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/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/Formula/Formula.py b/EduNLP/Formula/Formula.py
index f4c868be..6eb80049 100644
--- a/EduNLP/Formula/Formula.py
+++ b/EduNLP/Formula/Formula.py
@@ -15,6 +15,18 @@
 
 class Formula(object):
     """
+    The part transform a formula to the parsed abstracted syntax tree.
+
+    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")
@@ -29,22 +41,21 @@ class Formula(object):
     <Formula: x>
     >>> 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):
-        """
-
-        Parameters
-        ----------
-        formula: str or List[Dict]
-            latex formula string or the parsed abstracted syntax tree
-        variable_standardization
-        const_mathord
-        init
-        args
-        kwargs
-        """
         self._formula = formula
         self._ast = None
         if init is True:
@@ -55,6 +66,15 @@ 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 {}
@@ -118,6 +138,26 @@ def resetable(self):
 
 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]
+        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"])
@@ -128,15 +168,16 @@ 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}, \
 {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}, \
 {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}]
-    """
 
+    """
     def __init__(self,
                  formula_list: (list, List[str], List[Formula]),
                  variable_standardization=False,
@@ -186,6 +227,15 @@ 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 +270,15 @@ 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/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
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..261bdefd 100644
--- a/EduNLP/I2V/i2v.py
+++ b/EduNLP/I2V/i2v.py
@@ -4,17 +4,22 @@
 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):
     """
+    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
@@ -24,18 +29,40 @@ 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
-    """
 
     def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrained_t2v=False, **kwargs):
+    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)
 
-        self.tokenizer: Tokenizer = get_tokenizer(tokenizer, **tokenizer_kwargs if tokenizer_kwargs is not None else {})
+    Returns
+    -------
+    i2v model: I2V
+    """
+    def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrained_t2v=False, **kwargs):
         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,
@@ -46,9 +73,11 @@ 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 +115,67 @@ def vector_size(self):
 
 
 class D2V(I2V):
+    """
+    The model aims to transfer item to vector directly.
+
+    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
+
+    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"
+    >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False)
+    >>> i2v(item)
+    ([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:
+        '''
+        It is a function to switch item to vector. And before using the function, it is nesseary to load model.
+
+        Parameters
+        -----------
+        items:str
+            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:list
+        '''
         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
@@ -98,8 +186,65 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs):
 
 
 class W2V(I2V):
+    """
+    The model aims to transfer tokens to vector.
+
+    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
+
+    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:
+        '''
+        It is a function to switch item to vector. And before using the function, it is nesseary to load model.
+
+        Parameters
+        -----------
+        items:str
+            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:list
+        '''
         tokens = self.tokenize(items, return_token=True) if tokenize is True else items
         tokens = [token for token in tokens]
         return self.t2v(tokens, *args, **kwargs), self.t2v.infer_tokens(tokens, *args, **kwargs)
@@ -109,6 +254,69 @@ 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):
+    """
+    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)
+
+    @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"],
@@ -116,21 +324,42 @@ 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"],
+    'luna_bert': [Bert, 'luna_bert'],
 }
 
 
 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
-    model_dir
+    -----------
+    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'
 
     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}$"}
+    >>> i2v = get_pretrained_i2v("test_d2v", "examples/test_model/data/d2v")
+    >>> print(i2v(item))
+    ([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..ce3a2d34 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
@@ -66,6 +80,20 @@ 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
+        --------
+        sequence
+            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..00ba5df9 100644
--- a/EduNLP/ModelZoo/utils/masker.py
+++ b/EduNLP/ModelZoo/utils/masker.py
@@ -7,8 +7,15 @@
 
 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)
@@ -29,17 +36,13 @@ class Masker(object):
     [['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
-        """
         self.seed = np.random.default_rng(seed)
         self.per = per
         self.mask = mask
diff --git a/EduNLP/ModelZoo/utils/padder.py b/EduNLP/ModelZoo/utils/padder.py
index ed86cfef..57f6219b 100644
--- a/EduNLP/ModelZoo/utils/padder.py
+++ b/EduNLP/ModelZoo/utils/padder.py
@@ -5,7 +5,8 @@
 
 
 class PadSequence(object):
-    """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.
@@ -17,24 +18,18 @@ class PadSequence(object):
     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:
@@ -59,6 +54,8 @@ def pad_sequence(sequence: list, max_length=None, pad_val=0, clip=True):
 
     Returns
     -------
+    Modified list:list
+        padding the sequence in the same size.
 
     Examples
     --------
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..40bdc16f
--- /dev/null
+++ b/EduNLP/Pretrain/bert_vec.py
@@ -0,0 +1,163 @@
+from EduNLP import logger
+import multiprocessing
+import transformers
+from EduNLP.Tokenizer import PureTextTokenizer
+from copy import deepcopy
+from typing import Optional, Union
+import itertools as it
+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
+
+
+__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']
+    >>> 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)
+        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})
+        self.pure_text_tokenizer = PureTextTokenizer()
+
+    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:
+            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: (list, str), *args, **kwargs):
+        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):
+    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):
+    """
+
+    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
+    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
+    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 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
+        epochs = 1
+        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
+    )
+
+    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,
+        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()
+    trainer.save_model(output_dir)
diff --git a/EduNLP/Pretrain/gensim_vec.py b/EduNLP/Pretrain/gensim_vec.py
index 17482b23..51d408ae 100644
--- a/EduNLP/Pretrain/gensim_vec.py
+++ b/EduNLP/Pretrain/gensim_vec.py
@@ -19,17 +19,24 @@ 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
     ----------
+    tokenizer: Tokenizer
 
