': 13, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},
-   {'id': 15, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 16, 'type': 'mathord', 'text': '\\pi', 'role': None}]
-   >>> fs.ast
-   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   'structure': {'bro': [None, 3],
-      'child': [1, 2],
-      'father': None,
-      'forest': None}},
-   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   'structure': {'bro': [None, 2],
-      'child': None,
-      'father': 0,
-      'forest': [6, 12]}},
-   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
-   {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
-   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
-   'structure': {'bro': [3, None],
-      'child': None,
-      'father': None,
-      'forest': [10, 14]}},
-   {'val': {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   'structure': {'bro': [None, 8],
-      'child': [6, 7],
-      'father': None,
-      'forest': None}},
-   {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   show more (open the raw output data in a text editor) ...
-   >>> fs.variable_standardization()[0]
-   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
-   >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
-
-.. figure:: ../../_static/formulagroup.png
-
-
-Text syntax structure parsing
---------------------
-
-This section is mainly realized by EduNLP.SIF.Parse module. Its main function is to extract letters and numbers in the text and convert them into standard format.
-
-This module is mainly used as an *middle module* to parse the input text. Users generally do not call this module directly.
-
-Introduction of main content
-+++++++++++++++
-
-1. Judge the type of the incoming text in the following order
-
-* is_chinese: its function is to match Chinese characters[\u4e00-\u9fa5].
- 
-* is_alphabet: its function is to match alphabets other than formulas. Only the alphabets between two Chinese characters will be corrected (wrapped with $$), and the rest of the cases are regarded as formulas that do not conform to latex syntax.
- 
-* is_number: its function is to match numbers other than formulas. Only the numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
- 
-2. Match latex formula
-
-* If Chinese characters appear in latex, print warning only once.
- 
-* Use _is_formula_legal function, check the completeness and analyzability of latex formula, and report an error for formulas that do not conform to latex syntax.
-
-Call the library
->>>>>>>>>>>>
-
-::
-
-   from EduNLP.SIF.Parser import Parser
-
-Input
->>>>>>>
-
-Types: str
-
-Content: question text
-
-::
-
-   >>> text1 = '生产某种零件的A工厂25名工人的日加工零件数_   _'
-   >>> text2 = 'X的分布列为(   )'
-   >>> text3 = '① AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．AC的中点为D'
-   >>> text4 = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
-
-Parsing
->>>>>>>>>>>>>>>>>>>>
-
-::
-
-   >>> text_parser1 = Parser(text1)
-   >>> text_parser2 = Parser(text2)
-   >>> text_parser3 = Parser(text3)
-   >>> text_parser4 = Parser(text4)
-
-Related parameters description(?)
->>>>>>>>>>>>
-
-- Try to convert text to standard format
-
-::
-
-   >>> text_parser1.description_list()
-   >>> print('text_parser1.text:',text_parser1.text)
-   text_parser1.text: 生产某种零件的$A$工厂$25$名工人的日加工零件数$\SIFBlank$
-   >>> text_parser2.description_list()
-   >>> print('text_parser2.text:',text_parser2.text)
-   text_parser2.text: $X$的分布列为$\SIFChoice$
-
-- Determine if the text has syntax errors
-
-::
-
-   >>> text_parser3.description_list()
-   >>> print('text_parser3.error_flag: ',text_parser3.error_flag)
-   text_parser3.error_flag:  1
-   >>> text_parser4.description_list()
-   >>> print('text_parser4.fomula_illegal_flag: ',text_parser4.fomula_illegal_flag)
-   text_parser4.fomula_illegal_flag:  1
-
diff --git a/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst b/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
deleted file mode 100644
index 2fc479c5..00000000
--- a/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
+++ /dev/null
@@ -1,168 +0,0 @@
-Formula syntax structure parsing
---------------------
-
-This section is mainly realized by EduNLP.Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
-
-Introduction of Main Content
-+++++++++++++++
-
-1.Formula: determine whether the single formula passed in is in str form. If so, use the ast method for processing, otherwise an error will be reported. In addition, parameter variable_standardization is given. If this parameter is true, the variable standardization method will be used to make sure the same variable has the same variable number.
-
-2.FormulaGroup: If you need to pass in a formula set, you can call this interface to get an ast forest. The tree structure in the forest is the same as that of Formula.
-
-Formula
->>>>>>>>>>>>
-
-Formula: firstly, in the word segmentation function, the formula of the original text is segmented. In addition, ``Formula parse tree`` function is provided, which can represent the abstract syntax analysis tree of mathematical formula in the form of text or picture.
-
-This module also provides the function of formula variable standardization, such as determining that 'x' in several sub formulas is the same variable.
-
-Initialization
-+++++++++
-
-Incoming parameters: item
-
-Item is the latex formula or the abstract syntax parse tree generated after the formula is parsed and its type is str or List[Dict].
-
-::
-
-   >>> f=Formula("x^2 + x+1 = y")
-   >>> f
-   <Formula: x^2 + x+1 = y>
-
-View the specific content after formula segmentation
-++++++++++++++++++++++++++++
-
-- View node elements after formula segmentation
-
-::
-
-   >>> f.elements
-   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
-   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
-   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]
-
-- View the abstract parsing tree of formulas
-
-::
-
-   >>> f.ast
-   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   'structure': {'bro': [None, 3],'child': [1, 2],'father': None,'forest': None}},
-   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},
-   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
-   {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
-   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
-   'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
-   'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
-   'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
-   'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},
-   'structure': {'bro': [7, None],'child': None,'father': None,'forest': None}}]
-
-   >>> print('nodes: ',f.ast_graph.nodes)
-   nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]
-   >>> print('edges: ' ,f.ast_graph.edges)
-   edges:  [(0, 1), (0, 2)]
-
-- show the abstract parse tree by a picture
-
-::
-
-   >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
-   >>> plt.show()
-
-.. figure:: ../../../_static/formula.png
-
-Variable Standardization
-+++++++++++
-
-This parameter makes the same variable have the same variable number.
-
-For example: the number of variable ``x`` is ``0`` and the number of variable ``y`` is ``1``.
-
-::
-
-   >>> f.variable_standardization().elements
-   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},
-   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},
-   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
-   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
-
-FormulaGroup
->>>>>>>>>>>>>>>
-
-Call ``FormulaGroup`` class to parse the equations. The related attributes and functions are the same as those above.
-
-::
-
-   >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
-   >>> fs
-   <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
-   >>> fs.elements
-   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
-   {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},
-   {'id': 8, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 9, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},
-   {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},
-   {'id': 13, 'type': 'bin', 'text': '+', 'role': None},
-   {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},
-   {'id': 15, 'type': 'rel', 'text': '=', 'role': None},
-   {'id': 16, 'type': 'mathord', 'text': '\\pi', 'role': None}]
-   >>> fs.ast
-   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   'structure': {'bro': [None, 3],
-      'child': [1, 2],
-      'father': None,
-      'forest': None}},
-   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   'structure': {'bro': [None, 2],
-      'child': None,
-      'father': 0,
-      'forest': [6, 12]}},
-   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
-   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
-   {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
-   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
-   {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
-   'structure': {'bro': [3, None],
-      'child': None,
-      'father': None,
-      'forest': [10, 14]}},
-   {'val': {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
-   'structure': {'bro': [None, 8],
-      'child': [6, 7],
-      'father': None,
-      'forest': None}},
-   {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
-   show more (open the raw output data in a text editor) ...
-   >>> fs.variable_standardization()[0]
-   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
-   >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
-
-.. figure:: ../../../_static/formulagroup.png
diff --git a/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst b/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
deleted file mode 100644
index bdfe6848..00000000
--- a/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
+++ /dev/null
@@ -1,72 +0,0 @@
-Text syntax structure parsing
---------------------
-
-This section is mainly realized by EduNLP.SIF.Parse module. Its main function is to extract letters and numbers in the text and convert them into standard format.
-
-This module is mainly used as an *middle module* to parse the input text. Users generally do not call this module directly.
-
-Introduction of Main Content
-+++++++++++++++
-
-1. Judge the type of the incoming text in the following order
-
-* is_chinese: its function is to match Chinese characters[\u4e00-\u9fa5].
-
-* is_alphabet: its function is to match alphabets other than formulas. Only the alphabets between two Chinese characters will be corrected (wrapped with $$), and the rest of the cases are regarded as formulas that do not conform to latex syntax.
-
-* is_number: its function is to match numbers other than formulas. Only the numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
-
-2. Match latex formula
-
-* If Chinese characters appear in latex, print warning only once.
-
-* Use _is_formula_legal function, check the completeness and analyzability of latex formula, and report an error for formulas that do not conform to latex syntax.
-
-Input
->>>>>>>
-
-Type: str
-
-Content：question text
-
-::
-
-   >>> text1 = '生产某种零件的A工厂25名工人的日加工零件数_   _'
-   >>> text2 = 'X的分布列为(   )'
-   >>> text3 = '① AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．AC的中点为D'
-   >>> text4 = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
-
-Parsing
->>>>>>>>>>>>>>>>>>>>
-
-::
-
-   >>> text_parser1 = Parser(text1)
-   >>> text_parser2 = Parser(text2)
-   >>> text_parser3 = Parser(text3)
-   >>> text_parser4 = Parser(text4)
-
-Related parameters description(?)
->>>>>>>>>>>>
-
-- Try to convert text to standard format
-
-::
-
-   >>> text_parser1.description_list()
-   >>> print('text_parser1.text:',text_parser1.text)
-   text_parser1.text: 生产某种零件的$A$工厂$25$名工人的日加工零件数$\SIFBlank$
-   >>> text_parser2.description_list()
-   >>> print('text_parser2.text:',text_parser2.text)
-   text_parser2.text: $X$的分布列为$\SIFChoice$
-
-- Determine if the text has syntax errors
-
-::
-
-   >>> text_parser3.description_list()
-   >>> print('text_parser3.error_flag: ',text_parser3.error_flag)
-   text_parser3.error_flag:  1
-   >>> text_parser4.description_list()
-   >>> print('text_parser4.fomula_illegal_flag: ',text_parser4.fomula_illegal_flag)
-   text_parser4.fomula_illegal_flag:  1
diff --git a/docs/source/tutorial/en/pretrain.rst b/docs/source/tutorial/en/pretrain.rst
deleted file mode 100644
index 9319b87d..00000000
--- a/docs/source/tutorial/en/pretrain.rst
+++ /dev/null
@@ -1,130 +0,0 @@
-Pre-training
-=======
-
-In the field of NLP, Pre-trained Language Models has become a very important basic technology.
-In this chapter, we will introduce the pre training tools in EduNLP:
-
-* How to train with a corpus to get a pre-trained model
-* How to load the pre-trained model
-* Public pre-trained models
-
-Import modules
-----------
-
-::
-
-   from EduNLP.I2V import get_pretrained_i2v
-   from EduNLP.Vector import get_pretrained_t2v
-
-Train the Model
-------------
-
-Call train_Vector function interface directly to make the training model easier. This section calls the relevant training models in the gensim library. At present, the training methods of "sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf" are provided. Parameter embedding_dim is also provided for users to determine vector dimension according to their needs.
-
-Basic Steps
-##################
-
-1.Determine the type of model and select the appropriate tokenizer (GensimWordTokenizer、 GensimSegTokenizer) to finish tokenization.
-
-2.Call train_vector function to get the required pre-trained model。
-
-Examples：
-
-::
-
-   >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
-   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-   >>> print(token_item.tokens[:10])
-   ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
-   
-   # 10 dimension with fasstext method
-   train_vector(sif_items, "../../../data/w2v/gensim_luna_stem_tf_", 10, method="d2v")
-
-
-Load models
---------
-
-Transfer the obtained model to the I2V module to load the model.
- 
-Examples：
-
-::
-
-   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
-   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
-
-The overview of our public model
-------------
-
-Version description
-##################
-
-First level version:
-
-* Public version 1 (luna_pub): college entrance examination
-* Public version 2 (luna_pub_large): college entrance examination + regional examination
-
-Second level version:
-
-* Minor subjects(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
-* Major subjects(science, arts and all subject)
-
-Third level version【to be finished】:
-
-* Don't use third-party initializers
-* Use third-party initializers
-
-Description of train data in models
-##################
-
-* Currently, the data used in w2v and d2v models are the subjects of senior high school.
-* test data:`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
-
-At present, the following models are provided. More models of different subjects and question types are being trained. Please look forward to it.
-    "d2v_all_256" (all subject), "d2v_sci_256" (Science), "d2v_eng_256" (English)，"d2v_lit_256" (Arts)
-
-
-Examples of Model Training
-------------
-
-Get the dataset
-####################
-
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-
-   prepare_dataset  <../../build/blitz/pretrain/prepare_dataset.ipynb>
-
-An example of d2v in gensim model
-####################
-
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-
-   d2v_bow_tfidf  <../../build/blitz/pretrain/gensim/d2v_bow_tfidf.ipynb>
-   d2v_general  <../../build/blitz/pretrain/gensim/d2v_general.ipynb>
-   d2v_stem_tf  <../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
-
-An example of w2v in gensim model
-####################
-
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-
-   w2v_stem_text  <../../build/blitz/pretrain/gensim/w2v_stem_text.ipynb>
-   w2v_stem_tf  <../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
-
-An example of seg_token
-####################
-
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-
-   d2v.ipynb  <../../build/blitz/pretrain/seg_token/d2v.ipynb>
-   d2v_d1  <../../build/blitz/pretrain/seg_token/d2v_d1.ipynb>
-   d2v_d2  <../../build/blitz/pretrain/seg_token/d2v_d2.ipynb>
\ No newline at end of file
diff --git a/docs/source/tutorial/en/pretrain/loading.rst b/docs/source/tutorial/en/pretrain/loading.rst
deleted file mode 100644
index 31fa3ea8..00000000
--- a/docs/source/tutorial/en/pretrain/loading.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-Load models
---------
-
-Transfer the obtained model to the I2V module to load the model.
- 
-Examples：
-
-::
-
-        >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
-        >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
diff --git a/docs/source/tutorial/en/pretrain/pub.rst b/docs/source/tutorial/en/pretrain/pub.rst
deleted file mode 100644
index 34407745..00000000
--- a/docs/source/tutorial/en/pretrain/pub.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-The overview of our public model
-------------
-
-
-Version Description
-##################
-
-First level version:
-
-* Public version 1 (luna_pub): college entrance examination
-* Public version 2 (luna_pub_large): college entrance examination + regional examination
-
-Second level version:
-
-* Minor subjects(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
-* Major subjects(science, arts and all subject)
-
-Third level version【to be finished】:
-
-* Don't use third-party initializers
-* Use third-party initializers
-
-Description of train data in models
-##################
-
-* Currently, the data used in w2v and d2v models are the subjects of senior high school.
-* test data:`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
-
-At present, the following models are provided. More models of different subjects and question types are being trained. Please look forward to it.
-    "d2v_all_256" (all subject), "d2v_sci_256" (Science), "d2v_eng_256" (English)，"d2v_lit_256" (Arts)
-
-Examples of model training
-------------
-
-Get the dataset
-####################
-
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-
-   prepare_dataset  <../../../build/blitz/pretrain/prepare_dataset.ipynb>
-
-An example of d2v in gensim model
-####################
-
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-
-   d2v_bow_tfidf  <../../../build/blitz/pretrain/gensim/d2v_bow_tfidf.ipynb>
-   d2v_general  <../../../build/blitz/pretrain/gensim/d2v_general.ipynb>
-   d2v_stem_tf  <../../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
-
-An example of w2v in gensim model
-####################
-
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-
-   w2v_stem_text  <../../../build/blitz/pretrain/gensim/w2v_stem_text.ipynb>
-   w2v_stem_tf  <../../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
-
-An example of seg_token
-####################
-
-.. toctree::
-   :maxdepth: 1
-   :titlesonly:
-
-   d2v.ipynb  <../../../build/blitz/pretrain/seg_token/d2v.ipynb>
-   d2v_d1  <../../../build/blitz/pretrain/seg_token/d2v_d1.ipynb>
-   d2v_d2  <../../../build/blitz/pretrain/seg_token/d2v_d2.ipynb>
diff --git a/docs/source/tutorial/en/pretrain/start.rst b/docs/source/tutorial/en/pretrain/start.rst
deleted file mode 100644
index 9c5bc241..00000000
--- a/docs/source/tutorial/en/pretrain/start.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-Train the model
-------------
-
-Call train_Vector function interface directly to make the training model easier. This section calls the relevant training models in the gensim library. At present, the training methods of "sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf" are provided. Parameter embedding_dim is also provided for users to determine vector dimension according to their needs.
-
-Basic Steps
-##################
-
-1.Determine the type of model and select the appropriate tokenizer (GensimWordTokenizer、 GensimSegTokenizer) to finish tokenization.
-
-2.Call train_vector function to get the required pre-trained model。
-
-Examples：
-
-::
-
-        >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
-        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-        >>> print(token_item.tokens[:10])
-        ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
-        
-        # 10 dimension with fasstext method
-        train_vector(sif_items, "../../../data/w2v/gensim_luna_stem_tf_", 10, method="d2v")
diff --git a/docs/source/tutorial/en/seg.rst b/docs/source/tutorial/en/seg.rst
deleted file mode 100644
index ad2696a2..00000000
--- a/docs/source/tutorial/en/seg.rst
+++ /dev/null
@@ -1,187 +0,0 @@
-Component Segmentation
-=========
-
-Educational resource is a kind of multimodal data, including data such as text, picture, formula and so on.
-At the same time, it may also contain different components semantically, such as question stems, options, etc. Therefore, we first need to identify and segment the different components of educational resources:
-
-* Semantic Component Segmentation
-* Structural Component Segmentation
-
-Main Processing Contents
---------------------
-
-1. Convert multiple-choice questions in the form of dict to qualified item by `Syntax parsing <parse.rst>`_;
-
-2. The input items are segmented and grouped according to the element type.
-
-Semantic Component Segmentation
-------------
-
-Because multiple-choice questions are given in the form of dict, it is necessary to convert them into text format while retaining their data relationship. This function can be realized by dict2str4sif function which can convert multiple-choice question items into character format and identify question stem and options。
-
-Import Modules
-+++++++++
-
-::
-
- from EduNLP.utils import dict2str4sif
-
-Basic Usage
-++++++++++++++++++
-
-::
-
- >>> item = {
- ...     "stem": r"若复数$z=1+2 i+i^{3}$，则$|z|=$",
- ...     "options": ['0', '1', r'$\sqrt{2}$', '2'],
- ... }
- >>> dict2str4sif(item) # doctest: +ELLIPSIS
- '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
-
-Optional additional parameters / interfaces
-++++++++++++++++++++++
-
-1.add_list_no_tag: if this parameter is true, it means that you need to count the labels in the options section.
-
-::
-
- >>> dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS
- '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
- 
- >>> dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS
- '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2$\\SIFTag{options_end}$'
-
-2.tag_mode: The location for the label can be selected using this parameter. 'delimiter' is to label both the beginning and the end,'head' is to label only the head, and 'tail' is to label only the tail.
-
-::
-
- >>> dict2str4sif(item, tag_mode="head") # doctest: +ELLIPSIS
- '$\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{options}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2'
- 
- >>> dict2str4sif(item, tag_mode="tail") # doctest: +ELLIPSIS
- '若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options}$'
-
-3.key_as_tag: If this parameter is false, this process will only adds $\SIFSep$ between the options without distinguishing the type of segmentation label.
-
-::
-
- >>> dict2str4sif(item, key_as_tag=False)
- '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
-
-Structural Component Segmentation
-------------
-
-This step is to segment sliced items. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
-
-
-There are two modes:
-
-* linear mode: it is used for text processing (word segmentation using jieba library);
-
-* ast mode: it is used to parse the formula.
-
-Basic Segmentation process:
-
-- Match components with regular expression matching
-
-- Process the components with special structures, such as converting the base64 encoded picture to numpy form
-
-- Classify the elements into each element group
-
-- Enter the corresponding parameters as required to get the filtered results
-
-Import Modules
-+++++++++
-
-::
-
- from EduNLP.SIF.segment import seg
- from EduNLP.SIF import sif4sci
-
-Basic Usage
-++++++++++++++++++
-
-::
-
- >>> test_item = r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$"
- >>> seg(test_item)
- >>> ['如图所示，则', '\\bigtriangleup ABC', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
-
-Optional additional parameters/interfaces
-++++++++++++++++++++++
-
-1.describe: count the number of elements of different types
-
-::
-
- >>> s.describe()
- {'t': 3, 'f': 1, 'g': 1, 'm': 1}
-
-2.filter: this interface can screen out one or more types of elements.
-
-Using this interface, you can pass in a "keep" parameter or a special character directly to choose what type of elements to retain.
-
-Element type represented by symbol:
-
--   "t": text
--   "f": formula
--   "g": figure
--   "m": question mark
--   "a": tag
--   "s": sep tag
-
-::
-
- >>> with s.filter("f"):
- ...     s
- ['如图所示，则', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
- >>> with s.filter(keep="t"):
- ...     s
- ['如图所示，则', '的面积是', '。']
-
-3.symbol: this interface can convert some types of data into special symbols
-
-Element type represented by symbol:
-
--   "t": text
--   "f": formula
--   "g": figure
--   "m": question mark
-
-::
-
- >>> seg(test_item, symbol="fgm")
- ['如图所示，则', '[FORMULA]', '的面积是', '[MARK]', '。', '[FIGURE]']
- >>> seg(test_item, symbol="tfgm")
- ['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']
-
-In addition，sif4sci function is also provided, which can easily convert items into the result processed by Structural Component Segmentation
-
-::
-
- >>> segments = sif4sci(item["stem"], figures=figures, tokenization=False)
- >>> segments
- ['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\SIFChoice', \FigureID{1}]
-
-- When calling this function, you can selectively output a certain type of data according to your needs
-
-::
-
- >>> segments.formula_segments
- ['ABC',
- 'BC',
- 'AB',
- 'AC',
- '\\bigtriangleup ABC',
- 'I',
- 'II',
- 'III',
- 'I,II,III',
- 'p_1,p_2,p_3']
-
-- Similar to seg function, sif4sci function also provides depth options to help with your research ----- By modifying the ``symbol`` parameter, different components can be transformed into specific markers.
-
-::
-
- >>> sif4sci(item["stem"], figures=figures, tokenization=False, symbol="tfgm")
- ['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']
diff --git a/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst b/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
deleted file mode 100644
index 3901f4cb..00000000
--- a/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
+++ /dev/null
@@ -1,47 +0,0 @@
-Semantic Component Segmentation
-------------
-
-Because multiple-choice questions are given in the form of dict, it is necessary to convert them into text format while retaining their data relationship. This function can be realized by dict2str4sif function which can convert multiple-choice question items into character format and identify question stem and options。
-
-
-Basic Usage
-++++++++++++++++++
-
-::
-
- >>> item = {
- ...     "stem": r"若复数$z=1+2 i+i^{3}$，则$|z|=$",
- ...     "options": ['0', '1', r'$\sqrt{2}$', '2'],
- ... }
- >>> dict2str4sif(item) # doctest: +ELLIPSIS
- '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
-
-Optional additional parameters / interfaces
-++++++++++++++++++++++
-
-1.add_list_no_tag: if this parameter is true, it means that you need to count the labels in the options section.
-
-::
-
- >>> dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS
- '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
- 
- >>> dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS
- '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2$\\SIFTag{options_end}$'
-
-2.tag_mode: The location for the label can be selected using this parameter. 'delimiter' is to label both the beginning and the end,'head' is to label only the head, and 'tail' is to label only the tail.
-
-::
-
- >>> dict2str4sif(item, tag_mode="head") # doctest: +ELLIPSIS
- '$\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{options}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2'
- 
- >>> dict2str4sif(item, tag_mode="tail") # doctest: +ELLIPSIS
- '若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options}$'
-
-3.key_as_tag: If this parameter is false, this process will only adds $\SIFSep$ between the options without distinguishing the type of segmentation label.
-
-::
-
- >>> dict2str4sif(item, key_as_tag=False)
- '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
\ No newline at end of file
diff --git a/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst b/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
deleted file mode 100644
index 8661c3d6..00000000
--- a/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
+++ /dev/null
@@ -1,67 +0,0 @@
-Structural Component Segmentation
-------------
-
-This step is to segment sliced items. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
-
-
-There are two modes:
-