     Examples
     ----------
@@ -76,9 +83,39 @@ 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 ;
+
+    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])
+    [['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 +154,7 @@ 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 +165,38 @@ 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
+        the text of question
+    w2v_prefix
+    embedding_dim:int
+        vector_size
+    method:str
+        the method of training,
+        e.g.: sg, cbow, fasttext, d2v, bow, tfidf
+    binary: model format
+        True:bin;
+        False:kv
+    train_params: dict
+        the training parameters passed to model
+
+    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])
+    [['公式'], [\\FormFigureID{wrong1?}], ['如图'], ['[FIGURE]'],...['最大值'], ['[MARK]']]
+    >>> 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(
         min_count=0,
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/EduNLP/SIF/parser/parser.py b/EduNLP/SIF/parser/parser.py
index db290946..b9b1e269 100644
--- a/EduNLP/SIF/parser/parser.py
+++ b/EduNLP/SIF/parser/parser.py
@@ -1,8 +1,21 @@
 from EduNLP.Formula.ast import str2ast, katex_parse
+import re
 
 
 class Parser:
-    def __init__(self, data):
+    """
+    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, check_formula=True):
         self.lookahead = 0
         self.head = 0
         self.text = data
@@ -13,6 +26,7 @@ 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('$\\SIFChoice$')
@@ -98,6 +112,17 @@ 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]
@@ -222,7 +247,7 @@ def get_token(self):
             while self.head < len(self.text) and self.text[self.head] != '$':
                 ch_informula = self.text[self.head]
                 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
@@ -230,8 +255,9 @@ 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
-            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/segment/segment.py b/EduNLP/SIF/segment/segment.py
index 93c5713f..39e2f869 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
@@ -76,6 +80,41 @@ 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
+        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
+    """
     def __init__(self, item, figures: dict = None):
         self._segments = []
         self._text_segments = []
@@ -119,6 +158,7 @@ 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)):
@@ -137,6 +177,7 @@ def append(self, segment) -> None:
 
     @property
     def segments(self):
+        """return segments"""
         if self._seg_idx is None:
             return self._segments
         else:
@@ -144,29 +185,37 @@ 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
         ----------
@@ -175,6 +224,8 @@ def symbolize(self, to_symbolize="fgm"):
             "f": formula
             "g": figure
             "m": question mark
+            "a": tag
+            "s": sep
 
         Returns
         -------
@@ -201,6 +252,16 @@ 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
+        ----------
+        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 +284,7 @@ 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),
@@ -233,6 +295,7 @@ 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
     ----------
@@ -242,6 +305,8 @@ def seg(item, figures=None, symbol=None):
 
     Returns
     -------
+    list
+        segmented item
 
     Examples
     --------
@@ -282,18 +347,18 @@ 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 af4fa63a..41518966 100644
--- a/EduNLP/SIF/sif.py
+++ b/EduNLP/SIF/sif.py
@@ -10,17 +10,31 @@
 __all__ = ["is_sif", "to_sif", "sif4sci"]
 
 
-def is_sif(item):
+def is_sif(item, check_formula=True, return_parser=False):
     r"""
+    the part aims to check whether the input is sif format
+
     Parameters
     ----------
-    item
+    item:str
+        a raw item which respects stem
+    check_formula: bool
+        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
+    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
 
     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 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);
 
     Examples
     --------
@@ -30,27 +44,38 @@ def is_sif(item):
     >>> 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
+    >>> ret = is_sif(text, return_parser=True)
+    >>> ret # doctest: +ELLIPSIS
+    (False, <EduNLP.SIF.parser.parser.Parser object...>)
     """
-    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
+    ret = True if item_parser.error_flag == 0 and item_parser.modify_flag == 0 else False
+    if return_parser is True:
+        return ret, item_parser
+    else:
+        return ret
 
 
-def to_sif(item):
+def to_sif(item, check_formula=True, parser: Parser = None):
     r"""
+    the part aims to switch item to sif formate
+
     Parameters
     ----------
-    item
+    items:str
+        a raw item which respects stem
+    check_formula: bool
+        whether to check the formulas when parsing item (only work when parser=None).
+    parser: Parser
+        the parser of item returned from is_sif.
 
     Returns
     -------
-    item
+    item:str
+        the item which accords with sif format
 
     Examples
     --------
@@ -58,14 +83,20 @@ def to_sif(item):
     >>> 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, return_parser=True)
+    >>> ret # doctest: +ELLIPSIS
+    (False, <EduNLP.SIF.parser.parser.Parser object...>)
+    >>> 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...
+
     """
-    item_parser = Parser(item)
-    item_parser.description_list()
-    item = item_parser.text
-    return item
+    if parser is not None:
+        return parser.text
+    else:
+        return is_sif(item, check_formula, return_parser=True)[1].text
 
 
-def sif4sci(item: str, figures: (dict, bool) = None, safe=True, 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"""
 
@@ -73,29 +104,58 @@ def sif4sci(item: str, figures: (dict, bool) = None, safe=True, symbol: str = No
 
     Parameters
     ----------
-    item
-    figures
-    safe
-    symbol
-    tokenization
+    item:str
+        a raw item which respects stem
+    figures:dict
+        when it is a dict, it means the id-to-instance for figures in 'FormFigureID{...}' format,
+        when it is a bool, it means whether to instantiate figures in 'FormFigureBase64{...}' format
+
+    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
+        select the methods to symbolize:
+            "t": text
+            "f": formula
+            "g": figure
+            "m": question mark
+            "a": tag
+            "s": sep
+
+    tokenization: bool
+        whether to tokenize item after segmentation
+
     tokenization_params:
-        method: which tokenizer to be used, "linear" or "ast"
-        The parameters only useful for "linear":
+        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
 
-        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
     -------
-    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
     --------
@@ -189,8 +249,15 @@ 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 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, parser=item_parser)
+        elif mode != 0:
+            raise KeyError(
+                "Unknown mode %s, use only 0 or 1 or 2." % mode
+            )
 
         ret = seg(item, figures, symbol)
 
diff --git a/EduNLP/SIF/tokenization/formula/ast_token.py b/EduNLP/SIF/tokenization/formula/ast_token.py
index 67ca8ffd..22a2af23 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 7b5d1212..1e3236bc 100644
--- a/EduNLP/SIF/tokenization/formula/linear_token.py
+++ b/EduNLP/SIF/tokenization/formula/linear_token.py
@@ -6,6 +6,37 @@
 
 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
+    ----------
+    formula:str
+    preserve_braces:
+        when it is False "{" and "}" will be filted
+    with_dollar:
+        have dollar or not
+    preserve_dollar:
+        keep "$"
+    number_as_tag:
+        whether switch number to tag, it just can idenify the number which is more than one bit.
+    preserve_src
+
+    Returns
+    --------
+    list
+        return a preliminary list which cut fully
+
+    Examples
+    ----------
+    >>> cut(r"${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 +166,7 @@ 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 +197,7 @@ 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:
@@ -201,6 +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 299eaf62..650492c4 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,
@@ -16,9 +17,30 @@
 
 class TokenList(object):
     """
+    Parameters
+    ----------
+    segment_list:list
+        segmented item
+    text_params:dict
+    formula_params:dict
+    figure_params:dict
+
     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 = []
@@ -38,21 +60,23 @@ 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
 
     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:
@@ -60,6 +84,22 @@ 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)
@@ -79,6 +119,7 @@ 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
         ----------
@@ -97,6 +138,8 @@ 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)
@@ -125,6 +168,7 @@ 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 = []
@@ -137,22 +181,27 @@ 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):
@@ -164,6 +213,7 @@ 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)
@@ -175,6 +225,7 @@ 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))
@@ -198,27 +249,32 @@ 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)
@@ -226,6 +282,16 @@ def _append(self, seg_type):
         self._segments.append((start, end, seg_type))
 
     def append(self, segment, lazy=False):
+        """
+        the total api for appending elements
+
+        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)):
@@ -259,15 +325,18 @@ 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):
+        """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))
@@ -285,6 +354,7 @@ 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)
@@ -292,6 +362,7 @@ 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)
@@ -299,6 +370,7 @@ 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):
@@ -306,10 +378,26 @@ 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
+        ----------
+        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
+        --------
+        list
+            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}
@@ -332,6 +420,7 @@ 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),
@@ -341,10 +430,38 @@ 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
+    ----------
+    list
+        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)
 
 
 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":
diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py
index bb7b47e5..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
     --------
@@ -40,7 +53,6 @@ class PureTextTokenizer(Tokenizer):
     '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',',
     '\\quad', 'A', '\\cap', 'B', '=']
     """
-
     def __init__(self, *args, **kwargs):
         self.tokenization_params = {
             "formula_params": {
@@ -56,6 +68,18 @@ def __call__(self, items: Iterable, key=lambda x: x, *args, **kwargs):
 
 class TextTokenizer(Tokenizer):
     r"""
+    Duel with text and formula including special formula.
+
+    Parameters
+    ----------
+    items: str
+    key
+    args
+    kwargs
+
+    Returns
+    -------
+    token
 
     Examples
     ----------
@@ -65,8 +89,13 @@ 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):
         self.tokenization_params = {
             "formula_params": {
@@ -88,12 +117,16 @@ 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
     ----------
     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
     -------
diff --git a/EduNLP/Vector/__init__.py b/EduNLP/Vector/__init__.py
index efccc5ad..ba4618dd 100644
--- a/EduNLP/Vector/__init__.py
+++ b/EduNLP/Vector/__init__.py
@@ -4,5 +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
new file mode 100644
index 00000000..6f0fef5a
--- /dev/null
+++ b/EduNLP/Vector/bert_vec.py
@@ -0,0 +1,57 @@
+import numpy as np
+from pathlib import PurePath
+from transformers import AutoModel
+from .const import UNK, PAD
+from .meta import Vector
+import torch
+
+
+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",
+    ... "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([2, 12, 768])
+    >>> tokens = model.infer_tokens(inputs)
+    >>> tokens.shape
+    torch.Size([2, 10, 768])
+    >>> tokens = model.infer_tokens(inputs, return_special_tokens=True)
+    >>> tokens.shape
+    torch.Size([2, 12, 768])
+    >>> item = model.infer_vector(inputs)
+    >>> item.shape
+    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: dict):
+        # 1, sent_len, embedding_size
+        tokens = self.model(**items).last_hidden_state
+        return tokens
+
+    def infer_vector(self, items: dict) -> torch.Tensor:
+        vector = self(items)
+        return vector[:, 0, :]
+
+    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
+    def vector_size(self):
+        return self.model.config.hidden_size
diff --git a/EduNLP/Vector/gensim_vec.py b/EduNLP/Vector/gensim_vec.py
index 13c5600a..ece25442 100644
--- a/EduNLP/Vector/gensim_vec.py
+++ b/EduNLP/Vector/gensim_vec.py
@@ -11,16 +11,19 @@
 