-* linear mode: it is used for text processing (word segmentation using jieba library);
-
-* ast mode: it is used to parse the formula.
-
-Basic Usage
-++++++++++++++++++
-
-::
-
- >>> test_item = r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$"
- >>> seg(test_item)
- >>> ['如图所示，则', '\\bigtriangleup ABC', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
-
-Optional additional parameters/interfaces
-++++++++++++++++++++++
-
-1.describe: count the number of elements of different types
-
-::
-
- >>> s.describe()
- {'t': 3, 'f': 1, 'g': 1, 'm': 1}
-
-2.filter: this interface can screen out one or more types of elements.
-
-Using this interface, you can pass in a "keep" parameter or a special character directly to choose what type of elements to retain.
-
-Element type represented by symbol:
-   "t": text
-   "f": formula
-   "g": figure
-   "m": question mark
-   "a": tag
-   "s": sep tag
-
-::
-
- >>> with s.filter("f"):
- ...     s
- ['如图所示，则', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
- >>> with s.filter(keep="t"):
- ...     s
- ['如图所示，则', '的面积是', '。']
-
-3.symbol: this interface can convert some types of data into special symbols
-
-Element type represented by symbol:
-
--   "t": text
--   "f": formula
--   "g": figure
--   "m": question mark
-
-::
-
- >>> seg(test_item, symbol="fgm")
- ['如图所示，则', '[FORMULA]', '的面积是', '[MARK]', '。', '[FIGURE]']
- >>> seg(test_item, symbol="tfgm")
- ['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']
diff --git a/docs/source/tutorial/en/sif.rst b/docs/source/tutorial/en/sif.rst
index 877cb503..0cbe7cf3 100644
--- a/docs/source/tutorial/en/sif.rst
+++ b/docs/source/tutorial/en/sif.rst
@@ -1,145 +1,2 @@
 Standard Item Format
-===============
-
-version: 0.2
-
-For the convenience of follow-up research and use, we need a unified test question grammar standard.
-
-Grammar Rules
------------
-
-1. Only Chinese characters, Chinese and English punctuation and line breaks are allowed in the question text.
-
-2. Represent underlines of blanks and brackets of choices with ``\$\SIFBlank\$`` and ``\$\SIFChoice\$`` respectively.
-
-3. We use ``$\FigureID{ uuid }$`` or Base64 to represent pictures. Especially, ``$\FormFigureID{ uuid }$`` is used to represent formulas pictures.
-
-4. Text format description: we represent text in different styles with ``$\textf{item,CHAR_EN}$``. Currently, we have defined some styles: b-bold, i-italic, u-underline, w-wave, d-dotted, t-title. CHAR_EN Labels can be mixed and sorted alphabetically. An example: $\textf{EduNLP, b}$ looks **EduNLP**
-
-5. Other mathematical symbols like English letters, Roman characters and numbers need to be expressed in latex format, that is, embedded in `$$`.
-
-6. For the entry standard of molecular formula, please refer to `INCHI <https://zh.wikipedia.org/wiki/%E5%9B%BD%E9%99%85%E5%8C%96%E5%90%88%E7%89%A9%E6%A0%87%E8%AF%86>`_ for the time being.
-
-7. Currently, there are no requirements for latex internal syntax.
-
-::
-
-  1. Item -> CHARACTER|EN_PUN_LIST|CH_PUN_LIST|FORMULA|QUES_MARK
-  2. EN_PUN_LIST -> [',', '.', '?', '!', ':', ';', '\'', '\"', '(', ')', ' ','_','/','|','\\','<','>','[',']','-']
-  3. CH_PUN_LIST -> ['，', '。', '！', '？', '：','；', '‘', '’', '“', '”', '（', '）', ' ', '、','《','》','—','．']
-  4. FORMULA -> $latex formula$ | $\FormFigureID{UUID}$ | $\FormFigureBase64{BASE64}$
-  5. FIGURE -> $\FigureID{UUID}$ | $\FigureBase64{BASE64}$
-  6. UUID -> [a-zA-Z\-0-9]+
-  7. CHARACTER -> CHAR_EN | CHAR_CH
-  8. CHAR_EN -> [a-zA-Z]+
-  9. CHAR_CH -> [\u4e00-\u9fa5]+
-  10. DIGITAL -> [0-9]+
-  11. QUES_MARK -> $\SIFBlank$ | $\SIFChoice$
-
-
-Tips
-+++++++++++++++
-
-1. Reserved characters and escape characters.
-
-2. Numbers.
-
-3. Choices and blanks.
-
-4. A single number or letter is also required to be between `$$` (automatic verification could already realize it).
-
-5. Try to make sure Chinese is not included in the latex formula such as ``\text{CHAR_CH}``.
-
-6. When importing data using MySQL database, an ``\`` is automatically ignored which needs to be further processed as``\\``.
-
-Examples
------------------
-
-Standard Format:
-
-::
-
- 1. 若$x,y$满足约束条件$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，则$z=x+7 y$的最大值$\\SIFUnderline$'
- 
- 2. 已知函数$f(x)=|3 x+1|-2|x|$画出$y=f(x)$的图像求不等式$f(x)>f(x+1)$的解集$\\PictureID{3bf2ddf4-8af1-11eb-b750-b46bfc50aa29}$$\\PictureID{59b8bd14-8af1-11eb-93a5-b46bfc50aa29}$$\\PictureID{63118b3a-8b75-11eb-a5c0-b46bfc50aa29}$$\\PictureID{6a006179-8b76-11eb-b386-b46bfc50aa29}$$\\PictureID{088f15eb-8b7c-11eb-a86f-b46bfc50aa29}$
-
-Non-standard Format:
-
-1. Letters, numbers and mathematical symbols are mixed:
-
-    For example:
-    
-    ``完成下面的2x2列联表，``
-    
-    ``（单位：m3）``
-    
-    ``则输出的n=``
-    
-2. Some special mathematical symbols are not represented by the latex formula:
-
-    For example:
-    
-    ``命题中真命题的序号是 ①``
-    
-    ``AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．若D为AC的中点``
-    
-3. There are unicode encoded characters in the text:
-
-    For example:
-    ``则$a$的取值范围是（\u3000\u3000）``
-
-Functions for judging whether text is in SIF format and converting to SIF format
---------------------------------------------
-
-Call the Library
-++++++++
-::
-
-    from EduNLP.SIF import is_sif, to_sif
-
-is_sif
-+++++++++++
-
-::
-
-    >>> text1 = '若$x,y$满足约束条件' 
-    >>> text2 = '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' 
-    >>> text3 = '则$z=x+7 y$的最大值$\\SIFUnderline$'
-    >>> text4 = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
-    >>> is_sif(text1)
-    True
-    >>> is_sif(text2)
-    True
-    >>> is_sif(text3)
-    True
-    >>> is_sif(text4)
-    False
-
-to_sif
-+++++++++++
-
-::
-
-    >>> text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
-    >>> to_sif(text)
-    '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'
-
-
-Change Log
-----------------
-
-2021-05-18
-
-Changed
-
-1. Originally, we use ``\$\SIFUnderline\$`` and ``\$\SIFBracket\$`` to represent underlines of blanks and brackets of choices. Now we represent them with ``\$\SIFBlank\$`` and ``\$\SIFChoice\$``.
-
-2. Originally, we used ``$\PictureID{ uuid }$`` to represent pictures, but now we use ``$\FigureID{ uuid }$`` instead. Especially, ``$\FormFigureID{ uuid }$`` is used to represent formulas pictures.
-
-2021-06-28 
-  
-Added:
-
-1. There should not be line breaks between the notation ``$$``.
-
-2. Add text format description.
+====================
diff --git a/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst b/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
deleted file mode 100644
index eb624e94..00000000
--- a/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-GensimSegTokenizer
-=====================
-
-By default, the pictures, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text, formulas and labels. Also, the tokenizer uses linear analysis method for text and abstract analysis method of syntax tree for formulas.
-
-Compared to GensimWordTokenizer, the main differences are:
-
-* It provides the depth option for segmentation position, such as \SIFSep and \SIFTag.
-* By default, labels are inserted in the header of item components (such as text and formula).
\ No newline at end of file
diff --git a/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst b/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
deleted file mode 100644
index 98d4b10a..00000000
--- a/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
+++ /dev/null
@@ -1,23 +0,0 @@
-GensimWordTokenizer
-=====================
-
-By default, the pictures, blanks in the question text and other parts of the incoming item are converted into special characters for data security and the tokenization of text, formulas, labels and separators. Also, the tokenizer uses linear analysis method for text and abstract syntax tree method for formulas respectively. You can choose each of them by ``general`` parameter:
-
--true, it means that the incoming item conforms to SIF and the linear analysis method should be used.
--false, it means that the incoming item doesn't conform to SIF and the abstract syntax tree method should be used.
-
-Examples
-----------
-        
-::
-
-        >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
-        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-        >>> print(token_item.tokens[:10])
-        ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
-        >>> tokenizer = GensimWordTokenizer(symbol="fgmas", general=False)
-        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-        >>> print(token_item.tokens[:10])
-        ['公式', '[FORMULA]', '如图', '[FIGURE]', '[FORMULA]', '约束条件', '公式', '[FORMULA]', '[SEP]', '[FORMULA]']
diff --git a/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst b/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
deleted file mode 100644
index 8c36e67c..00000000
--- a/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-PureTextTokenizer
-================
-
-By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security. At the same time, special formulas such as $\\FormFigureID{...}$ and $\\FormFigureBase64{...}$ are screened out to facilitate the tokenization of text and plain text formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
-
-Examples
-----------
-
-::
-
-    >>> tokenizer = PureTextTokenizer()
-    >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
-    >>> tokens = tokenizer(items)
-    >>> next(tokens)[:10]
-    ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
-    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
-    >>> tokens = tokenizer(items)
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-    '\\quad', 'A', '\\cap', 'B', '=']
-    >>> items = [{
-    ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
-    ... "options": ["1", "2"]
-    ... }]
-    >>> tokens = tokenizer(items, key=lambda x: x["stem"])
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-    '\\quad', 'A', '\\cap', 'B', '=']
diff --git a/docs/source/tutorial/en/tokenization/TextTokenizer.rst b/docs/source/tutorial/en/tokenization/TextTokenizer.rst
deleted file mode 100644
index 08991be6..00000000
--- a/docs/source/tutorial/en/tokenization/TextTokenizer.rst
+++ /dev/null
@@ -1,27 +0,0 @@
-TextTokenizer
-================
-
-By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text and formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
-
-
-Examples
-----------
-
-::
-
-    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
-    >>> tokenizer = TextTokenizer()
-    >>> tokens = tokenizer(items)
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-    '\\quad', 'A', '\\cap', 'B', '=']
-    >>> items = [{
-    ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
-    ... "options": ["1", "2"]
-    ... }]
-    >>> tokens = tokenizer(items, key=lambda x: x["stem"])
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-    '\\quad', 'A', '\\cap', 'B', '=']
diff --git a/docs/source/tutorial/en/tokenize.rst b/docs/source/tutorial/en/tokenize.rst
deleted file mode 100644
index 3411b74b..00000000
--- a/docs/source/tutorial/en/tokenize.rst
+++ /dev/null
@@ -1,172 +0,0 @@
-Tokenization
-=======
-
-Tokenization, known as word segmentation and sentence segmentation, is a basic but very important step in the field of NLP.
-In EduNLP, we divided Tokenization into different levels according to different granularity. To avoid ambiguity, we define as follows:
-
-* Word/char level: word segmentation
-
-* Sentence level: sentence segmentation
-
-* Resource level: tokenization
-
-This module provides tokenization function of question text, converting questions into token sequences to facilitate the vectorization of questions. After that, each element in the sliced item needs word segmentation. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
-
-There are two modes: one is linear mode, which is used for text processing (word segmentation using jieba library). The other one is ast mode, which is used to parse the formula.
-
-Word Segmentation
--------
-
-Text-tokenization: A sentence (without formulas) consists of several "words" in order. The process of dividing a sentence into several words is called "Text-tokenization". According to the granularity of "words", it can be subdivided into "Word-tokenization" and "Char-tokenization".
-
-::
-
-   - Word-tokenization: each phrase is a token.
-   
-   - Char-tokenization: each character is a token.
-    
-
-Text-tokenization is divided into two main steps:
-
-1. Text-tokenization:
-
-   - Word-tokenization: use the word segmentation tool to segment and extract words from the question text. Our project supports `jieba`.
-
-   - Char-tokenization: process text by character.
-
-2. Filter: filter the specified stopwords.
-
-   The default stopwords used in this project:`[stopwords] <https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt>`_
-   You can also use your own stopwords. The following example demonstrates how to use.
-
-Examples:
-
-::
-
-   from EduNLP.SIF.tokenization.text import tokenize 
-   >>> text = "三角函数是基本初等函数之一"
-   >>> tokenize(text, granularity="word")
-   ['三角函数', '初等', '函数']
-   
-   >>> tokenize(text, granularity="char")
-   ['三', '角', '函', '数', '基', '初', '函', '数']
-    
-Sentence Segmentation
--------
-
-During the process of sentence segmentation, a long document is divided into several sentences. Each sentence is a "token" (to be realized).
-
-Tokenization
--------
-Tokenization is comprehensive analysis. In this process, sentences with formulas are segmented into several markers. Each marker is a "token".
-
-The implementation of this function is tokenize function. The required results can be obtained by passing in items after Structural Component Segmentation.
-
-::
-
-   from EduNLP.Tokenizer import get_tokenizer
-   >>> items = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
-   >>> tokenize(SegmentList(items))
-   ['如图所示', '三角形', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]
-   >>> tokenize(SegmentList(items),formula_params={"method": "ast"})
-   ['如图所示', '三角形', <Formula: ABC>, '面积', '\\\\SIFBlank', \\FigureID{1}]
-
-
-
-You can view ``./EduNLP/Tokenizer/tokenizer.py`` and ``./EduNLP/Pretrain/gensim_vec.py`` for more tokenizers. We provide some encapsulated tokenizers for users to call them conveniently. Following is a complete list of tokenizers:
-
-- TextTokenizer
-
-- PureTextTokenizer
-
-- GensimSegTokenizer
-
-- GensimWordTokenizer
-
-
-TextTokenizer
-+++++++++++++++++++++
-
-By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text and formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
-
-::
-
-   >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
-   >>> tokenizer = TextTokenizer()
-   >>> tokens = tokenizer(items)
-   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-   '\\quad', 'A', '\\cap', 'B', '=']
-   >>> items = [{
-   ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
-   ... "options": ["1", "2"]
-   ... }]
-   >>> tokens = tokenizer(items, key=lambda x: x["stem"])
-   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-   '\\quad', 'A', '\\cap', 'B', '=']
-
-PureTextTokenizer
-+++++++++++++++++++++
-
-By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security. At the same time, special formulas such as $\\FormFigureID{...}$ and $\\FormFigureBase64{...}$ are screened out to facilitate the tokenization of text and plain text formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
-
-::
-
-   >>> tokenizer = PureTextTokenizer()
-   >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
-   >>> tokens = tokenizer(items)
-   >>> next(tokens)[:10]
-   ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
-   >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
-   >>> tokens = tokenizer(items)
-   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-   '\\quad', 'A', '\\cap', 'B', '=']
-   >>> items = [{
-   ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
-   ... "options": ["1", "2"]
-   ... }]
-   >>> tokens = tokenizer(items, key=lambda x: x["stem"])
-   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-   '\\quad', 'A', '\\cap', 'B', '=']
-
-GensimWordTokenizer
-+++++++++++++++++++++++
-
-By default, the pictures, blanks in the question text and other parts of the incoming item are converted into special characters for data security and the tokenization of text, formulas, labels and separators. Also, the tokenizer uses linear analysis method for text and abstract syntax tree method for formulas respectively. You can choose each of them by ``general`` parameter:
-
--true, it means that the incoming item conforms to SIF and the linear analysis method should be used.
--false, it means that the incoming item doesn't conform to SIF and the abstract syntax tree method should be used.
-
-GensimSegTokenizer
-++++++++++++++++++++
-
-By default, the pictures, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text, formulas and labels. Also, the tokenizer uses linear analysis method for text and abstract analysis method of syntax tree for formulas.
-
-Compared to GensimWordTokenizer, the main differences are:
-
-* It provides the depth option for segmentation position, such as \SIFSep and \SIFTag.
-* By default, labels are inserted in the header of item components (such as text and formulas).
-
-Examples
-----------
-        
-::
-
-   >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
-   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-   >>> print(token_item.tokens[:10])
-   ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
-   >>> tokenizer = GensimWordTokenizer(symbol="fgmas", general=False)
-   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
-   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
-   >>> print(token_item.tokens[:10])
-   ['公式', '[FORMULA]', '如图', '[FIGURE]', '[FORMULA]', '约束条件', '公式', '[FORMULA]', '[SEP]', '[FORMULA]']
diff --git a/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst b/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst
deleted file mode 100644
index 1a8d4950..00000000
--- a/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst	
+++ /dev/null
@@ -1,3 +0,0 @@
-Sentence Segmentation
--------
-During the process of sentence segmentation, a long document is divided into several sentences. Each sentence is a "token" (to be realized).
diff --git a/docs/source/tutorial/en/tokenize/Tokenization.rst b/docs/source/tutorial/en/tokenize/Tokenization.rst
deleted file mode 100644
index fad25912..00000000
--- a/docs/source/tutorial/en/tokenize/Tokenization.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-Tokenization
--------
-Tokenization is comprehensive analysis. In this process, sentences with formulas are segmented into several markers. Each marker is a "token".
-We provide some encapsulated tokenizers for users to call them conveniently. The following is a complete list of tokenizers.
-
-Examples
-    
-::
-
-    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
-    >>> tokenizer = TextTokenizer()
-    >>> tokens = tokenizer(items)
-    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
-    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
-    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
-    '\\quad', 'A', '\\cap', 'B', '=']
-
-
-
-You can view ``./EduNLP/Tokenizer/tokenizer.py`` and ``./EduNLP/Pretrain/gensim_vec.py`` for more tokenizers. Following is a complete list of tokenizers:
-
-.. toctree::
-  :maxdepth: 1
-  :titlesonly:
-
-  ../tokenization/TextTokenizer
-  ../tokenization/PureTextTokenizer
-  ../tokenization/GensimSegTokenizer
-  ../tokenization/GensimWordTokenizer
diff --git a/docs/source/tutorial/en/tokenize/WordSegmentation.rst b/docs/source/tutorial/en/tokenize/WordSegmentation.rst
deleted file mode 100644
index a85f4dae..00000000
--- a/docs/source/tutorial/en/tokenize/WordSegmentation.rst
+++ /dev/null
@@ -1,36 +0,0 @@
-Word segmentation
--------
-
-Text-tokenization: A sentence (without formulas) consists of several "words" in order. The process of dividing a sentence into several words is called "Text-tokenization". According to the granularity of "words", it can be subdivided into "Word-tokenization" and "Char-tokenization".
-
-::
-
-   - Word-tokenization: each phrase is a token.
-
-   - Char-tokenization: each character is a token.
-
-
-Text-tokenization is divided into two main steps:
-
-1. Text-tokenization:
-
-   - Word-tokenization: use the word segmentation tool to segment and extract words from the question text. Our project supports `jieba`.
-
-   - Char-tokenization: process text by character.
-
-2. Filter: filter the specified stopwords.
-
-   The default stopwords used in this project:`[stopwords] <https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt>`_
-   You can also use your own stopwords. The following example demonstrates how to use.
-
-Examples:
-
-::
-    
-    >>> text = "三角函数是基本初等函数之一"
-    >>> tokenize(text, granularity="word")
-    ['三角函数', '初等', '函数']
-    
-    >>> tokenize(text, granularity="char")
-    ['三', '角', '函', '数', '基', '初', '函', '数']
-    
diff --git a/docs/source/tutorial/en/vectorization.rst b/docs/source/tutorial/en/vectorization.rst
deleted file mode 100644
index 5b744eeb..00000000
--- a/docs/source/tutorial/en/vectorization.rst
+++ /dev/null
@@ -1,157 +0,0 @@
-Vectorization
-=========
-
-This section provides a simple interface to convert the incoming items into vectors directly. Currently, the option of whether to use the pre training model is provided. You can choose according to your needs. If you don't want to use the pre-trained model, you can call D2V directly, or call get_pretrained_i2v function if you want to use the pre-trained model.
-
-- Don't use the pre-trained model
-
-- Use the pre-trained model
-
-Overview Flow
----------------------------
-
-1.Perform `syntax parsing <parse.rst>`_ on incoming items to get items in SIF format；
-
-2.Perform `component segmentation <seg.rst>`_ on sif_items;
-
-3.Perform `tokenization <tokenize.rst>`_ on segmented items;
-
-4.Use the existing or pre-trained model we provided to convert the tokenized items into vectors.
-
-
-Don't use the pre-trained model: call existing models directly
-------------------------------------
-
-You can use any pre-trained model provided by yourself (just give the storage path of the model) to convert the given question text into vectors.
-
-* Advantages: it is flexible to use your own model and its parameters can be adjusted freely.
-
-Import modules
-++++++++++
-
-::
-
-   from EduNLP.I2V import D2V,W2V,get_pretrained_i2v
-   from EduNLP.Vector import T2V,get_pretrained_t2v
-
-Models provided
-++++++++++++++++++++
-
-- W2V
-
-- D2V
-
-- T2V
-
-W2V
-<<<<<<<<<
-
-This model directly uses the relevant model methods in the gensim library to convert words into vectors. Currently, there are four methods:
-
- - FastText
-
- - Word2Vec
-
- - KeyedVectors
-
-::
-
-   >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") # doctest: +ELLIPSIS
-   >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
-   >>> item_vector # doctest: +ELLIPSIS
-   array([[...]], dtype=float32)
-
-D2V
-<<<<<<<<<<<<
-
-This model is a comprehensive processing method which can convert items into vectors. Currently, the following methods are provided:
-
-- d2v: call doc2vec module in gensim library to convert items into vectors.
-
-- BowLoader: call corpora module in gensim library to convert docs into bows.
-
-- TfidfLoader: call TfidfModel module in gensim library to convert docs into bows.
-
-::
-
-   >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$"}
-   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
-   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
-   >>> i2v(item)
-   ([array([ 4.76559885e-02, -1.60574958e-01,  1.94614579e-03,  2.40295693e-01,
-   2.24517003e-01, -3.24351490e-02,  4.35789041e-02, -1.65670961e-02,...
-
-T2V
-<<<<<<<<<<
-
-You can use any pre-trained model provided by yourself to represent the segmentation sequences of a group of questions as vectors (just give the storage path of the model).
-
-- Advantages: the model and its parameters can be adjusted independently and has strong flexibility.
-
-Input
-^^^^^^^^^^
-
-Types: list
-Contents: the combination of each question segmentation sequences in one question group.
->You can transfer question text (`str` type) to tokens using ``GensimWordTokenizer`` model
-
-::
-
-   >>> token_items=['公式','[FORMULA]','公式','[FORMULA]','如图','[FIGURE]','x',',','y','约束条件','[SEP]','z','=','x','+','7','y','最大值','[MARK]']
-   >>> path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
-   >>> t2v = T2V('d2v',filepath=path)
-   >>> t2v(token_items)
-   [array([ 0.0256574 ,  0.06061139, -0.00121044, -0.0167674 , -0.0111706 ,
-   0.05325712, -0.02097339, -0.01613594,  0.02904145,  0.0185046 ,...
-
-Specific process of processing
-++++++++++++++++++++
-
-1.Call get_tokenizer function to get the result after word segmentation;
-
-2.Select the model provided for vectorization depending on the model used.
-
-
-Use the pre-training model: call get_pretrained_i2v directly
----------------------------------------------
-
-Use the pre-training model provided by EduNLP to convert the given question text into vectors.
-
-* Advantages: Simple and convenient.
-
-* Disadvantages: Only the model given in the project can be used, which has great limitations.
-
-* Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
-
-Selection and Use of Models
-##################
-
-Select the pre-training model according to the subject:
-
-+--------------------+------------------------+
-|   Pre-training model name  | Subject of model training data |
-+====================+========================+
-|    d2v_all_256     |        all subject          |
-+--------------------+------------------------+
-|    d2v_sci_256     |         Science           |
-+--------------------+------------------------+
-|    d2v_lit_256     |         Arts           |
-+--------------------+------------------------+
-|    d2v_eng_256     |         English           |
-+--------------------+------------------------+
-
-
-The concrete process of processing
-##################
-
-1.Download the corresponding preprocessing model
-
-2.Transfer the obtained model to D2V and process it with D2V
-  Convert the obtained model into D2V and process it through D2V
-
-Examples:
-
-::
-
-  >>> i2v = get_pretrained_i2v("d2v_sci_256")
-  >>> i2v(item)
diff --git a/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst b/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
deleted file mode 100644
index 41dcab64..00000000
--- a/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
+++ /dev/null
@@ -1,42 +0,0 @@
-Use the pre-training model: call get_pretrained_i2v directly
----------------------------------------------
-
-Use the pre-training model provided by EduNLP to convert the given question text into vectors.
-
-* Advantages: Simple and convenient.
-
-* Disadvantages: Only the model given in the project can be used, which has great limitations.
-
-* Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
-
-Selection and use of models
-##################
-
-Select the pre-training model according to the subject:
-
-+--------------------+------------------------+
-|   Pre-training model name  | Subject of model training data |
-+====================+========================+
-|    d2v_all_256     |        all subject          |
-+--------------------+------------------------+
-|    d2v_sci_256     |         Science           |
-+--------------------+------------------------+
-|    d2v_lit_256     |         Arts           |
-+--------------------+------------------------+
-|    d2v_eng_256     |         English           |
-+--------------------+------------------------+
-
-The concrete process of processing
-##################
-
-1.Download the corresponding preprocessing model
-
-2.Transfer the obtained model to D2V and process it with D2V
-  Convert the obtained model into D2V and process it through D2V
-
-Examples：
-
-::
-
-  >>> i2v = get_pretrained_i2v("d2v_sci_256")
-  >>> i2v(item)
diff --git a/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst b/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
deleted file mode 100644
index 2989f8ba..00000000
--- a/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-Don't use the pre-trained model: call existing models directly
-------------------------------------
-
-You can use any pre-trained model provided by yourself (just give the storage path of the model) to convert the given question text into vectors.
-
-* Advantages: it is flexible to use your own model and its parameters can be adjusted freely.
-
-Specific process of processing
-++++++++++++++++++++
-
-1.Call get_tokenizer function to get the result after word segmentation;
-
-2.Select the model provided for vectorization depending on the model used.
-
-Examples：
-
-::
-
-  >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
-  >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
-  >>> i2v(item)

From 66f91c1d322cef9d6580d56f3f2f7f005a48c382 Mon Sep 17 00:00:00 2001
From: fannazya <bfsudcs_fanzy@163.com>
Date: Sat, 23 Oct 2021 20:27:06 +0800
Subject: [PATCH 146/149] Revert "Revert "Add English version tutorial""

This reverts commit c84f5ac96d29b4f2c444336634785f27163d4d6f.
---
 docs/source/tutorial/en/index.rst             |  52 +++-
 docs/source/tutorial/en/parse.rst             | 290 ++++++++++++++++++
 .../parse/FormulaSyntaxStructureParsing.rst   | 168 ++++++++++
 .../en/parse/TextSyntaxStructureParsing.rst   |  72 +++++
 docs/source/tutorial/en/pretrain.rst          | 130 ++++++++
 docs/source/tutorial/en/pretrain/loading.rst  |  11 +
 docs/source/tutorial/en/pretrain/pub.rst      |  74 +++++
 docs/source/tutorial/en/pretrain/start.rst    |  24 ++
 docs/source/tutorial/en/seg.rst               | 187 +++++++++++
 .../en/seg/SemanticComponentSegmentation.rst  |  47 +++
 .../seg/StructuralComponentSegmentation.rst   |  67 ++++
 docs/source/tutorial/en/sif.rst               | 145 ++++++++-
 .../en/tokenization/GensimSegTokenizer.rst    |   9 +
 .../en/tokenization/GensimWordTokenizer.rst   |  23 ++
 .../en/tokenization/PureTextTokenizer.rst     |  31 ++
 .../en/tokenization/TextTokenizer.rst         |  27 ++
 docs/source/tutorial/en/tokenize.rst          | 172 +++++++++++
 .../en/tokenize/Sentence Segmentation.rst     |   3 +
 .../tutorial/en/tokenize/Tokenization.rst     |  29 ++
 .../tutorial/en/tokenize/WordSegmentation.rst |  36 +++
 docs/source/tutorial/en/vectorization.rst     | 157 ++++++++++
 .../en/vectorization/WithPre-trainedModel.rst |  42 +++
 .../vectorization/WithoutPre-trainedModel.rst |  21 ++
 23 files changed, 1815 insertions(+), 2 deletions(-)
 create mode 100644 docs/source/tutorial/en/parse.rst
 create mode 100644 docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
 create mode 100644 docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
 create mode 100644 docs/source/tutorial/en/pretrain.rst
 create mode 100644 docs/source/tutorial/en/pretrain/loading.rst
 create mode 100644 docs/source/tutorial/en/pretrain/pub.rst
 create mode 100644 docs/source/tutorial/en/pretrain/start.rst
 create mode 100644 docs/source/tutorial/en/seg.rst
 create mode 100644 docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
 create mode 100644 docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
 create mode 100644 docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
 create mode 100644 docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
 create mode 100644 docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
 create mode 100644 docs/source/tutorial/en/tokenization/TextTokenizer.rst
 create mode 100644 docs/source/tutorial/en/tokenize.rst
 create mode 100644 docs/source/tutorial/en/tokenize/Sentence Segmentation.rst
 create mode 100644 docs/source/tutorial/en/tokenize/Tokenization.rst
 create mode 100644 docs/source/tutorial/en/tokenize/WordSegmentation.rst
 create mode 100644 docs/source/tutorial/en/vectorization.rst
 create mode 100644 docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
 create mode 100644 docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst

diff --git a/docs/source/tutorial/en/index.rst b/docs/source/tutorial/en/index.rst
index 108a9487..4c8cc040 100644
--- a/docs/source/tutorial/en/index.rst
+++ b/docs/source/tutorial/en/index.rst
@@ -1,2 +1,52 @@
 Get Started
-===========
+=====
+
+*  `Standard Item Format <sif.rst>`_
+
+*  `Syntax Parsing <parse.rst>`_
+
+*  `Component Segmentation <seg.rst>`_
+
+*  `Tokenization <tokenize.rst>`_
+
+*  `Pre-training <pretrain.rst>`_
+
+*  `Vectorization <vectorization.rst>`_
+
+Main process
+----------
+
+.. figure:: ../../_static/new_flow.png
+
+* `Syntax Parsing <parse.rst>`_ : Its function is to convert the incoming item into SIF format, which means letters and numbers should be between ``$...$`` and the brackets and underlines of the choice questions should be converted to special symbols we defined in SIF)
+
+* `Component Segmentation <seg.rst>`_ : Its function is to segment items in SIF format according to the types of items, so as to serve the later tokenization module.(that is, elements in different types can be tokenized using their corresponding methods)。
+
+* `Tokenization <tokenize.rst>`_: Its function is to tokenize segmented items, so as to serve the later tokenization module.
+  Generally, the tokenization method in the text form can be used directly. For formulas, the ast method can also be used for parsing(call the formula module)；
+
+* `Vectorization <vectorization.rst>`_: This part mainly calls I2V class and its subclasses. Its function is to vectorize the list of tokenized items, so as to get the corresponding static vectors.
+  For vectorization module, You can call your own trained model or directly call the provided pre-training model(call get_ pretrained_ I2V module).
+
+* **Downstream Model**：Process the obtained vectors to get the desired results。
+
+Examples
+--------
+
+To help you quickly understand the functions of this project, this section only shows the usages of common function interface. Intermediate function modules (such as parse, formula, segment, etc.) and more subdivided interface methods are not shown. For further study, please refer to relevant documents.
+
+.. nbgallery::
+    :caption: This is a thumbnail gallery:
+    :name: tokenize_gallery
+    :glob:
+    
+    Tokenization  <../../build/blitz/tokenizer/tokenizer.ipynb>
+
+
+
+.. nbgallery::
+    :caption: This is a thumbnail gallery:
+    :name: vectorization_gallery
+    :glob:
+    
+    Vectorization  <../../build/blitz/vectorization/total_vector.ipynb>
diff --git a/docs/source/tutorial/en/parse.rst b/docs/source/tutorial/en/parse.rst
new file mode 100644
index 00000000..69608c5e
--- /dev/null
+++ b/docs/source/tutorial/en/parse.rst
@@ -0,0 +1,290 @@
+Syntax Parsing
+=========
+
+In educational resources, texts and formulas have internal implicit or explicit syntax structures. It is of great benefit for further processing to extract these structures.
+* Text syntax structure parsing
+
+* Formula syntax structure parsing
+
+The purpose is as follows:
+
+
+1. Represent underlines of blanks and brackets of choices with special identifiers. And the alphabets and formulas should be wrapped with $$, so that items of different types can be cut accurately through the symbol $.
+2. Determine whether the current item is legal and report the error type.
+
+Specific processing content
+--------------------
+
+1.Its function is to match alphabets and numbers other than formulas. Only the alphabets and numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+
+2.Match brackets like "( )" (both English format and Chinese format), that is, brackets with no content or spaces, which should be replaced with ``$\\SIFChoice$``
+
+3.Match continuous underscores or underscores with spaces to replace them with ``$\\SIFBlank$``.
+
+4.Match latex formulas，check the completeness and analyzability of latex formulas, and report an error for illegal formula.
+
+Formula syntax structure parsing
+--------------------
+
+This section is mainly realized by EduNLP.Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
+
+Introduction of Main Introduction
++++++++++++++++
+
+1.Formula: determine whether the single formula passed in is in str form. If so, use the ast method for processing, otherwise an error will be reported. In addition, parameter variable_standardization is given. If this parameter is true, the variable standardization method will be used to make sure the same variable has the same variable number.
+
+2.FormulaGroup: If you need to pass in a formula set, you can call this interface to get an ast forest. The tree structure in the forest is the same as that of Formula.
+
+Formula
+>>>>>>>>>>>>
+
+Formula: firstly, in the word segmentation function, the formula of the original text is segmented. In addition, ``Formula parse tree`` function is provided, which can represent the abstract syntax analysis tree of mathematical formula in the form of text or picture.
+
+This module also provides the function of formula variable standardization, such as determining that 'x' in several sub formulas is the same variable.
+
+Call the library
++++++++++
+
+::
+
+   import matplotlib.pyplot as plt
+   from EduNLP.Formula import Formula
+   from EduNLP.Formula.viz import ForestPlotter
+
+Initialization
++++++++++
+
+Incoming parameters: item
+
+Item is the latex formula or the abstract syntax parse tree generated after the formula is parsed and its type is str or List[Dict].
+
+::
+
+   >>> f=Formula("x^2 + x+1 = y")
+   >>> f
+   <Formula: x^2 + x+1 = y>
+
+View the specific content after formula segmentation
+++++++++++++++++++++++++++++
+
+- View node elements after formula segmentation
+
+::
+
+   >>> f.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]
+
+- View the abstract parse tree of formulas
+
+::
+
+   >>> f.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],'child': [1, 2],'father': None,'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [7, None],'child': None,'father': None,'forest': None}}]
+
+   >>> print('nodes: ',f.ast_graph.nodes)
+   nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]
+   >>> print('edges: ' ,f.ast_graph.edges)
+   edges:  [(0, 1), (0, 2)]
+
+- show the abstract parse tree by a picture
+
+::
+
+   >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
+   >>> plt.show()
+
+
+.. figure:: ../../_static/formula.png
+
+
+Variable standardization
++++++++++++
+
+This parameter makes the same variable have the same variable number.
+
+For example: the number of variable ``x`` is ``0`` and the number of variable ``y`` is ``1``.
+
+::
+
+   >>> f.variable_standardization().elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+
+FormulaGroup
+>>>>>>>>>>>>>>>
+
+Call ``FormulaGroup`` class to parse the equations. The related attributes and functions are the same as those above.
+
+::
+
+   import matplotlib.pyplot as plt
+   from EduNLP.Formula import Formula
+   from EduNLP.Formula import FormulaGroup
+   from EduNLP.Formula.viz import ForestPlotter
+   >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
+   >>> fs
+   <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
+   >>> fs.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},
+   {'id': 8, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 9, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},
+   {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 13, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 15, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 16, 'type': 'mathord', 'text': '\\pi', 'role': None}]
+   >>> fs.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],
+      'child': [1, 2],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2],
+      'child': None,
+      'father': 0,
+      'forest': [6, 12]}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [3, None],
+      'child': None,
+      'father': None,
+      'forest': [10, 14]}},
+   {'val': {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 8],
+      'child': [6, 7],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   show more (open the raw output data in a text editor) ...
+   >>> fs.variable_standardization()[0]
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+   >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
+
+.. figure:: ../../_static/formulagroup.png
+
+
+Text syntax structure parsing
+--------------------
+
+This section is mainly realized by EduNLP.SIF.Parse module. Its main function is to extract letters and numbers in the text and convert them into standard format.
+
+This module is mainly used as an *middle module* to parse the input text. Users generally do not call this module directly.
+
+Introduction of main content
++++++++++++++++
+
+1. Judge the type of the incoming text in the following order
+
+* is_chinese: its function is to match Chinese characters[\u4e00-\u9fa5].
+ 
+* is_alphabet: its function is to match alphabets other than formulas. Only the alphabets between two Chinese characters will be corrected (wrapped with $$), and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+ 
+* is_number: its function is to match numbers other than formulas. Only the numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+ 
+2. Match latex formula
+
+* If Chinese characters appear in latex, print warning only once.
+ 
+* Use _is_formula_legal function, check the completeness and analyzability of latex formula, and report an error for formulas that do not conform to latex syntax.
+
+Call the library
+>>>>>>>>>>>>
+
+::
+
+   from EduNLP.SIF.Parser import Parser
+
+Input
+>>>>>>>
+
+Types: str
+
+Content: question text
+
+::
+
+   >>> text1 = '生产某种零件的A工厂25名工人的日加工零件数_   _'
+   >>> text2 = 'X的分布列为(   )'
+   >>> text3 = '① AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．AC的中点为D'
+   >>> text4 = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
+
+Parsing
+>>>>>>>>>>>>>>>>>>>>
+
+::
+
+   >>> text_parser1 = Parser(text1)
+   >>> text_parser2 = Parser(text2)
+   >>> text_parser3 = Parser(text3)
+   >>> text_parser4 = Parser(text4)
+
+Related parameters description(?)
+>>>>>>>>>>>>
+
+- Try to convert text to standard format
+
+::
+
+   >>> text_parser1.description_list()
+   >>> print('text_parser1.text:',text_parser1.text)
+   text_parser1.text: 生产某种零件的$A$工厂$25$名工人的日加工零件数$\SIFBlank$
+   >>> text_parser2.description_list()
+   >>> print('text_parser2.text:',text_parser2.text)
+   text_parser2.text: $X$的分布列为$\SIFChoice$
+
+- Determine if the text has syntax errors
+
+::
+
+   >>> text_parser3.description_list()
+   >>> print('text_parser3.error_flag: ',text_parser3.error_flag)
+   text_parser3.error_flag:  1
+   >>> text_parser4.description_list()
+   >>> print('text_parser4.fomula_illegal_flag: ',text_parser4.fomula_illegal_flag)
+   text_parser4.fomula_illegal_flag:  1
+
diff --git a/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst b/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
new file mode 100644
index 00000000..2fc479c5
--- /dev/null
+++ b/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
@@ -0,0 +1,168 @@
+Formula syntax structure parsing
+--------------------
+
+This section is mainly realized by EduNLP.Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
+
+Introduction of Main Content
++++++++++++++++
+
+1.Formula: determine whether the single formula passed in is in str form. If so, use the ast method for processing, otherwise an error will be reported. In addition, parameter variable_standardization is given. If this parameter is true, the variable standardization method will be used to make sure the same variable has the same variable number.
+
+2.FormulaGroup: If you need to pass in a formula set, you can call this interface to get an ast forest. The tree structure in the forest is the same as that of Formula.
+
+Formula
+>>>>>>>>>>>>
+
+Formula: firstly, in the word segmentation function, the formula of the original text is segmented. In addition, ``Formula parse tree`` function is provided, which can represent the abstract syntax analysis tree of mathematical formula in the form of text or picture.
+
+This module also provides the function of formula variable standardization, such as determining that 'x' in several sub formulas is the same variable.
+
+Initialization
++++++++++
+
+Incoming parameters: item
+
+Item is the latex formula or the abstract syntax parse tree generated after the formula is parsed and its type is str or List[Dict].
+
+::
+
+   >>> f=Formula("x^2 + x+1 = y")
+   >>> f
+   <Formula: x^2 + x+1 = y>
+
+View the specific content after formula segmentation
+++++++++++++++++++++++++++++
+
+- View node elements after formula segmentation
+
+::
+
+   >>> f.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None}]
+
+- View the abstract parsing tree of formulas
+
+::
+
+   >>> f.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],'child': [1, 2],'father': None,'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None},
+   'structure': {'bro': [3, 5], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   'structure': {'bro': [4, 6], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   'structure': {'bro': [5, 7], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [6, 8], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [7, None],'child': None,'father': None,'forest': None}}]
+
+   >>> print('nodes: ',f.ast_graph.nodes)
+   nodes:  [0, 1, 2, 3, 4, 5, 6, 7, 8]
+   >>> print('edges: ' ,f.ast_graph.edges)
+   edges:  [(0, 1), (0, 2)]
+
+- show the abstract parse tree by a picture
+
+::
+
+   >>> ForestPlotter().export(f.ast_graph, root_list=[node["val"]["id"] for node in f.ast if node["structure"]["father"] is None],)
+   >>> plt.show()
+
+.. figure:: ../../../_static/formula.png
+
+Variable Standardization
++++++++++++
+
+This parameter makes the same variable have the same variable number.
+
+For example: the number of variable ``x`` is ``0`` and the number of variable ``y`` is ``1``.
+
+::
+
+   >>> f.variable_standardization().elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0},
+   {'id': 5, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 6, 'type': 'textord', 'text': '1', 'role': None},
+   {'id': 7, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 8, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+
+FormulaGroup
+>>>>>>>>>>>>>>>
+
+Call ``FormulaGroup`` class to parse the equations. The related attributes and functions are the same as those above.
+
+::
+
+   >>> fs = FormulaGroup(["x^2 = y", "x^3 = y^2", "x + y = \pi"])
+   >>> fs
+   <FormulaGroup: <Formula: x^2 = y>;<Formula: x^3 = y^2>;<Formula: x + y = \pi>>
+   >>> fs.elements
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   {'id': 7, 'type': 'textord', 'text': '3', 'role': 'sup'},
+   {'id': 8, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 9, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   {'id': 10, 'type': 'mathord', 'text': 'y', 'role': 'base'},
+   {'id': 11, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   {'id': 12, 'type': 'mathord', 'text': 'x', 'role': None},
+   {'id': 13, 'type': 'bin', 'text': '+', 'role': None},
+   {'id': 14, 'type': 'mathord', 'text': 'y', 'role': None},
+   {'id': 15, 'type': 'rel', 'text': '=', 'role': None},
+   {'id': 16, 'type': 'mathord', 'text': '\\pi', 'role': None}]
+   >>> fs.ast
+   [{'val': {'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 3],
+      'child': [1, 2],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   'structure': {'bro': [None, 2],
+      'child': None,
+      'father': 0,
+      'forest': [6, 12]}},
+   {'val': {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'},
+   'structure': {'bro': [1, None], 'child': None, 'father': 0, 'forest': None}},
+   {'val': {'id': 3, 'type': 'rel', 'text': '=', 'role': None},
+   'structure': {'bro': [0, 4], 'child': None, 'father': None, 'forest': None}},
+   {'val': {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None},
+   'structure': {'bro': [3, None],
+      'child': None,
+      'father': None,
+      'forest': [10, 14]}},
+   {'val': {'id': 5, 'type': 'supsub', 'text': '\\supsub', 'role': None},
+   'structure': {'bro': [None, 8],
+      'child': [6, 7],
+      'father': None,
+      'forest': None}},
+   {'val': {'id': 6, 'type': 'mathord', 'text': 'x', 'role': 'base'},
+   show more (open the raw output data in a text editor) ...
+   >>> fs.variable_standardization()[0]
+   [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}]
+   >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],)
+
+.. figure:: ../../../_static/formulagroup.png
diff --git a/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst b/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
new file mode 100644
index 00000000..bdfe6848
--- /dev/null
+++ b/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
@@ -0,0 +1,72 @@
+Text syntax structure parsing
+--------------------
+
+This section is mainly realized by EduNLP.SIF.Parse module. Its main function is to extract letters and numbers in the text and convert them into standard format.
+
+This module is mainly used as an *middle module* to parse the input text. Users generally do not call this module directly.
+
+Introduction of Main Content
++++++++++++++++
+
+1. Judge the type of the incoming text in the following order
+
+* is_chinese: its function is to match Chinese characters[\u4e00-\u9fa5].
+
+* is_alphabet: its function is to match alphabets other than formulas. Only the alphabets between two Chinese characters will be corrected (wrapped with $$), and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+
+* is_number: its function is to match numbers other than formulas. Only the numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
+
+2. Match latex formula
+
+* If Chinese characters appear in latex, print warning only once.
+
+* Use _is_formula_legal function, check the completeness and analyzability of latex formula, and report an error for formulas that do not conform to latex syntax.
+
+Input
+>>>>>>>
+
+Type: str
+
+Content：question text
+
+::
+
+   >>> text1 = '生产某种零件的A工厂25名工人的日加工零件数_   _'
+   >>> text2 = 'X的分布列为(   )'
+   >>> text3 = '① AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．AC的中点为D'
+   >>> text4 = '支持公式如$\\frac{y}{x}$，$\\SIFBlank$，$\\FigureID{1}$，不支持公式如$\\frac{ \\dddot y}{x}$'
+
+Parsing
+>>>>>>>>>>>>>>>>>>>>
+
+::
+
+   >>> text_parser1 = Parser(text1)
+   >>> text_parser2 = Parser(text2)
+   >>> text_parser3 = Parser(text3)
+   >>> text_parser4 = Parser(text4)
+
+Related parameters description(?)
+>>>>>>>>>>>>
+
+- Try to convert text to standard format
+
+::
+
+   >>> text_parser1.description_list()
+   >>> print('text_parser1.text:',text_parser1.text)
+   text_parser1.text: 生产某种零件的$A$工厂$25$名工人的日加工零件数$\SIFBlank$
+   >>> text_parser2.description_list()
+   >>> print('text_parser2.text:',text_parser2.text)
+   text_parser2.text: $X$的分布列为$\SIFChoice$
+
+- Determine if the text has syntax errors
+
+::
+
+   >>> text_parser3.description_list()
+   >>> print('text_parser3.error_flag: ',text_parser3.error_flag)
+   text_parser3.error_flag:  1
+   >>> text_parser4.description_list()
+   >>> print('text_parser4.fomula_illegal_flag: ',text_parser4.fomula_illegal_flag)
+   text_parser4.fomula_illegal_flag:  1
diff --git a/docs/source/tutorial/en/pretrain.rst b/docs/source/tutorial/en/pretrain.rst
new file mode 100644
index 00000000..9319b87d
--- /dev/null
+++ b/docs/source/tutorial/en/pretrain.rst
@@ -0,0 +1,130 @@
+Pre-training
+=======
+
+In the field of NLP, Pre-trained Language Models has become a very important basic technology.
+In this chapter, we will introduce the pre training tools in EduNLP:
+
+* How to train with a corpus to get a pre-trained model
+* How to load the pre-trained model
+* Public pre-trained models
+
+Import modules
+----------
+
+::
+
+   from EduNLP.I2V import get_pretrained_i2v
+   from EduNLP.Vector import get_pretrained_t2v
+
+Train the Model
+------------
+
+Call train_Vector function interface directly to make the training model easier. This section calls the relevant training models in the gensim library. At present, the training methods of "sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf" are provided. Parameter embedding_dim is also provided for users to determine vector dimension according to their needs.
+
+Basic Steps
+##################
+
+1.Determine the type of model and select the appropriate tokenizer (GensimWordTokenizer、 GensimSegTokenizer) to finish tokenization.
+
+2.Call train_vector function to get the required pre-trained model。
+
+Examples：
+
+::
+
+   >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+   
+   # 10 dimension with fasstext method
+   train_vector(sif_items, "../../../data/w2v/gensim_luna_stem_tf_", 10, method="d2v")
+
+
+Load models
+--------
+
+Transfer the obtained model to the I2V module to load the model.
+ 
+Examples：
+
+::
+
+   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+
+The overview of our public model
+------------
+
+Version description
+##################
+
+First level version:
+
+* Public version 1 (luna_pub): college entrance examination
+* Public version 2 (luna_pub_large): college entrance examination + regional examination
+
+Second level version:
+
+* Minor subjects(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
+* Major subjects(science, arts and all subject)
+
+Third level version【to be finished】:
+
+* Don't use third-party initializers
+* Use third-party initializers
+
+Description of train data in models
+##################
+
+* Currently, the data used in w2v and d2v models are the subjects of senior high school.
+* test data:`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
+
+At present, the following models are provided. More models of different subjects and question types are being trained. Please look forward to it.
+    "d2v_all_256" (all subject), "d2v_sci_256" (Science), "d2v_eng_256" (English)，"d2v_lit_256" (Arts)
+
+
+Examples of Model Training
+------------
+
+Get the dataset
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   prepare_dataset  <../../build/blitz/pretrain/prepare_dataset.ipynb>
+
+An example of d2v in gensim model
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v_bow_tfidf  <../../build/blitz/pretrain/gensim/d2v_bow_tfidf.ipynb>
+   d2v_general  <../../build/blitz/pretrain/gensim/d2v_general.ipynb>
+   d2v_stem_tf  <../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
+
+An example of w2v in gensim model
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   w2v_stem_text  <../../build/blitz/pretrain/gensim/w2v_stem_text.ipynb>
+   w2v_stem_tf  <../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
+
+An example of seg_token
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v.ipynb  <../../build/blitz/pretrain/seg_token/d2v.ipynb>
+   d2v_d1  <../../build/blitz/pretrain/seg_token/d2v_d1.ipynb>
+   d2v_d2  <../../build/blitz/pretrain/seg_token/d2v_d2.ipynb>
\ No newline at end of file
diff --git a/docs/source/tutorial/en/pretrain/loading.rst b/docs/source/tutorial/en/pretrain/loading.rst
new file mode 100644
index 00000000..31fa3ea8
--- /dev/null
+++ b/docs/source/tutorial/en/pretrain/loading.rst
@@ -0,0 +1,11 @@
+Load models
+--------
+
+Transfer the obtained model to the I2V module to load the model.
+ 
+Examples：
+
+::
+
+        >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+        >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
diff --git a/docs/source/tutorial/en/pretrain/pub.rst b/docs/source/tutorial/en/pretrain/pub.rst
new file mode 100644
index 00000000..34407745
--- /dev/null
+++ b/docs/source/tutorial/en/pretrain/pub.rst
@@ -0,0 +1,74 @@
+The overview of our public model
+------------
+
+
+Version Description
+##################
+
+First level version:
+
+* Public version 1 (luna_pub): college entrance examination
+* Public version 2 (luna_pub_large): college entrance examination + regional examination
+
+Second level version:
+
+* Minor subjects(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
+* Major subjects(science, arts and all subject)
+
+Third level version【to be finished】:
+
+* Don't use third-party initializers
+* Use third-party initializers
+
+Description of train data in models
+##################
+
+* Currently, the data used in w2v and d2v models are the subjects of senior high school.
+* test data:`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
+
+At present, the following models are provided. More models of different subjects and question types are being trained. Please look forward to it.
+    "d2v_all_256" (all subject), "d2v_sci_256" (Science), "d2v_eng_256" (English)，"d2v_lit_256" (Arts)
+
+Examples of model training
+------------
+
+Get the dataset
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   prepare_dataset  <../../../build/blitz/pretrain/prepare_dataset.ipynb>
+
+An example of d2v in gensim model
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v_bow_tfidf  <../../../build/blitz/pretrain/gensim/d2v_bow_tfidf.ipynb>
+   d2v_general  <../../../build/blitz/pretrain/gensim/d2v_general.ipynb>
+   d2v_stem_tf  <../../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
+
+An example of w2v in gensim model
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   w2v_stem_text  <../../../build/blitz/pretrain/gensim/w2v_stem_text.ipynb>
+   w2v_stem_tf  <../../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
+
+An example of seg_token
+####################
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   d2v.ipynb  <../../../build/blitz/pretrain/seg_token/d2v.ipynb>
+   d2v_d1  <../../../build/blitz/pretrain/seg_token/d2v_d1.ipynb>
+   d2v_d2  <../../../build/blitz/pretrain/seg_token/d2v_d2.ipynb>
diff --git a/docs/source/tutorial/en/pretrain/start.rst b/docs/source/tutorial/en/pretrain/start.rst
new file mode 100644
index 00000000..9c5bc241
--- /dev/null
+++ b/docs/source/tutorial/en/pretrain/start.rst
@@ -0,0 +1,24 @@
+Train the model
+------------
+
+Call train_Vector function interface directly to make the training model easier. This section calls the relevant training models in the gensim library. At present, the training methods of "sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf" are provided. Parameter embedding_dim is also provided for users to determine vector dimension according to their needs.
+
+Basic Steps
+##################
+
+1.Determine the type of model and select the appropriate tokenizer (GensimWordTokenizer、 GensimSegTokenizer) to finish tokenization.
+
+2.Call train_vector function to get the required pre-trained model。
+
+Examples：
+
+::
+
+        >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+        >>> print(token_item.tokens[:10])
+        ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+        
+        # 10 dimension with fasstext method
+        train_vector(sif_items, "../../../data/w2v/gensim_luna_stem_tf_", 10, method="d2v")
diff --git a/docs/source/tutorial/en/seg.rst b/docs/source/tutorial/en/seg.rst
new file mode 100644
index 00000000..ad2696a2
--- /dev/null
+++ b/docs/source/tutorial/en/seg.rst
@@ -0,0 +1,187 @@
+Component Segmentation
+=========
+
+Educational resource is a kind of multimodal data, including data such as text, picture, formula and so on.
+At the same time, it may also contain different components semantically, such as question stems, options, etc. Therefore, we first need to identify and segment the different components of educational resources:
+
+* Semantic Component Segmentation
+* Structural Component Segmentation
+
+Main Processing Contents
+--------------------
+
+1. Convert multiple-choice questions in the form of dict to qualified item by `Syntax parsing <parse.rst>`_;
+
+2. The input items are segmented and grouped according to the element type.
+
+Semantic Component Segmentation
+------------
+
+Because multiple-choice questions are given in the form of dict, it is necessary to convert them into text format while retaining their data relationship. This function can be realized by dict2str4sif function which can convert multiple-choice question items into character format and identify question stem and options。
+
+Import Modules
++++++++++
+
+::
+
+ from EduNLP.utils import dict2str4sif
+
+Basic Usage
+++++++++++++++++++
+
+::
+
+ >>> item = {
+ ...     "stem": r"若复数$z=1+2 i+i^{3}$，则$|z|=$",
+ ...     "options": ['0', '1', r'$\sqrt{2}$', '2'],
+ ... }
+ >>> dict2str4sif(item) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+
+Optional additional parameters / interfaces
+++++++++++++++++++++++
+
+1.add_list_no_tag: if this parameter is true, it means that you need to count the labels in the options section.
+
+::
+
+ >>> dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+ 
+ >>> dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2$\\SIFTag{options_end}$'
+
+2.tag_mode: The location for the label can be selected using this parameter. 'delimiter' is to label both the beginning and the end,'head' is to label only the head, and 'tail' is to label only the tail.
+
+::
+
+ >>> dict2str4sif(item, tag_mode="head") # doctest: +ELLIPSIS
+ '$\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{options}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2'
+ 
+ >>> dict2str4sif(item, tag_mode="tail") # doctest: +ELLIPSIS
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options}$'
+
+3.key_as_tag: If this parameter is false, this process will only adds $\SIFSep$ between the options without distinguishing the type of segmentation label.
+
+::
+
+ >>> dict2str4sif(item, key_as_tag=False)
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
+
+Structural Component Segmentation
+------------
+
+This step is to segment sliced items. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
+
+
+There are two modes:
+
+* linear mode: it is used for text processing (word segmentation using jieba library);
+
+* ast mode: it is used to parse the formula.
+
+Basic Segmentation process:
+
+- Match components with regular expression matching
+
+- Process the components with special structures, such as converting the base64 encoded picture to numpy form
+
+- Classify the elements into each element group
+
+- Enter the corresponding parameters as required to get the filtered results
+
+Import Modules
++++++++++
+
+::
+
+ from EduNLP.SIF.segment import seg
+ from EduNLP.SIF import sif4sci
+
+Basic Usage
+++++++++++++++++++
+
+::
+
+ >>> test_item = r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$"
+ >>> seg(test_item)
+ >>> ['如图所示，则', '\\bigtriangleup ABC', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+
+Optional additional parameters/interfaces
+++++++++++++++++++++++
+
+1.describe: count the number of elements of different types
+
+::
+
+ >>> s.describe()
+ {'t': 3, 'f': 1, 'g': 1, 'm': 1}
+
+2.filter: this interface can screen out one or more types of elements.
+
+Using this interface, you can pass in a "keep" parameter or a special character directly to choose what type of elements to retain.
+
+Element type represented by symbol:
+
+-   "t": text
+-   "f": formula
+-   "g": figure
+-   "m": question mark
+-   "a": tag
+-   "s": sep tag
+
+::
+
+ >>> with s.filter("f"):
+ ...     s
+ ['如图所示，则', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+ >>> with s.filter(keep="t"):
+ ...     s
+ ['如图所示，则', '的面积是', '。']
+
+3.symbol: this interface can convert some types of data into special symbols
+
+Element type represented by symbol:
+
+-   "t": text
+-   "f": formula
+-   "g": figure
+-   "m": question mark
+
+::
+
+ >>> seg(test_item, symbol="fgm")
+ ['如图所示，则', '[FORMULA]', '的面积是', '[MARK]', '。', '[FIGURE]']
+ >>> seg(test_item, symbol="tfgm")
+ ['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']
+
+In addition，sif4sci function is also provided, which can easily convert items into the result processed by Structural Component Segmentation
+
+::
+
+ >>> segments = sif4sci(item["stem"], figures=figures, tokenization=False)
+ >>> segments
+ ['如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形', 'ABC', '的斜边', 'BC', ', 直角边', 'AB', ', ', 'AC', '.', '\\bigtriangleup ABC', '的三边所围成的区域记为', 'I', ',黑色部分记为', 'II', ', 其余部分记为', 'III', '.在整个图形中随机取一点，此点取自', 'I,II,III', '的概率分别记为', 'p_1,p_2,p_3', ',则', '\\SIFChoice', \FigureID{1}]