 
 class W2V(Vector):
+    """
+    The part uses gensim library providing FastText, Word2Vec and KeyedVectors method to transfer word to 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:
@@ -59,17 +62,34 @@ 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:
-        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]
 
 
 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 +108,11 @@ 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 +136,22 @@ 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 model:D2V
+    """
     def __init__(self, filepath, method="d2v"):
         self._method = method
         self._filepath = filepath
diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py
index ec0887ef..4bb72727 100644
--- a/EduNLP/Vector/t2v.py
+++ b/EduNLP/Vector/t2v.py
@@ -6,20 +6,42 @@
 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
 
+
 MODELS = {
     "w2v": W2V,
     "d2v": D2V,
     "rnn": RNNModel,
     "lstm": RNNModel,
     "gru": RNNModel,
-    "elmo": RNNModel
+    "elmo": RNNModel,
+    'bert': BertModel
 }
 
 
 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
+    ----------
+    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$\\FormFigureBase64{wrong2?}$，\
+    ... 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
@@ -49,10 +71,42 @@ 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"],
+    "luna_bert": ["http://base.ustc.edu.cn/data/model_zoo/EduNLP/LUNABert.zip", "bert"]
 }
 
 
 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
+    ----------
+    name:str
+        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
+    -------
+    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", "examples/test_model/data/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/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
     ----------
diff --git a/docs/source/_static/formula.png b/docs/source/_static/formula.png
new file mode 100644
index 00000000..10fecbd3
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diff --git a/docs/source/_static/formulagroup.png b/docs/source/_static/formulagroup.png
new file mode 100644
index 00000000..4b48f46c
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diff --git a/docs/source/_static/new_flow.png b/docs/source/_static/new_flow.png
new file mode 100644
index 00000000..f103cc7d
Binary files /dev/null and b/docs/source/_static/new_flow.png differ
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
index 00000000..bbfacbd3
Binary files /dev/null and "b/docs/source/_static/\346\226\260\346\265\201\347\250\213\345\233\276.png" differ
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"
new file mode 100644
index 00000000..dfdb8737
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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/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/sif.rst b/docs/source/api/sif.rst
index a49f7f15..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
+Parser
+--------
+.. automodule:: EduNLP.SIF.parser.parser.Parser
    :members:
    :imported-members:
 
 
-Parser
---------
-.. automodule:: EduNLP.SIF.parser
+Segment
+----------
+.. automodule:: EduNLP.SIF.segment.segment
    :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
diff --git a/docs/source/api/vector.rst b/docs/source/api/vector.rst
index 9081dfea..1c73d4bc 100644
--- a/docs/source/api/vector.rst
+++ b/docs/source/api/vector.rst
@@ -1,10 +1,16 @@
 EduNLP.Vector
 ==========================
 
-Vector
----------------
+EduNLP.Vector.rnn
+--------------------
 
-.. automodule:: EduNLP.Vector
+.. automodule:: EduNLP.Vector.rnn
    :members:
    :imported-members:
 
+EduNLP.Vector
+-------------------------
+
+.. automodule:: EduNLP.Vector
+   :members:
+   :imported-members:
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 9d6a118b..7787a16d 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -74,6 +74,11 @@ 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',
+    '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/index.rst b/docs/source/index.rst
index 16107eae..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
@@ -151,11 +156,11 @@ 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::
@@ -164,13 +169,12 @@ If this repository is helpful for you, please cite our work
    :hidden:
    :glob:
 