+
+- When calling this function, you can selectively output a certain type of data according to your needs
+
+::
+
+ >>> segments.formula_segments
+ ['ABC',
+ 'BC',
+ 'AB',
+ 'AC',
+ '\\bigtriangleup ABC',
+ 'I',
+ 'II',
+ 'III',
+ 'I,II,III',
+ 'p_1,p_2,p_3']
+
+- Similar to seg function, sif4sci function also provides depth options to help with your research ----- By modifying the ``symbol`` parameter, different components can be transformed into specific markers.
+
+::
+
+ >>> sif4sci(item["stem"], figures=figures, tokenization=False, symbol="tfgm")
+ ['[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[FIGURE]']
diff --git a/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst b/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
new file mode 100644
index 00000000..3901f4cb
--- /dev/null
+++ b/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
@@ -0,0 +1,47 @@
+Semantic Component Segmentation
+------------
+
+Because multiple-choice questions are given in the form of dict, it is necessary to convert them into text format while retaining their data relationship. This function can be realized by dict2str4sif function which can convert multiple-choice question items into character format and identify question stem and options。
+
+
+Basic Usage
+++++++++++++++++++
+
+::
+
+ >>> item = {
+ ...     "stem": r"若复数$z=1+2 i+i^{3}$，则$|z|=$",
+ ...     "options": ['0', '1', r'$\sqrt{2}$', '2'],
+ ... }
+ >>> dict2str4sif(item) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+
+Optional additional parameters / interfaces
+++++++++++++++++++++++
+
+1.add_list_no_tag: if this parameter is true, it means that you need to count the labels in the options section.
+
+::
+
+ >>> dict2str4sif(item, add_list_no_tag=True) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
+ 
+ >>> dict2str4sif(item, add_list_no_tag=False) # doctest: +ELLIPSIS
+ '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2$\\SIFTag{options_end}$'
+
+2.tag_mode: The location for the label can be selected using this parameter. 'delimiter' is to label both the beginning and the end,'head' is to label only the head, and 'tail' is to label only the tail.
+
+::
+
+ >>> dict2str4sif(item, tag_mode="head") # doctest: +ELLIPSIS
+ '$\\SIFTag{stem}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{options}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2'
+ 
+ >>> dict2str4sif(item, tag_mode="tail") # doctest: +ELLIPSIS
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options}$'
+
+3.key_as_tag: If this parameter is false, this process will only adds $\SIFSep$ between the options without distinguishing the type of segmentation label.
+
+::
+
+ >>> dict2str4sif(item, key_as_tag=False)
+ '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
\ No newline at end of file
diff --git a/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst b/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
new file mode 100644
index 00000000..8661c3d6
--- /dev/null
+++ b/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
@@ -0,0 +1,67 @@
+Structural Component Segmentation
+------------
+
+This step is to segment sliced items. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
+
+
+There are two modes:
+
+* linear mode: it is used for text processing (word segmentation using jieba library);
+
+* ast mode: it is used to parse the formula.
+
+Basic Usage
+++++++++++++++++++
+
+::
+
+ >>> test_item = r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$"
+ >>> seg(test_item)
+ >>> ['如图所示，则', '\\bigtriangleup ABC', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+
+Optional additional parameters/interfaces
+++++++++++++++++++++++
+
+1.describe: count the number of elements of different types
+
+::
+
+ >>> s.describe()
+ {'t': 3, 'f': 1, 'g': 1, 'm': 1}
+
+2.filter: this interface can screen out one or more types of elements.
+
+Using this interface, you can pass in a "keep" parameter or a special character directly to choose what type of elements to retain.
+
+Element type represented by symbol:
+   "t": text
+   "f": formula
+   "g": figure
+   "m": question mark
+   "a": tag
+   "s": sep tag
+
+::
+
+ >>> with s.filter("f"):
+ ...     s
+ ['如图所示，则', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
+ >>> with s.filter(keep="t"):
+ ...     s
+ ['如图所示，则', '的面积是', '。']
+
+3.symbol: this interface can convert some types of data into special symbols
+
+Element type represented by symbol:
+
+-   "t": text
+-   "f": formula
+-   "g": figure
+-   "m": question mark
+
+::
+
+ >>> seg(test_item, symbol="fgm")
+ ['如图所示，则', '[FORMULA]', '的面积是', '[MARK]', '。', '[FIGURE]']
+ >>> seg(test_item, symbol="tfgm")
+ ['[TEXT]', '[FORMULA]', '[TEXT]', '[MARK]', '[TEXT]', '[FIGURE]']
diff --git a/docs/source/tutorial/en/sif.rst b/docs/source/tutorial/en/sif.rst
index 0cbe7cf3..877cb503 100644
--- a/docs/source/tutorial/en/sif.rst
+++ b/docs/source/tutorial/en/sif.rst
@@ -1,2 +1,145 @@
 Standard Item Format
-====================
+===============
+
+version: 0.2
+
+For the convenience of follow-up research and use, we need a unified test question grammar standard.
+
+Grammar Rules
+-----------
+
+1. Only Chinese characters, Chinese and English punctuation and line breaks are allowed in the question text.
+
+2. Represent underlines of blanks and brackets of choices with ``\$\SIFBlank\$`` and ``\$\SIFChoice\$`` respectively.
+
+3. We use ``$\FigureID{ uuid }$`` or Base64 to represent pictures. Especially, ``$\FormFigureID{ uuid }$`` is used to represent formulas pictures.
+
+4. Text format description: we represent text in different styles with ``$\textf{item,CHAR_EN}$``. Currently, we have defined some styles: b-bold, i-italic, u-underline, w-wave, d-dotted, t-title. CHAR_EN Labels can be mixed and sorted alphabetically. An example: $\textf{EduNLP, b}$ looks **EduNLP**
+
+5. Other mathematical symbols like English letters, Roman characters and numbers need to be expressed in latex format, that is, embedded in `$$`.
+
+6. For the entry standard of molecular formula, please refer to `INCHI <https://zh.wikipedia.org/wiki/%E5%9B%BD%E9%99%85%E5%8C%96%E5%90%88%E7%89%A9%E6%A0%87%E8%AF%86>`_ for the time being.
+
+7. Currently, there are no requirements for latex internal syntax.
+
+::
+
+  1. Item -> CHARACTER|EN_PUN_LIST|CH_PUN_LIST|FORMULA|QUES_MARK
+  2. EN_PUN_LIST -> [',', '.', '?', '!', ':', ';', '\'', '\"', '(', ')', ' ','_','/','|','\\','<','>','[',']','-']
+  3. CH_PUN_LIST -> ['，', '。', '！', '？', '：','；', '‘', '’', '“', '”', '（', '）', ' ', '、','《','》','—','．']
+  4. FORMULA -> $latex formula$ | $\FormFigureID{UUID}$ | $\FormFigureBase64{BASE64}$
+  5. FIGURE -> $\FigureID{UUID}$ | $\FigureBase64{BASE64}$
+  6. UUID -> [a-zA-Z\-0-9]+
+  7. CHARACTER -> CHAR_EN | CHAR_CH
+  8. CHAR_EN -> [a-zA-Z]+
+  9. CHAR_CH -> [\u4e00-\u9fa5]+
+  10. DIGITAL -> [0-9]+
+  11. QUES_MARK -> $\SIFBlank$ | $\SIFChoice$
+
+
+Tips
++++++++++++++++
+
+1. Reserved characters and escape characters.
+
+2. Numbers.
+
+3. Choices and blanks.
+
+4. A single number or letter is also required to be between `$$` (automatic verification could already realize it).
+
+5. Try to make sure Chinese is not included in the latex formula such as ``\text{CHAR_CH}``.
+
+6. When importing data using MySQL database, an ``\`` is automatically ignored which needs to be further processed as``\\``.
+
+Examples
+-----------------
+
+Standard Format:
+
+::
+
+ 1. 若$x,y$满足约束条件$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，则$z=x+7 y$的最大值$\\SIFUnderline$'
+ 
+ 2. 已知函数$f(x)=|3 x+1|-2|x|$画出$y=f(x)$的图像求不等式$f(x)>f(x+1)$的解集$\\PictureID{3bf2ddf4-8af1-11eb-b750-b46bfc50aa29}$$\\PictureID{59b8bd14-8af1-11eb-93a5-b46bfc50aa29}$$\\PictureID{63118b3a-8b75-11eb-a5c0-b46bfc50aa29}$$\\PictureID{6a006179-8b76-11eb-b386-b46bfc50aa29}$$\\PictureID{088f15eb-8b7c-11eb-a86f-b46bfc50aa29}$
+
+Non-standard Format:
+
+1. Letters, numbers and mathematical symbols are mixed:
+
+    For example:
+    
+    ``完成下面的2x2列联表，``
+    
+    ``（单位：m3）``
+    
+    ``则输出的n=``
+    
+2. Some special mathematical symbols are not represented by the latex formula:
+
+    For example:
+    
+    ``命题中真命题的序号是 ①``
+    
+    ``AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．若D为AC的中点``
+    
+3. There are unicode encoded characters in the text:
+
+    For example:
+    ``则$a$的取值范围是（\u3000\u3000）``
+
+Functions for judging whether text is in SIF format and converting to SIF format
+--------------------------------------------
+
+Call the Library
+++++++++
+::
+
+    from EduNLP.SIF import is_sif, to_sif
+
+is_sif
++++++++++++
+
+::
+
+    >>> text1 = '若$x,y$满足约束条件' 
+    >>> text2 = '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' 
+    >>> text3 = '则$z=x+7 y$的最大值$\\SIFUnderline$'
+    >>> text4 = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
+    >>> is_sif(text1)
+    True
+    >>> is_sif(text2)
+    True
+    >>> is_sif(text3)
+    True
+    >>> is_sif(text4)
+    False
+
+to_sif
++++++++++++
+
+::
+
+    >>> text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
+    >>> to_sif(text)
+    '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'
+
+
+Change Log
+----------------
+
+2021-05-18
+
+Changed
+
+1. Originally, we use ``\$\SIFUnderline\$`` and ``\$\SIFBracket\$`` to represent underlines of blanks and brackets of choices. Now we represent them with ``\$\SIFBlank\$`` and ``\$\SIFChoice\$``.
+
+2. Originally, we used ``$\PictureID{ uuid }$`` to represent pictures, but now we use ``$\FigureID{ uuid }$`` instead. Especially, ``$\FormFigureID{ uuid }$`` is used to represent formulas pictures.
+
+2021-06-28 
+  
+Added:
+
+1. There should not be line breaks between the notation ``$$``.
+
+2. Add text format description.
diff --git a/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst b/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
new file mode 100644
index 00000000..eb624e94
--- /dev/null
+++ b/docs/source/tutorial/en/tokenization/GensimSegTokenizer.rst
@@ -0,0 +1,9 @@
+GensimSegTokenizer
+=====================
+
+By default, the pictures, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text, formulas and labels. Also, the tokenizer uses linear analysis method for text and abstract analysis method of syntax tree for formulas.
+
+Compared to GensimWordTokenizer, the main differences are:
+
+* It provides the depth option for segmentation position, such as \SIFSep and \SIFTag.
+* By default, labels are inserted in the header of item components (such as text and formula).
\ No newline at end of file
diff --git a/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst b/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
new file mode 100644
index 00000000..98d4b10a
--- /dev/null
+++ b/docs/source/tutorial/en/tokenization/GensimWordTokenizer.rst
@@ -0,0 +1,23 @@
+GensimWordTokenizer
+=====================
+
+By default, the pictures, blanks in the question text and other parts of the incoming item are converted into special characters for data security and the tokenization of text, formulas, labels and separators. Also, the tokenizer uses linear analysis method for text and abstract syntax tree method for formulas respectively. You can choose each of them by ``general`` parameter:
+
+-true, it means that the incoming item conforms to SIF and the linear analysis method should be used.
+-false, it means that the incoming item doesn't conform to SIF and the abstract syntax tree method should be used.
+
+Examples
+----------
+        
+::
+
+        >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+        >>> print(token_item.tokens[:10])
+        ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+        >>> tokenizer = GensimWordTokenizer(symbol="fgmas", general=False)
+        >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+        ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+        >>> print(token_item.tokens[:10])
+        ['公式', '[FORMULA]', '如图', '[FIGURE]', '[FORMULA]', '约束条件', '公式', '[FORMULA]', '[SEP]', '[FORMULA]']
diff --git a/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst b/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
new file mode 100644
index 00000000..8c36e67c
--- /dev/null
+++ b/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
@@ -0,0 +1,31 @@
+PureTextTokenizer
+================
+
+By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security. At the same time, special formulas such as $\\FormFigureID{...}$ and $\\FormFigureBase64{...}$ are screened out to facilitate the tokenization of text and plain text formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
+
+Examples
+----------
+
+::
+
+    >>> tokenizer = PureTextTokenizer()
+    >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+    ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)[:10]
+    ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
+    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
+    >>> items = [{
+    ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+    ... "options": ["1", "2"]
+    ... }]
+    >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
diff --git a/docs/source/tutorial/en/tokenization/TextTokenizer.rst b/docs/source/tutorial/en/tokenization/TextTokenizer.rst
new file mode 100644
index 00000000..08991be6
--- /dev/null
+++ b/docs/source/tutorial/en/tokenization/TextTokenizer.rst
@@ -0,0 +1,27 @@
+TextTokenizer
+================
+
+By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text and formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
+
+
+Examples
+----------
+
+::
+
+    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+    >>> tokenizer = TextTokenizer()
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
+    >>> items = [{
+    ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+    ... "options": ["1", "2"]
+    ... }]
+    >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
diff --git a/docs/source/tutorial/en/tokenize.rst b/docs/source/tutorial/en/tokenize.rst
new file mode 100644
index 00000000..3411b74b
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize.rst
@@ -0,0 +1,172 @@
+Tokenization
+=======
+
+Tokenization, known as word segmentation and sentence segmentation, is a basic but very important step in the field of NLP.
+In EduNLP, we divided Tokenization into different levels according to different granularity. To avoid ambiguity, we define as follows:
+
+* Word/char level: word segmentation
+
+* Sentence level: sentence segmentation
+
+* Resource level: tokenization
+
+This module provides tokenization function of question text, converting questions into token sequences to facilitate the vectorization of questions. After that, each element in the sliced item needs word segmentation. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
+
+There are two modes: one is linear mode, which is used for text processing (word segmentation using jieba library). The other one is ast mode, which is used to parse the formula.
+
+Word Segmentation
+-------
+
+Text-tokenization: A sentence (without formulas) consists of several "words" in order. The process of dividing a sentence into several words is called "Text-tokenization". According to the granularity of "words", it can be subdivided into "Word-tokenization" and "Char-tokenization".
+
+::
+
+   - Word-tokenization: each phrase is a token.
+   
+   - Char-tokenization: each character is a token.
+    
+
+Text-tokenization is divided into two main steps:
+
+1. Text-tokenization:
+
+   - Word-tokenization: use the word segmentation tool to segment and extract words from the question text. Our project supports `jieba`.
+
+   - Char-tokenization: process text by character.
+
+2. Filter: filter the specified stopwords.
+
+   The default stopwords used in this project:`[stopwords] <https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt>`_
+   You can also use your own stopwords. The following example demonstrates how to use.
+
+Examples:
+
+::
+
+   from EduNLP.SIF.tokenization.text import tokenize 
+   >>> text = "三角函数是基本初等函数之一"
+   >>> tokenize(text, granularity="word")
+   ['三角函数', '初等', '函数']
+   
+   >>> tokenize(text, granularity="char")
+   ['三', '角', '函', '数', '基', '初', '函', '数']
+    
+Sentence Segmentation
+-------
+
+During the process of sentence segmentation, a long document is divided into several sentences. Each sentence is a "token" (to be realized).
+
+Tokenization
+-------
+Tokenization is comprehensive analysis. In this process, sentences with formulas are segmented into several markers. Each marker is a "token".
+
+The implementation of this function is tokenize function. The required results can be obtained by passing in items after Structural Component Segmentation.
+
+::
+
+   from EduNLP.Tokenizer import get_tokenizer
+   >>> items = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$"
+   >>> tokenize(SegmentList(items))
+   ['如图所示', '三角形', 'ABC', '面积', '\\\\SIFBlank', \\FigureID{1}]
+   >>> tokenize(SegmentList(items),formula_params={"method": "ast"})
+   ['如图所示', '三角形', <Formula: ABC>, '面积', '\\\\SIFBlank', \\FigureID{1}]
+
+
+
+You can view ``./EduNLP/Tokenizer/tokenizer.py`` and ``./EduNLP/Pretrain/gensim_vec.py`` for more tokenizers. We provide some encapsulated tokenizers for users to call them conveniently. Following is a complete list of tokenizers:
+
+- TextTokenizer
+
+- PureTextTokenizer
+
+- GensimSegTokenizer
+
+- GensimWordTokenizer
+
+
+TextTokenizer
++++++++++++++++++++++
+
+By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text and formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
+
+::
+
+   >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+   >>> tokenizer = TextTokenizer()
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+   >>> items = [{
+   ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+   ... "options": ["1", "2"]
+   ... }]
+   >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+
+PureTextTokenizer
++++++++++++++++++++++
+
+By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security. At the same time, special formulas such as $\\FormFigureID{...}$ and $\\FormFigureBase64{...}$ are screened out to facilitate the tokenization of text and plain text formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
+
+::
+
+   >>> tokenizer = PureTextTokenizer()
+   >>> items = ["有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$"]
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)[:10]
+   ['公式', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[SEP]', 'z']
+   >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+   >>> tokens = tokenizer(items)
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+   >>> items = [{
+   ... "stem": "已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$",
+   ... "options": ["1", "2"]
+   ... }]
+   >>> tokens = tokenizer(items, key=lambda x: x["stem"])
+   >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+   ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+   '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+   '\\quad', 'A', '\\cap', 'B', '=']
+
+GensimWordTokenizer
++++++++++++++++++++++++
+
+By default, the pictures, blanks in the question text and other parts of the incoming item are converted into special characters for data security and the tokenization of text, formulas, labels and separators. Also, the tokenizer uses linear analysis method for text and abstract syntax tree method for formulas respectively. You can choose each of them by ``general`` parameter:
+
+-true, it means that the incoming item conforms to SIF and the linear analysis method should be used.
+-false, it means that the incoming item doesn't conform to SIF and the abstract syntax tree method should be used.
+
+GensimSegTokenizer
+++++++++++++++++++++
+
+By default, the pictures, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security and tokenization of text, formulas and labels. Also, the tokenizer uses linear analysis method for text and abstract analysis method of syntax tree for formulas.
+
+Compared to GensimWordTokenizer, the main differences are:
+
+* It provides the depth option for segmentation position, such as \SIFSep and \SIFTag.
+* By default, labels are inserted in the header of item components (such as text and formulas).
+
+Examples
+----------
+        
+::
+
+   >>> tokenizer = GensimWordTokenizer(symbol="gmas", general=True)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]']
+   >>> tokenizer = GensimWordTokenizer(symbol="fgmas", general=False)
+   >>> token_item = tokenizer("有公式$\\FormFigureID{wrong1?}$，如图$\\FigureID{088f15ea-xxx}$,\
+   ... 若$x,y$满足约束条件公式$\\FormFigureBase64{wrong2?}$,$\\SIFSep$，则$z=x+7 y$的最大值为$\\SIFBlank$")
+   >>> print(token_item.tokens[:10])
+   ['公式', '[FORMULA]', '如图', '[FIGURE]', '[FORMULA]', '约束条件', '公式', '[FORMULA]', '[SEP]', '[FORMULA]']
diff --git a/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst b/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst
new file mode 100644
index 00000000..1a8d4950
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst	
@@ -0,0 +1,3 @@
+Sentence Segmentation
+-------
+During the process of sentence segmentation, a long document is divided into several sentences. Each sentence is a "token" (to be realized).
diff --git a/docs/source/tutorial/en/tokenize/Tokenization.rst b/docs/source/tutorial/en/tokenize/Tokenization.rst
new file mode 100644
index 00000000..fad25912
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize/Tokenization.rst
@@ -0,0 +1,29 @@
+Tokenization
+-------
+Tokenization is comprehensive analysis. In this process, sentences with formulas are segmented into several markers. Each marker is a "token".
+We provide some encapsulated tokenizers for users to call them conveniently. The following is a complete list of tokenizers.
+
+Examples
+    
+::
+
+    >>> items = ["已知集合$A=\\left\\{x \\mid x^{2}-3 x-4<0\\right\\}, \\quad B=\\{-4,1,3,5\\}, \\quad$ 则 $A \\cap B=$"]
+    >>> tokenizer = TextTokenizer()
+    >>> tokens = tokenizer(items)
+    >>> next(tokens)  # doctest: +NORMALIZE_WHITESPACE
+    ['已知', '集合', 'A', '=', '\\left', '\\{', 'x', '\\mid', 'x', '^', '{', '2', '}', '-', '3', 'x', '-', '4', '<',
+    '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
+    '\\quad', 'A', '\\cap', 'B', '=']
+
+
+
+You can view ``./EduNLP/Tokenizer/tokenizer.py`` and ``./EduNLP/Pretrain/gensim_vec.py`` for more tokenizers. Following is a complete list of tokenizers:
+
+.. toctree::
+  :maxdepth: 1
+  :titlesonly:
+
+  ../tokenization/TextTokenizer
+  ../tokenization/PureTextTokenizer
+  ../tokenization/GensimSegTokenizer
+  ../tokenization/GensimWordTokenizer
diff --git a/docs/source/tutorial/en/tokenize/WordSegmentation.rst b/docs/source/tutorial/en/tokenize/WordSegmentation.rst
new file mode 100644
index 00000000..a85f4dae
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize/WordSegmentation.rst
@@ -0,0 +1,36 @@
+Word segmentation
+-------
+
+Text-tokenization: A sentence (without formulas) consists of several "words" in order. The process of dividing a sentence into several words is called "Text-tokenization". According to the granularity of "words", it can be subdivided into "Word-tokenization" and "Char-tokenization".
+
+::
+
+   - Word-tokenization: each phrase is a token.
+
+   - Char-tokenization: each character is a token.
+
+
+Text-tokenization is divided into two main steps:
+
+1. Text-tokenization:
+
+   - Word-tokenization: use the word segmentation tool to segment and extract words from the question text. Our project supports `jieba`.
+
+   - Char-tokenization: process text by character.
+
+2. Filter: filter the specified stopwords.
+
+   The default stopwords used in this project:`[stopwords] <https://github.com/bigdata-ustc/EduNLP/blob/master/EduNLP/meta_data/sif_stopwords.txt>`_
+   You can also use your own stopwords. The following example demonstrates how to use.
+
+Examples:
+
+::
+    
+    >>> text = "三角函数是基本初等函数之一"
+    >>> tokenize(text, granularity="word")
+    ['三角函数', '初等', '函数']
+    
+    >>> tokenize(text, granularity="char")
+    ['三', '角', '函', '数', '基', '初', '函', '数']
+    
diff --git a/docs/source/tutorial/en/vectorization.rst b/docs/source/tutorial/en/vectorization.rst
new file mode 100644
index 00000000..5b744eeb
--- /dev/null
+++ b/docs/source/tutorial/en/vectorization.rst
@@ -0,0 +1,157 @@
+Vectorization
+=========
+
+This section provides a simple interface to convert the incoming items into vectors directly. Currently, the option of whether to use the pre training model is provided. You can choose according to your needs. If you don't want to use the pre-trained model, you can call D2V directly, or call get_pretrained_i2v function if you want to use the pre-trained model.
+
+- Don't use the pre-trained model
+
+- Use the pre-trained model
+
+Overview Flow
+---------------------------
+
+1.Perform `syntax parsing <parse.rst>`_ on incoming items to get items in SIF format；
+
+2.Perform `component segmentation <seg.rst>`_ on sif_items;
+
+3.Perform `tokenization <tokenize.rst>`_ on segmented items;
+
+4.Use the existing or pre-trained model we provided to convert the tokenized items into vectors.
+
+
+Don't use the pre-trained model: call existing models directly
+------------------------------------
+
+You can use any pre-trained model provided by yourself (just give the storage path of the model) to convert the given question text into vectors.
+
+* Advantages: it is flexible to use your own model and its parameters can be adjusted freely.
+
+Import modules
+++++++++++
+
+::
+
+   from EduNLP.I2V import D2V,W2V,get_pretrained_i2v
+   from EduNLP.Vector import T2V,get_pretrained_t2v
+
+Models provided
+++++++++++++++++++++
+
+- W2V
+
+- D2V
+
+- T2V
+
+W2V
+<<<<<<<<<
+
+This model directly uses the relevant model methods in the gensim library to convert words into vectors. Currently, there are four methods:
+
+ - FastText
+
+ - Word2Vec
+
+ - KeyedVectors
+
+::
+
+   >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") # doctest: +ELLIPSIS
+   >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"])
+   >>> item_vector # doctest: +ELLIPSIS
+   array([[...]], dtype=float32)
+
+D2V
+<<<<<<<<<<<<
+
+This model is a comprehensive processing method which can convert items into vectors. Currently, the following methods are provided:
+
+- d2v: call doc2vec module in gensim library to convert items into vectors.
+
+- BowLoader: call corpora module in gensim library to convert docs into bows.
+
+- TfidfLoader: call TfidfModel module in gensim library to convert docs into bows.
+
+::
+
+   >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$"}
+   >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+   >>> i2v(item)
+   ([array([ 4.76559885e-02, -1.60574958e-01,  1.94614579e-03,  2.40295693e-01,
+   2.24517003e-01, -3.24351490e-02,  4.35789041e-02, -1.65670961e-02,...
+
+T2V
+<<<<<<<<<<
+
+You can use any pre-trained model provided by yourself to represent the segmentation sequences of a group of questions as vectors (just give the storage path of the model).
+
+- Advantages: the model and its parameters can be adjusted independently and has strong flexibility.
+
+Input
+^^^^^^^^^^
+
+Types: list
+Contents: the combination of each question segmentation sequences in one question group.
+>You can transfer question text (`str` type) to tokens using ``GensimWordTokenizer`` model
+
+::
+
+   >>> token_items=['公式','[FORMULA]','公式','[FORMULA]','如图','[FIGURE]','x',',','y','约束条件','[SEP]','z','=','x','+','7','y','最大值','[MARK]']
+   >>> path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+   >>> t2v = T2V('d2v',filepath=path)
+   >>> t2v(token_items)
+   [array([ 0.0256574 ,  0.06061139, -0.00121044, -0.0167674 , -0.0111706 ,
+   0.05325712, -0.02097339, -0.01613594,  0.02904145,  0.0185046 ,...
+
+Specific process of processing
+++++++++++++++++++++
+
+1.Call get_tokenizer function to get the result after word segmentation;
+
+2.Select the model provided for vectorization depending on the model used.
+
+
+Use the pre-training model: call get_pretrained_i2v directly
+---------------------------------------------
+
+Use the pre-training model provided by EduNLP to convert the given question text into vectors.
+
+* Advantages: Simple and convenient.
+
+* Disadvantages: Only the model given in the project can be used, which has great limitations.
+
+* Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
+
+Selection and Use of Models
+##################
+
+Select the pre-training model according to the subject:
+
++--------------------+------------------------+
+|   Pre-training model name  | Subject of model training data |
++====================+========================+
+|    d2v_all_256     |        all subject          |
++--------------------+------------------------+
+|    d2v_sci_256     |         Science           |
++--------------------+------------------------+
+|    d2v_lit_256     |         Arts           |
++--------------------+------------------------+
+|    d2v_eng_256     |         English           |
++--------------------+------------------------+
+
+
+The concrete process of processing
+##################
+
+1.Download the corresponding preprocessing model
+
+2.Transfer the obtained model to D2V and process it with D2V
+  Convert the obtained model into D2V and process it through D2V
+
+Examples:
+
+::
+
+  >>> i2v = get_pretrained_i2v("d2v_sci_256")
+  >>> i2v(item)
diff --git a/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst b/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
new file mode 100644
index 00000000..41dcab64
--- /dev/null
+++ b/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
@@ -0,0 +1,42 @@
+Use the pre-training model: call get_pretrained_i2v directly
+---------------------------------------------
+
+Use the pre-training model provided by EduNLP to convert the given question text into vectors.
+
+* Advantages: Simple and convenient.
+
+* Disadvantages: Only the model given in the project can be used, which has great limitations.
+
+* Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
+
+Selection and use of models
+##################
+
+Select the pre-training model according to the subject:
+
++--------------------+------------------------+
+|   Pre-training model name  | Subject of model training data |
++====================+========================+
+|    d2v_all_256     |        all subject          |
++--------------------+------------------------+
+|    d2v_sci_256     |         Science           |
++--------------------+------------------------+
+|    d2v_lit_256     |         Arts           |
++--------------------+------------------------+
+|    d2v_eng_256     |         English           |
++--------------------+------------------------+
+
+The concrete process of processing
+##################
+
+1.Download the corresponding preprocessing model
+
+2.Transfer the obtained model to D2V and process it with D2V
+  Convert the obtained model into D2V and process it through D2V
+
+Examples：
+
+::
+
+  >>> i2v = get_pretrained_i2v("d2v_sci_256")
+  >>> i2v(item)
diff --git a/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst b/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
new file mode 100644
index 00000000..2989f8ba
--- /dev/null
+++ b/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
@@ -0,0 +1,21 @@
+Don't use the pre-trained model: call existing models directly
+------------------------------------
+
+You can use any pre-trained model provided by yourself (just give the storage path of the model) to convert the given question text into vectors.
+
+* Advantages: it is flexible to use your own model and its parameters can be adjusted freely.
+
+Specific process of processing
+++++++++++++++++++++
+
+1.Call get_tokenizer function to get the result after word segmentation;
+
+2.Select the model provided for vectorization depending on the model used.
+
+Examples：
+
+::
+
+  >>> model_path = "../test_model/test_gensim_luna_stem_tf_d2v_256.bin"
+  >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+  >>> i2v(item)

From 3b76b5f80d46e2e9b0eb8e5667d800808b901637 Mon Sep 17 00:00:00 2001
From: fannazya <bfsudcs_fanzy@163.com>
Date: Mon, 25 Oct 2021 22:03:35 +0800
Subject: [PATCH 147/149] update English version

---
 docs/source/_static/new_flow.png              | Bin 0 -> 13843 bytes
 docs/source/index.rst                         |   5 +++
 docs/source/tutorial/en/index.rst             |  12 +++----
 docs/source/tutorial/en/parse.rst             |  33 +++++++++---------
 .../parse/FormulaSyntaxStructureParsing.rst   |  14 ++++----
 .../en/parse/TextSyntaxStructureParsing.rst   |   8 ++---
 docs/source/tutorial/en/pretrain.rst          |  22 ++++++------
 docs/source/tutorial/en/pretrain/loading.rst  |   2 +-
 docs/source/tutorial/en/pretrain/pub.rst      |  14 ++++----
 docs/source/tutorial/en/pretrain/start.rst    |   2 +-
 docs/source/tutorial/en/seg.rst               |  14 ++++----
 .../en/seg/SemanticComponentSegmentation.rst  |   4 +--
 .../seg/StructuralComponentSegmentation.rst   |   4 +--
 docs/source/tutorial/en/sif.rst               |  12 +++----
 .../en/tokenization/PureTextTokenizer.rst     |   2 +-
 docs/source/tutorial/en/tokenize.rst          |   9 ++---
 .../en/tokenize/Sentence Segmentation.rst     |   2 +-
 .../tutorial/en/tokenize/Tokenization.rst     |   2 +-
 .../tutorial/en/tokenize/WordSegmentation.rst |   2 +-
 docs/source/tutorial/en/vectorization.rst     |  12 +++----
 .../en/vectorization/WithPre-trainedModel.rst |   6 ++--
 .../vectorization/WithoutPre-trainedModel.rst |   4 +--
 22 files changed, 96 insertions(+), 89 deletions(-)
 create mode 100644 docs/source/_static/new_flow.png

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diff --git a/docs/source/index.rst b/docs/source/index.rst
index 27e9ed8e..83dcb4fd 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -143,6 +143,11 @@ If this repository is helpful for you, please cite our work
 
    tutorial/en/index
    tutorial/en/sif
+   tutorial/en/parse
+   tutorial/en/seg
+   tutorial/en/tokenize
+   tutorial/en/pretrain
+   tutorial/en/vectorization
 
 .. toctree::
    :maxdepth: 1
diff --git a/docs/source/tutorial/en/index.rst b/docs/source/tutorial/en/index.rst
index 4c8cc040..e75e48f1 100644
--- a/docs/source/tutorial/en/index.rst
+++ b/docs/source/tutorial/en/index.rst
@@ -1,5 +1,5 @@
 Get Started
-=====
+===============
 
 *  `Standard Item Format <sif.rst>`_
 
@@ -14,7 +14,7 @@ Get Started
 *  `Vectorization <vectorization.rst>`_
 
 Main process
-----------
+---------------
 
 .. figure:: ../../_static/new_flow.png
 
@@ -23,15 +23,15 @@ Main process
 * `Component Segmentation <seg.rst>`_ : Its function is to segment items in SIF format according to the types of items, so as to serve the later tokenization module.(that is, elements in different types can be tokenized using their corresponding methods)。
 
 * `Tokenization <tokenize.rst>`_: Its function is to tokenize segmented items, so as to serve the later tokenization module.
-  Generally, the tokenization method in the text form can be used directly. For formulas, the ast method can also be used for parsing(call the formula module)；
+  Generally, the tokenization method in the text form can be used directly. For formulas, the ast method can also be used for parsing(call the formula module).
 