-   api/index
-   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/en/index.rst b/docs/source/tutorial/en/index.rst
index 108a9487..e75e48f1 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..5aba283d
--- /dev/null
+++ b/docs/source/tutorial/en/parse.rst
@@ -0,0 +1,291 @@
+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 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.
+
+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 whether 'x' in several sub formulas is the same variable.
+
+Import modules
++++++++++++++++++++++
+
+::
+
+   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. 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
+
+* 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.
+
+Import modules
+>>>>>>>>>>>>>>>>>>>
+
+::
+
+   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..c09da64b
--- /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  whether '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..6822c961
--- /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. 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
+
+* 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..58105f44
--- /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:
+
+* Single subject(Chinese,Math,English,History,Geography,Politics,Biology,Physics,Chemistry)
+* Multiple subject(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..83b54c39
--- /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..60077309
--- /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..4aa91619
--- /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..4e2f2d39
--- /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, 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
++++++++++++++++++++++++
+
+::
+
+ 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..c6535941
--- /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..f5c44f7e
--- /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..7650aae6 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}$$\\PictureID{088f15eb-8b7c-11eb-a86f-b46bfc50aa29}$
+
+Non-standard Format:
+
+1. Letters, numbers and mathematical symbols are mixed:
+
+    For example:
+    
+    ``完成下面的2x2列联表，``
+    
+    ``（单位：m3）``
+    
+    ``则输出的n=``
+    
+2. Some special mathematical symbols are not represented by the latex formula:
+
+    For example:
+    
+    ``命题中真命题的序号是 ①``
+    
+    ``AB是⊙O的直径，AC是⊙O的切线，BC交⊙O于点E．若D为AC的中点``
+    
+3. There are unicode encoded characters in the text:
+
+    For example:
+    ``则$a$的取值范围是（\u3000\u3000）``
+
+Functions for judging whether text is in SIF format and converting to SIF format
+--------------------------------------------------------------------------------------------------
+
+Import modules
+++++++++
+::
+
+    from EduNLP.SIF import is_sif, to_sif
+
+is_sif
++++++++++++
+
+::
+
+    >>> text1 = '若$x,y$满足约束条件' 
+    >>> text2 = '$\\left\\{\\begin{array}{c}2 x+y-2 \\leq 0 \\\\ x-y-1 \\geq 0 \\\\ y+1 \\geq 0\\end{array}\\right.$，' 
+    >>> text3 = '则$z=x+7 y$的最大值$\\SIFUnderline$'
+    >>> text4 = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
+    >>> is_sif(text1)
+    True
+    >>> is_sif(text2)
+    True
+    >>> is_sif(text3)
+    True
+    >>> is_sif(text4)
+    False
+
+to_sif
++++++++++++
+
+::
+
+    >>> text = '某校一个课外学习小组为研究某作物的发芽率y和温度x（单位...'
+    >>> to_sif(text)
+    '某校一个课外学习小组为研究某作物的发芽率$y$和温度$x$（单位...'
+
+
+Change Log
+----------------
+
+2021-05-18
+
+Changed
+
+1. Originally, we use ``\$\SIFUnderline\$`` and ``\$\SIFBracket\$`` to represent underlines of blanks and brackets of choices. Now we represent them with ``\$\SIFBlank\$`` and ``\$\SIFChoice\$``.
+
+2. Originally, we used ``$\PictureID{ uuid }$`` to represent pictures, but now we use ``$\FigureID{ uuid }$`` instead. Especially, ``$\FormFigureID{ uuid }$`` is used to represent formulas pictures.
+
+2021-06-28 
+  
+Added:
+
+1. There should not be line breaks between the notation ``$$``.
+
+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..88ec975e
--- /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..f6350614
--- /dev/null
+++ b/docs/source/tutorial/en/tokenize.rst
@@ -0,0 +1,173 @@
+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..902b2bb5
--- /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..c955602b
--- /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..181f0b80
--- /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..eb59a34c
--- /dev/null
+++ b/docs/source/tutorial/en/vectorization.rst
@@ -0,0 +1,158 @@
+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.
+
+
+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
+--------------------------------------------------------------------------
+
+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.
+
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..844fdd3b
--- /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..62ce6155
--- /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)
diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst
index 546065b0..5dafba2b 100644
--- a/docs/source/tutorial/zh/index.rst
+++ b/docs/source/tutorial/zh/index.rst
@@ -3,148 +3,51 @@
 
 *  `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>`_ 
 
-*  `向量化 <vectorization.rst>`_ 
-
 *  `预训练 <pretrain.rst>`_ 
 
-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
-^^^^^^^^^^^
-
-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
-####################
+*  `向量化 <vectorization.rst>`_ 
 
-.. 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>
+主要流程
+----------
 
+.. figure:: ../../_static/新流程图.png
 
-语法解析
-^^^^^^^^^^^
+* `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）。
 
-文本语法结构解析
-####################
+* `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）。
 
-.. 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>
+* `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 <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）。
 
-公式语法结构解析
-####################
+* **下游模型**：将得到的向量进一步处理，从而得到所需的结果。
 
-.. 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>
+示例
+--------
 
+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
+    :name: tokenize_gallery
     :glob:
     
-    Code for beginner to learn how to use Tokenizer  <../../build/blitz/tokenizer/tokenizer.ipynb>
+    令牌化  <../../build/blitz/tokenizer/tokenizer.ipynb>
 
 
-向量化
-^^^^^^^^^^^
 
 .. nbgallery::
     :caption: This is a thumbnail gallery:
     :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>
+    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 9d6ea22e..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：
@@ -18,19 +19,274 @@
 
 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
 
-学习路线图
+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。
+
+调用库
++++++++++
+
+::
+
+   import matplotlib.pyplot as plt
+   from EduNLP.Formula import Formula
+   from EduNLP.Formula.viz import ForestPlotter
+
+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。
+
+::
+
+   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
+
+
+文本语法结构解析
 --------------------
 
-.. 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
+
+调用库
+>>>>>>>>>>>>
+
+::
+
+   from EduNLP.SIF.Parser import Parser
+
+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/pretrain.rst b/docs/source/tutorial/zh/pretrain.rst
index 477717a4..ff4d4fed 100644
--- a/docs/source/tutorial/zh/pretrain.rst
+++ b/docs/source/tutorial/zh/pretrain.rst
@@ -8,13 +8,123 @@
 * 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
+
+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")
+
+
+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
+------------
+
+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 e1e1c0db..f65a2b41 100644
--- a/docs/source/tutorial/zh/seg.rst
+++ b/docs/source/tutorial/zh/seg.rst
@@ -14,13 +14,173 @@
 