 * `Vectorization <vectorization.rst>`_: This part mainly calls I2V class and its subclasses. Its function is to vectorize the list of tokenized items, so as to get the corresponding static vectors.
-  For vectorization module, You can call your own trained model or directly call the provided pre-training model(call get_ pretrained_ I2V module).
+  For vectorization module, You can call your own trained model or directly call the provided pre-training model(call get_pretrained_I2V module).
 
-* **Downstream Model**：Process the obtained vectors to get the desired results。
+* **Downstream Model**：Process the obtained vectors to get the desired results.
 
 Examples
---------
+---------
 
 To help you quickly understand the functions of this project, this section only shows the usages of common function interface. Intermediate function modules (such as parse, formula, segment, etc.) and more subdivided interface methods are not shown. For further study, please refer to relevant documents.
 
diff --git a/docs/source/tutorial/en/parse.rst b/docs/source/tutorial/en/parse.rst
index 69608c5e..22542da2 100644
--- a/docs/source/tutorial/en/parse.rst
+++ b/docs/source/tutorial/en/parse.rst
@@ -1,7 +1,8 @@
 Syntax Parsing
-=========
+=================
 
 In educational resources, texts and formulas have internal implicit or explicit syntax structures. It is of great benefit for further processing to extract these structures.
+
 * Text syntax structure parsing
 
 * Formula syntax structure parsing
@@ -13,23 +14,23 @@ The purpose is as follows:
 2. Determine whether the current item is legal and report the error type.
 
 Specific processing content
---------------------
+--------------------------------
 
 1.Its function is to match alphabets and numbers other than formulas. Only the alphabets and numbers between two Chinese characters will be corrected, and the rest of the cases are regarded as formulas that do not conform to latex syntax.
 
 2.Match brackets like "( )" (both English format and Chinese format), that is, brackets with no content or spaces, which should be replaced with ``$\\SIFChoice$``
 
-3.Match continuous underscores or underscores with spaces to replace them with ``$\\SIFBlank$``.
+3.Match continuous underscores or underscores with spaces and replace them with ``$\\SIFBlank$``.
 
 4.Match latex formulas，check the completeness and analyzability of latex formulas, and report an error for illegal formula.
 
 Formula syntax structure parsing
---------------------
+-------------------------------------
 
-This section is mainly realized by EduNLP.Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
+This section is mainly realized by EduNLP. Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
 
 Introduction of Main Introduction
-+++++++++++++++
++++++++++++++++++++++++++++++++++++++++
 
 1.Formula: determine whether the single formula passed in is in str form. If so, use the ast method for processing, otherwise an error will be reported. In addition, parameter variable_standardization is given. If this parameter is true, the variable standardization method will be used to make sure the same variable has the same variable number.
 
@@ -40,10 +41,10 @@ Formula
 
 Formula: firstly, in the word segmentation function, the formula of the original text is segmented. In addition, ``Formula parse tree`` function is provided, which can represent the abstract syntax analysis tree of mathematical formula in the form of text or picture.
 
-This module also provides the function of formula variable standardization, such as determining that 'x' in several sub formulas is the same variable.
+This module also provides the function of formula variable standardization, such as determining whether 'x' in several sub formulas is the same variable.
 
 Call the library
-+++++++++
++++++++++++++++++++++
 
 ::
 
@@ -52,7 +53,7 @@ Call the library
    from EduNLP.Formula.viz import ForestPlotter
 
 Initialization
-+++++++++
++++++++++++++++
 
 Incoming parameters: item
 
@@ -65,7 +66,7 @@ Item is the latex formula or the abstract syntax parse tree generated after the
    <Formula: x^2 + x+1 = y>
 
 View the specific content after formula segmentation
-++++++++++++++++++++++++++++
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 - View node elements after formula segmentation
 
@@ -123,7 +124,7 @@ View the specific content after formula segmentation
 
 
 Variable standardization
-+++++++++++
++++++++++++++++++++++++++++++
 
 This parameter makes the same variable have the same variable number.
 
@@ -209,14 +210,14 @@ Call ``FormulaGroup`` class to parse the equations. The related attributes and f
 
 
 Text syntax structure parsing
---------------------
+------------------------------------
 
 This section is mainly realized by EduNLP.SIF.Parse module. Its main function is to extract letters and numbers in the text and convert them into standard format.
 
-This module is mainly used as an *middle module* to parse the input text. Users generally do not call this module directly.
+This module is mainly used as an *middle module* to parse the input text. In general, users do not call this module directly.
 
 Introduction of main content
-+++++++++++++++
++++++++++++++++++++++++++++++++++++
 
 1. Judge the type of the incoming text in the following order
 
@@ -233,7 +234,7 @@ Introduction of main content
 * Use _is_formula_legal function, check the completeness and analyzability of latex formula, and report an error for formulas that do not conform to latex syntax.
 
 Call the library
->>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>
 
 ::
 
@@ -263,7 +264,7 @@ Parsing
    >>> text_parser3 = Parser(text3)
    >>> text_parser4 = Parser(text4)
 
-Related parameters description(?)
+Related parameters description
 >>>>>>>>>>>>
 
 - Try to convert text to standard format
diff --git a/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst b/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
index 2fc479c5..c09da64b 100644
--- a/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
+++ b/docs/source/tutorial/en/parse/FormulaSyntaxStructureParsing.rst
@@ -1,10 +1,10 @@
 Formula syntax structure parsing
---------------------
+----------------------------------
 
-This section is mainly realized by EduNLP.Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
+This section is mainly realized by EduNLP. Formula modules, which can determine if the text has syntax errors and convert the syntax formula into the form of ast tree. In practice, this module is often used as part of an intermediate process, and the relevant parameters of this module can be automatically chosen by calling the corresponding model, so it generally does not need special attention.
 
 Introduction of Main Content
-+++++++++++++++
++++++++++++++++++++++++++++++++++++++
 
 1.Formula: determine whether the single formula passed in is in str form. If so, use the ast method for processing, otherwise an error will be reported. In addition, parameter variable_standardization is given. If this parameter is true, the variable standardization method will be used to make sure the same variable has the same variable number.
 
@@ -15,10 +15,10 @@ Formula
 
 Formula: firstly, in the word segmentation function, the formula of the original text is segmented. In addition, ``Formula parse tree`` function is provided, which can represent the abstract syntax analysis tree of mathematical formula in the form of text or picture.
 
-This module also provides the function of formula variable standardization, such as determining that 'x' in several sub formulas is the same variable.
+This module also provides the function of formula variable standardization, such as determining  whether 'x' in several sub formulas is the same variable.
 
 Initialization
-+++++++++
+++++++++++++++++++++
 
 Incoming parameters: item
 
@@ -31,7 +31,7 @@ Item is the latex formula or the abstract syntax parse tree generated after the
    <Formula: x^2 + x+1 = y>
 
 View the specific content after formula segmentation
-++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 - View node elements after formula segmentation
 
@@ -87,7 +87,7 @@ View the specific content after formula segmentation
 .. figure:: ../../../_static/formula.png
 
 Variable Standardization
-+++++++++++
++++++++++++++++++++++++++++++++++
 
 This parameter makes the same variable have the same variable number.
 
diff --git a/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst b/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
index bdfe6848..6822c961 100644
--- a/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
+++ b/docs/source/tutorial/en/parse/TextSyntaxStructureParsing.rst
@@ -1,12 +1,12 @@
 Text syntax structure parsing
---------------------
+--------------------------------
 
 This section is mainly realized by EduNLP.SIF.Parse module. Its main function is to extract letters and numbers in the text and convert them into standard format.
 
-This module is mainly used as an *middle module* to parse the input text. Users generally do not call this module directly.
+This module is mainly used as an *middle module* to parse the input text. In general, users do not call this module directly.
 
 Introduction of Main Content
-+++++++++++++++
++++++++++++++++++++++++++++++++++++++
 
 1. Judge the type of the incoming text in the following order
 
@@ -47,7 +47,7 @@ Parsing
    >>> text_parser4 = Parser(text4)
 
 Related parameters description(?)
->>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 - Try to convert text to standard format
 
diff --git a/docs/source/tutorial/en/pretrain.rst b/docs/source/tutorial/en/pretrain.rst
index 9319b87d..985cffde 100644
--- a/docs/source/tutorial/en/pretrain.rst
+++ b/docs/source/tutorial/en/pretrain.rst
@@ -1,5 +1,5 @@
 Pre-training
-=======
+==============
 
 In the field of NLP, Pre-trained Language Models has become a very important basic technology.
 In this chapter, we will introduce the pre training tools in EduNLP:
@@ -9,7 +9,7 @@ In this chapter, we will introduce the pre training tools in EduNLP:
 * Public pre-trained models
 
 Import modules
-----------
+---------------
 
 ::
 
@@ -17,7 +17,7 @@ Import modules
    from EduNLP.Vector import get_pretrained_t2v
 
 Train the Model
-------------
+------------------
 
 Call train_Vector function interface directly to make the training model easier. This section calls the relevant training models in the gensim library. At present, the training methods of "sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf" are provided. Parameter embedding_dim is also provided for users to determine vector dimension according to their needs.
 
@@ -43,7 +43,7 @@ Examples：
 
 
 Load models
---------
+----------------
 
 Transfer the obtained model to the I2V module to load the model.
  
@@ -55,10 +55,10 @@ Examples：
    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
 
 The overview of our public model
-------------
+------------------------------------
 
 Version description
-##################
+#######################
 
 First level version:
 
@@ -76,7 +76,7 @@ Third level version【to be finished】:
 * Use third-party initializers
 
 Description of train data in models
-##################
+##############################################
 
 * Currently, the data used in w2v and d2v models are the subjects of senior high school.
 * test data:`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
@@ -86,7 +86,7 @@ At present, the following models are provided. More models of different subjects
 
 
 Examples of Model Training
-------------
+------------------------------------
 
 Get the dataset
 ####################
@@ -98,7 +98,7 @@ Get the dataset
    prepare_dataset  <../../build/blitz/pretrain/prepare_dataset.ipynb>
 
 An example of d2v in gensim model
-####################
+##################################
 
 .. toctree::
    :maxdepth: 1
@@ -109,7 +109,7 @@ An example of d2v in gensim model
    d2v_stem_tf  <../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
 
 An example of w2v in gensim model
-####################
+##################################
 
 .. toctree::
    :maxdepth: 1
@@ -119,7 +119,7 @@ An example of w2v in gensim model
    w2v_stem_tf  <../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
 
 An example of seg_token
-####################
+#############################
 
 .. toctree::
    :maxdepth: 1
diff --git a/docs/source/tutorial/en/pretrain/loading.rst b/docs/source/tutorial/en/pretrain/loading.rst
index 31fa3ea8..83b54c39 100644
--- a/docs/source/tutorial/en/pretrain/loading.rst
+++ b/docs/source/tutorial/en/pretrain/loading.rst
@@ -1,5 +1,5 @@
 Load models
---------
+----------------
 
 Transfer the obtained model to the I2V module to load the model.
  
diff --git a/docs/source/tutorial/en/pretrain/pub.rst b/docs/source/tutorial/en/pretrain/pub.rst
index 34407745..60077309 100644
--- a/docs/source/tutorial/en/pretrain/pub.rst
+++ b/docs/source/tutorial/en/pretrain/pub.rst
@@ -1,9 +1,9 @@
 The overview of our public model
-------------
+------------------------------------
 
 
 Version Description
-##################
+#########################
 
 First level version:
 
@@ -21,7 +21,7 @@ Third level version【to be finished】:
 * Use third-party initializers
 
 Description of train data in models
-##################
+#######################################
 
 * Currently, the data used in w2v and d2v models are the subjects of senior high school.
 * test data:`[OpenLUNA.json] <http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json>`_
@@ -30,7 +30,7 @@ At present, the following models are provided. More models of different subjects
     "d2v_all_256" (all subject), "d2v_sci_256" (Science), "d2v_eng_256" (English)，"d2v_lit_256" (Arts)
 
 Examples of model training
-------------
+----------------------------
 
 Get the dataset
 ####################
@@ -42,7 +42,7 @@ Get the dataset
    prepare_dataset  <../../../build/blitz/pretrain/prepare_dataset.ipynb>
 
 An example of d2v in gensim model
-####################
+####################################
 
 .. toctree::
    :maxdepth: 1
@@ -53,7 +53,7 @@ An example of d2v in gensim model
    d2v_stem_tf  <../../../build/blitz/pretrain/gensim/d2v_stem_tf.ipynb>
 
 An example of w2v in gensim model
-####################
+####################################
 
 .. toctree::
    :maxdepth: 1
@@ -63,7 +63,7 @@ An example of w2v in gensim model
    w2v_stem_tf  <../../../build/blitz/pretrain/gensim/w2v_stem_tf.ipynb>
 
 An example of seg_token
-####################
+############################
 
 .. toctree::
    :maxdepth: 1
diff --git a/docs/source/tutorial/en/pretrain/start.rst b/docs/source/tutorial/en/pretrain/start.rst
index 9c5bc241..4aa91619 100644
--- a/docs/source/tutorial/en/pretrain/start.rst
+++ b/docs/source/tutorial/en/pretrain/start.rst
@@ -1,5 +1,5 @@
 Train the model
-------------
+------------------
 
 Call train_Vector function interface directly to make the training model easier. This section calls the relevant training models in the gensim library. At present, the training methods of "sg"、 "cbow"、 "fastext"、 "d2v"、 "bow"、 "tfidf" are provided. Parameter embedding_dim is also provided for users to determine vector dimension according to their needs.
 
diff --git a/docs/source/tutorial/en/seg.rst b/docs/source/tutorial/en/seg.rst
index ad2696a2..4e2f2d39 100644
--- a/docs/source/tutorial/en/seg.rst
+++ b/docs/source/tutorial/en/seg.rst
@@ -1,26 +1,26 @@
 Component Segmentation
-=========
+=========================
 
-Educational resource is a kind of multimodal data, including data such as text, picture, formula and so on.
+Educational resource is a kind of multimodal data, including data such as text, pictures, formulas and so on.
 At the same time, it may also contain different components semantically, such as question stems, options, etc. Therefore, we first need to identify and segment the different components of educational resources:
 
 * Semantic Component Segmentation
 * Structural Component Segmentation
 
 Main Processing Contents
---------------------
+---------------------------
 
 1. Convert multiple-choice questions in the form of dict to qualified item by `Syntax parsing <parse.rst>`_;
 
 2. The input items are segmented and grouped according to the element type.
 
 Semantic Component Segmentation
-------------
+---------------------------------
 
 Because multiple-choice questions are given in the form of dict, it is necessary to convert them into text format while retaining their data relationship. This function can be realized by dict2str4sif function which can convert multiple-choice question items into character format and identify question stem and options。
 
 Import Modules
-+++++++++
++++++++++++++++++++++++
 
 ::
 
@@ -39,7 +39,7 @@ Basic Usage
  '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
 
 Optional additional parameters / interfaces
-++++++++++++++++++++++
+++++++++++++++++++++++++++++++++++++++++++++++++++
 
 1.add_list_no_tag: if this parameter is true, it means that you need to count the labels in the options section.
 
@@ -69,7 +69,7 @@ Optional additional parameters / interfaces
  '若复数$z=1+2 i+i^{3}$，则$|z|=$0$\\SIFSep$1$\\SIFSep$$\\sqrt{2}$$\\SIFSep$2'
 
 Structural Component Segmentation
-------------
+------------------------------------------
 
 This step is to segment sliced items. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
 
diff --git a/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst b/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
index 3901f4cb..c6535941 100644
--- a/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
+++ b/docs/source/tutorial/en/seg/SemanticComponentSegmentation.rst
@@ -1,5 +1,5 @@
 Semantic Component Segmentation
-------------
+------------------------------------
 
 Because multiple-choice questions are given in the form of dict, it is necessary to convert them into text format while retaining their data relationship. This function can be realized by dict2str4sif function which can convert multiple-choice question items into character format and identify question stem and options。
 
@@ -17,7 +17,7 @@ Basic Usage
  '$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$$\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$$\\SIFTag{list_3}$2$\\SIFTag{options_end}$'
 
 Optional additional parameters / interfaces
-++++++++++++++++++++++
+++++++++++++++++++++++++++++++++++++++++++++++++
 
 1.add_list_no_tag: if this parameter is true, it means that you need to count the labels in the options section.
 
diff --git a/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst b/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
index 8661c3d6..f5c44f7e 100644
--- a/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
+++ b/docs/source/tutorial/en/seg/StructuralComponentSegmentation.rst
@@ -1,5 +1,5 @@
 Structural Component Segmentation
-------------
+------------------------------------
 
 This step is to segment sliced items. In this step, there is a depth option. You can select all positions or some labels for segmentation according to your needs, such as \SIFSep and \SIFTag. You can also select where to add labels, either at the head and tail or only at the head or tail.
 
@@ -20,7 +20,7 @@ Basic Usage
  >>> ['如图所示，则', '\\bigtriangleup ABC', '的面积是', '\\SIFBlank', '。', \FigureID{1}]
 
 Optional additional parameters/interfaces
-++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++
 
 1.describe: count the number of elements of different types
 
diff --git a/docs/source/tutorial/en/sif.rst b/docs/source/tutorial/en/sif.rst
index 877cb503..be5c895d 100644
--- a/docs/source/tutorial/en/sif.rst
+++ b/docs/source/tutorial/en/sif.rst
@@ -1,12 +1,12 @@
 Standard Item Format
-===============
+=======================
 
 version: 0.2
 
 For the convenience of follow-up research and use, we need a unified test question grammar standard.
 
 Grammar Rules
------------
+----------------
 
 1. Only Chinese characters, Chinese and English punctuation and line breaks are allowed in the question text.
 
@@ -16,7 +16,7 @@ Grammar Rules
 
 4. Text format description: we represent text in different styles with ``$\textf{item,CHAR_EN}$``. Currently, we have defined some styles: b-bold, i-italic, u-underline, w-wave, d-dotted, t-title. CHAR_EN Labels can be mixed and sorted alphabetically. An example: $\textf{EduNLP, b}$ looks **EduNLP**
 
-5. Other mathematical symbols like English letters, Roman characters and numbers need to be expressed in latex format, that is, embedded in `$$`.
+5. Other mathematical symbols like English letters, Roman characters and numbers need to be expressed in latex format, that is, embedded in ``$$`` .
 
 6. For the entry standard of molecular formula, please refer to `INCHI <https://zh.wikipedia.org/wiki/%E5%9B%BD%E9%99%85%E5%8C%96%E5%90%88%E7%89%A9%E6%A0%87%E8%AF%86>`_ for the time being.
 
@@ -46,11 +46,11 @@ Tips
 
 3. Choices and blanks.
 
-4. A single number or letter is also required to be between `$$` (automatic verification could already realize it).
+4. A single number or letter is also required to be between ``$$`` (automatic verification could already realize it).
 
 5. Try to make sure Chinese is not included in the latex formula such as ``\text{CHAR_CH}``.
 
-6. When importing data using MySQL database, an ``\`` is automatically ignored which needs to be further processed as``\\``.
+6. When importing data using MySQL database, an ``\`` is automatically ignored which needs to be further processed as ``\\``.
 