 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。
+
+导入库
++++++++++
+
+::
+
+ from EduNLP.utils import dict2str4sif
+
+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$2'
+
+结构成分分解
+------------
+
+对切片后的item中的各个元素进行分词，提供深度选项，可以按照需求选择所有地方切分或者在部分标签处切分（比如\SIFSep、\SIFTag处）；对标签添加的位置也可以进行选择，可以在头尾处添加或仅在头或尾处添加。
+
+具有两种模式:
+
+* linear模式，用于对文本进行处理（使用jieba库进行分词）；
+
+* ast模式，用于对公式进行解析。
+
+基础分解流程：
+
+- 使用正则匹配方法匹配出各个组成成分
+
+- 对特殊结构的成分进行处理，如将base64编码的图片转为numpy形式
+
+- 将当前元素分类放入各个元素组中
+
+- 按照需求输入相应的参数得到筛选后的结果
+
+导入库
++++++++++
+
+::
+
+ from EduNLP.SIF.segment import seg
+ from EduNLP.SIF import sif4sci
+
+基础使用方法
+++++++++++++++++++
+
+::
+
+ >>> test_item = r"如图所示，则$\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..0d34eb91 100644
--- a/docs/source/tutorial/zh/sif.rst
+++ b/docs/source/tutorial/zh/sif.rst
@@ -87,6 +87,42 @@ 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
+--------------------------------------------
+
+调用库
+++++++++
+::
+
+    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
 ----------------
@@ -106,4 +142,3 @@ 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..4982f031 100644
--- a/docs/source/tutorial/zh/tokenize.rst
+++ b/docs/source/tutorial/zh/tokenize.rst
@@ -16,13 +16,157 @@
 
 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：
+
+::
+
+   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']
    
-   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。
+
+::
+
+   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}]
+
+
+
+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', '=']
+
+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
+++++++++++++++++++++
+
+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
+
+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/zh/vectorization.rst b/docs/source/tutorial/zh/vectorization.rst
index 89175ba6..aff364ff 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:
+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
+------------------------------------
+
+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
+++++++++++++++++++++
 