 Examples
 -----------------
@@ -89,7 +89,7 @@ Non-standard Format:
     ``则$a$的取值范围是（\u3000\u3000）``
 
 Functions for judging whether text is in SIF format and converting to SIF format
---------------------------------------------
+--------------------------------------------------------------------------------------------------
 
 Call the Library
 ++++++++
diff --git a/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst b/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
index 8c36e67c..88ec975e 100644
--- a/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
+++ b/docs/source/tutorial/en/tokenization/PureTextTokenizer.rst
@@ -1,5 +1,5 @@
 PureTextTokenizer
-================
+===================
 
 By default, the pictures, labels, separators, blanks in the question text and other parts of the incoming item are converted into special characters for data security. At the same time, special formulas such as $\\FormFigureID{...}$ and $\\FormFigureBase64{...}$ are screened out to facilitate the tokenization of text and plain text formulas. Also, the tokenizer uses linear analysis method for text and formulas, and the ``key`` parameter provided is used to preprocess the incoming item, which will be improved based on users' requirements in the future.
 
diff --git a/docs/source/tutorial/en/tokenize.rst b/docs/source/tutorial/en/tokenize.rst
index 3411b74b..f6350614 100644
--- a/docs/source/tutorial/en/tokenize.rst
+++ b/docs/source/tutorial/en/tokenize.rst
@@ -1,5 +1,5 @@
 Tokenization
-=======
+==============
 
 Tokenization, known as word segmentation and sentence segmentation, is a basic but very important step in the field of NLP.
 In EduNLP, we divided Tokenization into different levels according to different granularity. To avoid ambiguity, we define as follows:
@@ -15,7 +15,7 @@ This module provides tokenization function of question text, converting question
 There are two modes: one is linear mode, which is used for text processing (word segmentation using jieba library). The other one is ast mode, which is used to parse the formula.
 
 Word Segmentation
--------
+---------------------
 
 Text-tokenization: A sentence (without formulas) consists of several "words" in order. The process of dividing a sentence into several words is called "Text-tokenization". According to the granularity of "words", it can be subdivided into "Word-tokenization" and "Char-tokenization".
 
@@ -52,12 +52,13 @@ Examples:
    ['三', '角', '函', '数', '基', '初', '函', '数']
     
 Sentence Segmentation
--------
+----------------------------
 
 During the process of sentence segmentation, a long document is divided into several sentences. Each sentence is a "token" (to be realized).
 
 Tokenization
--------
+--------------
+
 Tokenization is comprehensive analysis. In this process, sentences with formulas are segmented into several markers. Each marker is a "token".
 
 The implementation of this function is tokenize function. The required results can be obtained by passing in items after Structural Component Segmentation.
diff --git a/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst b/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst
index 1a8d4950..902b2bb5 100644
--- a/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst	
+++ b/docs/source/tutorial/en/tokenize/Sentence Segmentation.rst	
@@ -1,3 +1,3 @@
 Sentence Segmentation
--------
+-------------------------
 During the process of sentence segmentation, a long document is divided into several sentences. Each sentence is a "token" (to be realized).
diff --git a/docs/source/tutorial/en/tokenize/Tokenization.rst b/docs/source/tutorial/en/tokenize/Tokenization.rst
index fad25912..c955602b 100644
--- a/docs/source/tutorial/en/tokenize/Tokenization.rst
+++ b/docs/source/tutorial/en/tokenize/Tokenization.rst
@@ -1,5 +1,5 @@
 Tokenization
--------
+--------------
 Tokenization is comprehensive analysis. In this process, sentences with formulas are segmented into several markers. Each marker is a "token".
 We provide some encapsulated tokenizers for users to call them conveniently. The following is a complete list of tokenizers.
 
diff --git a/docs/source/tutorial/en/tokenize/WordSegmentation.rst b/docs/source/tutorial/en/tokenize/WordSegmentation.rst
index a85f4dae..181f0b80 100644
--- a/docs/source/tutorial/en/tokenize/WordSegmentation.rst
+++ b/docs/source/tutorial/en/tokenize/WordSegmentation.rst
@@ -1,5 +1,5 @@
 Word segmentation
--------
+---------------------
 
 Text-tokenization: A sentence (without formulas) consists of several "words" in order. The process of dividing a sentence into several words is called "Text-tokenization". According to the granularity of "words", it can be subdivided into "Word-tokenization" and "Char-tokenization".
 
diff --git a/docs/source/tutorial/en/vectorization.rst b/docs/source/tutorial/en/vectorization.rst
index 5b744eeb..e48fe730 100644
--- a/docs/source/tutorial/en/vectorization.rst
+++ b/docs/source/tutorial/en/vectorization.rst
@@ -1,5 +1,5 @@
 Vectorization
-=========
+==================
 
 This section provides a simple interface to convert the incoming items into vectors directly. Currently, the option of whether to use the pre training model is provided. You can choose according to your needs. If you don't want to use the pre-trained model, you can call D2V directly, or call get_pretrained_i2v function if you want to use the pre-trained model.
 
@@ -20,14 +20,14 @@ Overview Flow
 
 
 Don't use the pre-trained model: call existing models directly
-------------------------------------
+--------------------------------------------------------------------------
 
 You can use any pre-trained model provided by yourself (just give the storage path of the model) to convert the given question text into vectors.
 
 * Advantages: it is flexible to use your own model and its parameters can be adjusted freely.
 
 Import modules
-++++++++++
++++++++++++++++++++++++
 
 ::
 
@@ -105,7 +105,7 @@ Contents: the combination of each question segmentation sequences in one questio
    0.05325712, -0.02097339, -0.01613594,  0.02904145,  0.0185046 ,...
 
 Specific process of processing
-++++++++++++++++++++
+++++++++++++++++++++++++++++++++++++++++
 
 1.Call get_tokenizer function to get the result after word segmentation;
 
@@ -124,7 +124,7 @@ Use the pre-training model provided by EduNLP to convert the given question text
 * Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
 
 Selection and Use of Models
-##################
+####################################
 
 Select the pre-training model according to the subject:
 
@@ -142,7 +142,7 @@ Select the pre-training model according to the subject:
 
 
 The concrete process of processing
-##################
+####################################
 
 1.Download the corresponding preprocessing model
 
diff --git a/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst b/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
index 41dcab64..844fdd3b 100644
--- a/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
+++ b/docs/source/tutorial/en/vectorization/WithPre-trainedModel.rst
@@ -1,5 +1,5 @@
 Use the pre-training model: call get_pretrained_i2v directly
----------------------------------------------
+--------------------------------------------------------------------
 
 Use the pre-training model provided by EduNLP to convert the given question text into vectors.
 
@@ -10,7 +10,7 @@ Use the pre-training model provided by EduNLP to convert the given question text
 * Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
 
 Selection and use of models
-##################
+####################################
 
 Select the pre-training model according to the subject:
 
@@ -27,7 +27,7 @@ Select the pre-training model according to the subject:
 +--------------------+------------------------+
 
 The concrete process of processing
-##################
+####################################
 
 1.Download the corresponding preprocessing model
 
diff --git a/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst b/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
index 2989f8ba..62ce6155 100644
--- a/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
+++ b/docs/source/tutorial/en/vectorization/WithoutPre-trainedModel.rst
@@ -1,12 +1,12 @@
 Don't use the pre-trained model: call existing models directly
-------------------------------------
+----------------------------------------------------------------
 
 You can use any pre-trained model provided by yourself (just give the storage path of the model) to convert the given question text into vectors.
 
 * Advantages: it is flexible to use your own model and its parameters can be adjusted freely.
 
 Specific process of processing
-++++++++++++++++++++
++++++++++++++++++++++++++++++++++++
 
 1.Call get_tokenizer function to get the result after word segmentation;
 

From 3d9e4acf40456e2e04ebcc85acb9bc519571c79c Mon Sep 17 00:00:00 2001
From: fannazya <bfsudcs_fanzy@163.com>
Date: Sun, 7 Nov 2021 20:06:14 +0800
Subject: [PATCH 148/149] update English version tutorial

---
 docs/source/tutorial/en/parse.rst         |  4 +-
 docs/source/tutorial/en/pretrain.rst      |  4 +-
 docs/source/tutorial/en/sif.rst           |  2 +-
 docs/source/tutorial/en/vectorization.rst | 89 ++++++++++++-----------
 docs/source/tutorial/zh/vectorization.rst | 86 +++++++++++-----------
 5 files changed, 93 insertions(+), 92 deletions(-)

diff --git a/docs/source/tutorial/en/parse.rst b/docs/source/tutorial/en/parse.rst
index 22542da2..5aba283d 100644
--- a/docs/source/tutorial/en/parse.rst
+++ b/docs/source/tutorial/en/parse.rst
@@ -43,7 +43,7 @@ Formula: firstly, in the word segmentation function, the formula of the original
 
 This module also provides the function of formula variable standardization, such as determining whether 'x' in several sub formulas is the same variable.
 
-Call the library
+Import modules
 +++++++++++++++++++++
 
 ::
@@ -233,7 +233,7 @@ Introduction of main content
  
 * Use _is_formula_legal function, check the completeness and analyzability of latex formula, and report an error for formulas that do not conform to latex syntax.
 
-Call the library
+Import modules
 >>>>>>>>>>>>>>>>>>>
 
 ::
diff --git a/docs/source/tutorial/en/pretrain.rst b/docs/source/tutorial/en/pretrain.rst
index 985cffde..58105f44 100644
--- a/docs/source/tutorial/en/pretrain.rst
+++ b/docs/source/tutorial/en/pretrain.rst
@@ -67,8 +67,8 @@ First level version:
 
 Second level version:
 
-* Minor subjects(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
-* Major subjects(science, arts and all subject)
+* Single subject(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
+* Multiple subject(science, arts and all subject)
 
 Third level version【to be finished】:
 
diff --git a/docs/source/tutorial/en/sif.rst b/docs/source/tutorial/en/sif.rst
index be5c895d..7650aae6 100644
--- a/docs/source/tutorial/en/sif.rst
+++ b/docs/source/tutorial/en/sif.rst
@@ -91,7 +91,7 @@ Non-standard Format:
 Functions for judging whether text is in SIF format and converting to SIF format
 --------------------------------------------------------------------------------------------------
 
-Call the Library
+Import modules
 ++++++++
 ::
 
diff --git a/docs/source/tutorial/en/vectorization.rst b/docs/source/tutorial/en/vectorization.rst
index e48fe730..eb59a34c 100644
--- a/docs/source/tutorial/en/vectorization.rst
+++ b/docs/source/tutorial/en/vectorization.rst
@@ -19,6 +19,51 @@ Overview Flow
 4.Use the existing or pre-trained model we provided to convert the tokenized items into vectors.
 
 
+Use the pre-training model: call get_pretrained_i2v directly
+---------------------------------------------
+
+Use the pre-training model provided by EduNLP to convert the given question text into vectors.
+
+* Advantages: Simple and convenient.
+
+* Disadvantages: Only the model given in the project can be used, which has great limitations.
+
+* Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
+
+Selection and Use of Models
+####################################
+
+Select the pre-training model according to the subject:
+
++--------------------+------------------------+
+|   Pre-training model name  | Subject of model training data |
++====================+========================+
+|    d2v_all_256     |        all subject          |
++--------------------+------------------------+
+|    d2v_sci_256     |         Science           |
++--------------------+------------------------+
+|    d2v_lit_256     |         Arts           |
++--------------------+------------------------+
+|    d2v_eng_256     |         English           |
++--------------------+------------------------+
+
+
+The concrete process of processing
+####################################
+
+1.Download the corresponding preprocessing model
+
+2.Transfer the obtained model to D2V and process it with D2V
+  Convert the obtained model into D2V and process it through D2V
+
+Examples:
+
+::
+
+  >>> i2v = get_pretrained_i2v("d2v_sci_256")
+  >>> i2v(item)
+
+
 Don't use the pre-trained model: call existing models directly
 --------------------------------------------------------------------------
 
@@ -111,47 +156,3 @@ Specific process of processing
 
 2.Select the model provided for vectorization depending on the model used.
 
-
-Use the pre-training model: call get_pretrained_i2v directly
----------------------------------------------
-
-Use the pre-training model provided by EduNLP to convert the given question text into vectors.
-
-* Advantages: Simple and convenient.
-
-* Disadvantages: Only the model given in the project can be used, which has great limitations.
-
-* Call this function to obtain the corresponding pre-training model. At present, the following pre training models are provided: d2v_all_256, d2v_sci_256, d2v_eng_256 and d2v_lit_256.
-
-Selection and Use of Models
-####################################
-
-Select the pre-training model according to the subject:
-
-+--------------------+------------------------+
-|   Pre-training model name  | Subject of model training data |
-+====================+========================+
-|    d2v_all_256     |        all subject          |
-+--------------------+------------------------+
-|    d2v_sci_256     |         Science           |
-+--------------------+------------------------+
-|    d2v_lit_256     |         Arts           |
-+--------------------+------------------------+
-|    d2v_eng_256     |         English           |
-+--------------------+------------------------+
-
-
-The concrete process of processing
-####################################
-
-1.Download the corresponding preprocessing model
-
-2.Transfer the obtained model to D2V and process it with D2V
-  Convert the obtained model into D2V and process it through D2V
-
-Examples:
-
-::
-
-  >>> i2v = get_pretrained_i2v("d2v_sci_256")
-  >>> i2v(item)
diff --git a/docs/source/tutorial/zh/vectorization.rst b/docs/source/tutorial/zh/vectorization.rst
index 8c57cac7..aff364ff 100644
--- a/docs/source/tutorial/zh/vectorization.rst
+++ b/docs/source/tutorial/zh/vectorization.rst
@@ -19,6 +19,49 @@
 4.使用已有或者使用提供的预训练模型，将令牌化后的item转换为向量。
 
 
+使用预训练模型：直接调用get_pretrained_i2v
+---------------------------------------------
+
+使用 EduNLP 项目组给定的预训练模型将给定的题目文本转成向量。
+
+* 优点：简单方便。
+
+* 缺点：只能使用项目中给定的模型，局限性较大。
+
+* 调用此函数即可获得相应的预训练模型，目前提供以下的预训练模型：d2v_all_256、d2v_sci_256、d2v_eng_256、d2v_lit_256
+
+模型选择与使用
+##################
+
+根据题目所属学科选择预训练模型：
+
++--------------------+------------------------+
+|    预训练模型名称  | 模型训练数据的所属学科 |
++====================+========================+
+|    d2v_all_256     |        全学科          |
++--------------------+------------------------+
+|    d2v_sci_256     |         理科           |
++--------------------+------------------------+
+|    d2v_lit_256     |         文科           |
++--------------------+------------------------+
+|    d2v_eng_256     |         英语           |
++--------------------+------------------------+
+
+处理的具体流程
+##################
+
+1.下载相应的预处理模型
+
+2.将所得到的模型传入D2V，使用D2V进行处理
+
+Examples：
+
+::
+
+  >>> i2v = get_pretrained_i2v("d2v_sci_256")
+  >>> i2v(item)
+
+
 不使用预训练模型:直接调用已有模型
 ------------------------------------
 
@@ -110,46 +153,3 @@ T2V
 1.调用get_tokenizer函数，得到经过分词后的结果；
 
 2.根据使用的模型，选择提供的模型类型，进行向量化处理。
-
-
-使用预训练模型：直接调用get_pretrained_i2v
----------------------------------------------
-
-使用 EduNLP 项目组给定的预训练模型将给定的题目文本转成向量。
-
-* 优点：简单方便。
-
-* 缺点：只能使用项目中给定的模型，局限性较大。
-
-* 调用此函数即可获得相应的预训练模型，目前提供以下的预训练模型：d2v_all_256、d2v_sci_256、d2v_eng_256、d2v_lit_256
-
-模型选择与使用
-##################
-
-根据题目所属学科选择预训练模型：
-
-+--------------------+------------------------+
-|    预训练模型名称  | 模型训练数据的所属学科 |
-+====================+========================+
-|    d2v_all_256     |        全学科          |
-+--------------------+------------------------+
-|    d2v_sci_256     |         理科           |
-+--------------------+------------------------+
-|    d2v_lit_256     |         文科           |
-+--------------------+------------------------+
-|    d2v_eng_256     |         英语           |
-+--------------------+------------------------+
-
-处理的具体流程
-##################
-
-1.下载相应的预处理模型
-
-2.将所得到的模型传入D2V，使用D2V进行处理
-
-Examples：
-
-::
-
-  >>> i2v = get_pretrained_i2v("d2v_sci_256")
-  >>> i2v(item)

From 9071ccaad1691c715ba2451254d7b6acf2b46b3c Mon Sep 17 00:00:00 2001
From: KenelmQLH <46173279+KenelmQLH@users.noreply.github.com>
Date: Mon, 15 Nov 2021 16:04:07 +0800
Subject: [PATCH 149/149] [FEATURE] Fix OOV in word2vec (#105)

* [FEATURE] Fix oov in word2vec

* Update CHANGE.txt
---
 CHANGE.txt                  | 9 +++++++++
 EduNLP/Vector/gensim_vec.py | 3 ++-
 tests/test_vec/test_vec.py  | 1 +
 3 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/CHANGE.txt b/CHANGE.txt
index cd98345e..1a47033f 100644
--- a/CHANGE.txt
+++ b/CHANGE.txt
@@ -1,3 +1,12 @@
+v0.0.7:
+    1. add BERT and pretrained model (luna_bert)
+    2. speed up the process in sif
+    3. handling OOV in word2vec
+    4. add English tutorials
+    5. add api docs and prettify tutorials
+    6. fix the np.error in gensim_vec.W2V.infer_vector
+    7. fix the parameters lost in tokenization
+
 v0.0.6:
     1. dev: add half-pretrained rnn model
     2. important!!!: rename TextTokenizer to PureTextTokenizer, and add a new tokenizer named TextTokenizer (the two have similar but not the same behaviours).
diff --git a/EduNLP/Vector/gensim_vec.py b/EduNLP/Vector/gensim_vec.py
index 3ade74a5..ece25442 100644
--- a/EduNLP/Vector/gensim_vec.py
+++ b/EduNLP/Vector/gensim_vec.py
@@ -62,7 +62,8 @@ def __call__(self, *words):
             yield self[word]
 
     def __getitem__(self, item):
-        return self.wv[item] if item not in self.constants else np.zeros((self.vector_size,))
+        index = self.key_to_index(item)
+        return self.wv[item] if index not in self.constants.values() else np.zeros((self.vector_size,))
 
     def infer_vector(self, items, agg="mean", *args, **kwargs) -> np.ndarray:
         token_vectors = self.infer_tokens(items, *args, **kwargs)
diff --git a/tests/test_vec/test_vec.py b/tests/test_vec/test_vec.py
index bc7194d9..d6ae1a44 100644
--- a/tests/test_vec/test_vec.py
+++ b/tests/test_vec/test_vec.py
@@ -86,6 +86,7 @@ def test_w2v(stem_tokens, tmpdir, method, binary):
     assert w2v.vectors.shape == (len(w2v.wv.vectors) + len(w2v.constants), w2v.vector_size)
     assert w2v.key_to_index("[UNK]") == 0
     assert w2v.key_to_index("OOV") == 0
+    assert np.array_equal(w2v["OOV"], np.zeros((10,)))
 
     t2v = T2V("w2v", filepath=filepath, method=method, binary=binary)
     assert len(t2v(stem_tokens[:1])[0]) == t2v.vector_size