-   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>
+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。
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/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": "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"
-     }
-    }
-   ],
-   "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": "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"
-     },
-     "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/i2v/get_pretrained_i2v.ipynb b/examples/i2v/get_pretrained_i2v.ipynb
new file mode 100644
index 00000000..9fe707b7
--- /dev/null
+++ b/examples/i2v/get_pretrained_i2v.ipynb
@@ -0,0 +1,211 @@
+{
+ "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": [],
+   "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",
+    "\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": 3,
+   "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": 4,
+   "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",
+   "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..507c994e
--- /dev/null
+++ b/examples/i2v/i2v.ipynb
@@ -0,0 +1,192 @@
+{
+ "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
+}
\ No newline at end of file
diff --git a/examples/sif/sif.ipynb b/examples/sif/sif.ipynb
index 2076e126..3758f3f8 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)"
    ],
@@ -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 0x1B2ED7C31D0>"
+      ]
      },
      "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'])"
    ],
@@ -142,7 +151,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 7
+     "execution_count": 4
     }
    ],
    "metadata": {}
@@ -156,9 +165,9 @@
   },
   {
    "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": [
@@ -170,17 +179,17 @@
       ]
      },
      "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 +200,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 9
+     "execution_count": 6
     }
    ],
    "metadata": {}
@@ -232,9 +241,9 @@
   },
   {
    "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 +255,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 12
+     "execution_count": 7
     }
    ],
    "metadata": {}
@@ -260,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "source": [
     "segments.text_segments"
    ],
@@ -283,7 +292,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 13
+     "execution_count": 8
     }
    ],
    "metadata": {}
@@ -297,9 +306,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "source": [
-    "segments.formula_segments\n"
+    "segments.formula_segments\r\n"
    ],
    "outputs": [
     {
@@ -319,7 +328,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 15
+     "execution_count": 9
     }
    ],
    "metadata": {}
@@ -333,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "source": [
     "segments.figure_segments"
    ],
@@ -346,14 +355,14 @@
       ]
      },
      "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 +370,13 @@
     {
      "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 0x1B2ED7C31D0>"
+      ]
      },
      "metadata": {},
-     "execution_count": 17
+     "execution_count": 11
     }
    ],
    "metadata": {}
@@ -381,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "source": [
     "segments.ques_mark_segments"
    ],
@@ -394,7 +403,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 19
+     "execution_count": 12
     }
    ],
    "metadata": {}
@@ -420,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "source": [
     "sif4sci(item[\"stem\"], figures=figures, tokenization=False, symbol=\"tfgm\")"
    ],
@@ -433,7 +442,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 11
+     "execution_count": 13
     }
    ],
    "metadata": {
@@ -461,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "source": [
     "tokens = sif4sci(item[\"stem\"], figures=figures, tokenization=True)"
    ],
@@ -487,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "source": [
     "tokens.text_tokens"
    ],
@@ -532,7 +541,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 12
+     "execution_count": 15
     }
    ],
    "metadata": {
@@ -556,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "source": [
     "tokens.formula_tokens"
    ],
@@ -593,7 +602,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 13
+     "execution_count": 16
     }
    ],
    "metadata": {
@@ -619,17 +628,17 @@
   },
   {
    "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",
+   "execution_count": 17,
+   "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": [
@@ -665,7 +674,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 37
+     "execution_count": 17
     }
    ],
    "metadata": {
@@ -686,18 +695,18 @@
   },
   {
    "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 +726,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 39
+     "execution_count": 18
     }
    ],
    "metadata": {
@@ -736,55 +745,55 @@
   },
   {
    "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"
+   "execution_count": 19,
+   "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": "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 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": {},
-     "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",
-    "        f[i], root_list=[node for node in f[i]],\n",
-    "    )\n",
-    "# plt.show()\n"
+    "# 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": {}
@@ -799,19 +808,19 @@
   },
   {
    "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",
+   "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": [
@@ -860,7 +869,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 40
+     "execution_count": 21
     }
    ],
    "metadata": {
@@ -879,20 +888,20 @@
   },
   {
    "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",
+   "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": [
@@ -941,7 +950,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 44
+     "execution_count": 22
     }
    ],
    "metadata": {
@@ -967,9 +976,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 23,
    "source": [
-    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\n",
+    "sif4sci(item[\"stem\"], figures=figures, tokenization=True,\r\n",
     "        symbol=\"fgm\")"
    ],
    "outputs": [
@@ -981,7 +990,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 96
+     "execution_count": 23
     }
    ],
    "metadata": {
@@ -995,11 +1004,11 @@
  "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",
@@ -1010,9 +1019,9 @@
    "file_extension": ".py"
   },
   "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
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
diff --git a/examples/sif/sif_addition.ipynb b/examples/sif/sif_addition.ipynb
index 57830c43..7a2a1b20 100644
--- a/examples/sif/sif_addition.ipynb
+++ b/examples/sif/sif_addition.ipynb
@@ -2,102 +2,166 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# sif_addition"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+     ]
+    }
+   ],
    "source": [
     "from EduNLP.SIF import is_sif, to_sif,sif4sci"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## is_sif"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "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"
-   ],
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "True"
       ]
      },
+     "execution_count": 2,
      "metadata": {},
-     "execution_count": 4
+     "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": 5,
+   "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"
+       "(False, <EduNLP.SIF.parser.parser.Parser at 0x2a3083fa978>)"
       ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "execution_count": 5
+     "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": 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...'\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": 6
+     "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",
@@ -105,283 +169,279 @@
     " - \"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}$\"\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": 14
+     "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": 15,
-   "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": 15
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.describe()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "source": [
-    "with t1.filter('fgm'):\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": 18,
-   "source": [
-    "with t1.filter(keep='t'):\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": 19,
-   "source": [
-    "with t1.filter():\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": 20,
-   "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": 20
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.text_tokens"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "source": [
-    "t1.formula_tokens"
-   ],
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['\\\\bigtriangleup', 'ABC']"
       ]
      },
+     "execution_count": 13,
      "metadata": {},
-     "execution_count": 23
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.formula_tokens"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "source": [
-    "t1.figure_tokens"
-   ],
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[\\FigureID{1}]"
       ]
      },
+     "execution_count": 14,
      "metadata": {},
-     "execution_count": 24
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.figure_tokens"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "source": [
-    "t1.ques_mark_tokens"
-   ],
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['\\\\SIFBlank']"
       ]
      },
+     "execution_count": 15,
      "metadata": {},
-     "execution_count": 25
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "t1.ques_mark_tokens"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "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": 26
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "sif4sci(test_item, symbol=\"gm\", tokenization_params={\"formula_params\": {\"method\": \"ast\"}})"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "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": 27
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "sif4sci(test_item, symbol=\"tfgm\")"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "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": 28
+     "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": 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",
-    "  }"
-   ],
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "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",
+    "}"
+   ]
   },
   {
    "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",
-    "     ]"
-   ],
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "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",
+    "]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "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",
@@ -390,21 +450,21 @@
        " ['mathord_0', '<', 'mathord_1']]"
       ]
      },
+     "execution_count": 21,
      "metadata": {},
-     "execution_count": 33
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tls"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "source": [
-    "tls[1:]"
-   ],
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['mathord_0', '<', 'mathord_1'],\n",
@@ -412,38 +472,43 @@
        " ['mathord_0', '<', 'mathord_1']]"
       ]
      },
+     "execution_count": 22,
      "metadata": {},
-     "execution_count": 34
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tls[1:]"
+   ]
   },
   {
    "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 "
-   ],
+   "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": 35
+     "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": 36,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "tl1 = sif4sci(\n",
     "    test_item_1_str, \n",
@@ -452,60 +517,55 @@
     "        \"formula_params\": {\"method\": \"ast\", \"return_type\": \"list\", \"ord2token\": True}\n",
     "    })\n",
     "    "
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "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": 37
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl1.get_segments()[0]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "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": 38
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl1.get_segments()[1:3]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "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",
@@ -513,81 +573,81 @@
        " ['mathord', '=', 'mathord', '{ }', '\\\\sqrt']]"
       ]
      },
+     "execution_count": 27,
      "metadata": {},
-     "execution_count": 39
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl1.get_segments(add_seg_type=False)[0:3]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "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": 42,
+   "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": 43,
-   "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": 43
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "test_item_2_str"
+   ]
   },
   {
    "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 "
-   ],
+   "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": 44
+     "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": 45,
-   "source": [
-    "tl2.get_segments(add_seg_type=False)"
-   ],
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['\\\\SIFTag{options}'],\n",
@@ -598,143 +658,146 @@
        " ['y', '<', 'x']]"
       ]
      },
+     "execution_count": 32,
      "metadata": {},
-     "execution_count": 45
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl2.get_segments(add_seg_type=False)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "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": 46
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl2.get_segments(add_seg_type=False, drop=\"s\")"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "source": [
-    "tl3 = sif4sci(test_item_1[\"stem\"], symbol=\"gs\")\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": 47
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl3 = sif4sci(test_item_1[\"stem\"], symbol=\"gs\")\n",
+    "tl3.text_segments"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "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": 48
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl3.formula_segments"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "source": [
-    "tl3.figure_segments"
-   ],
+   "execution_count": 36,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[]"
       ]
      },
+     "execution_count": 36,
      "metadata": {},
-     "execution_count": 49
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tl3.figure_segments"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "source": [
-    "tl3.ques_mark_segments"
-   ],
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['\\\\SIFChoice']]"
       ]
      },
+     "execution_count": 37,
      "metadata": {},
-     "execution_count": 50
+     "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": "3.8.5",
-   "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 3.8.5 64-bit"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
   },
-  "interpreter": {
-   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/examples/t2v/get_pretrained_t2v.ipynb b/examples/t2v/get_pretrained_t2v.ipynb
new file mode 100644
index 00000000..801bcf87
--- /dev/null
+++ b/examples/t2v/get_pretrained_t2v.ipynb
@@ -0,0 +1,161 @@
+{
+ "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": [],
+   "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,  1.69it/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",
+    "\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\")"
+   ],
+   "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"
+     ]
+    }
+   ],
+   "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": {
+  "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/test_model/data/d2v/test_256.zip b/examples/test_model/data/d2v/test_256.zip
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index 00000000..f5ddd67b
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diff --git a/examples/test_model/data/d2v/test_256/test_256.bin b/examples/test_model/data/d2v/test_256/test_256.bin
new file mode 100644
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diff --git a/examples/test_model/data/w2v/test_w2v_256.zip b/examples/test_model/data/w2v/test_w2v_256.zip
new file mode 100644
index 00000000..00085d5f
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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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Binary files /dev/null and b/examples/test_model/data/w2v/test_w2v_256/test_w2v_256.kv differ
diff --git a/examples/tokenizer/tokenizer.ipynb b/examples/tokenizer/tokenizer.ipynb
index 4819b00d..a928ebba 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": [
+    "## 调用tokenize函数\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}$\"\r\n",
+    "}\r\n",
+    "\r\n",
+    "# 输出\r\n",
+    "tokenizer = get_tokenizer(\"text\")\r\n",
+    "tokens = tokenizer(item)\r\n",
     "next(tokens) "
    ],
    "outputs": [
@@ -472,6 +483,1125 @@
     }
    ],
    "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 调用sif4sci函数来进行令牌化\r\n",
+    "\r\n",
+    "### 概述\r\n",
+    "\r\n",
+    "SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。"
+   ],
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。    \n",
+    "\n",
+    "![Figure](../../asset/_static/item.png)"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为："
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "item = {\r\n",
+    "  \"stem\": r\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$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": [
+    "## 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": [
+    {
+     "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": {
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
diff --git a/examples/tokenizer/tokenizier.ipynb b/examples/tokenizer/tokenizier.ipynb
index 8dcec093..1f52994d 100644
--- a/examples/tokenizer/tokenizier.ipynb
+++ b/examples/tokenizer/tokenizier.ipynb
@@ -3,82 +3,76 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "source": [
-    "from EduNLP.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
-   ],
+   "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.Tokenizer import PureTextTokenizer, TextTokenizer, get_tokenizer"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# TextTokenizer and PureTextTokenizer\r\n",
-    "\r\n",
-    "- ‘text’ Tokenizer ignores and skips the FormulaFigures and tokenize latex Formulas as Text\r\n",
+    "# TextTokenizer and PureTextTokenizer\n",
+    "\n",
+    "- ‘text’ Tokenizer ignores and skips the FormulaFigures and tokenize latex Formulas as Text\n",
     "- ‘pure_text’ Tokenizer symbolizes the FormulaFigures as [FUMULA] and tokenize latex Formulas as Text"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "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))"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "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": {}
+   "source": [
+    "items = [{\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 $A \\\\cap B=$\",\n",
+    "        \"options\": [\"1\", \"2\"]\n",
+    "        }]\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\n",
+    "tokens = tokenizer(items, key=lambda x: x[\"stem\"])\n",
+    "print(next(tokens))"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "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"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['公式',\n",
@@ -102,30 +96,31 @@
        "  '[MARK]']]"
       ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "execution_count": 4
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "\n",
+    "tokenizer = get_tokenizer(\"text\") # tokenizer = TextTokenizer()\n",
+    "tokens = [t for t in tokenizer(items)]\n",
+    "tokens"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "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"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[['公式',\n",
@@ -147,17 +142,25 @@
        "  '[MARK]']]"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "execution_count": 5
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "tokenizer = get_tokenizer(\"pure_text\") # tokenizer = PureTextTokenizer()\n",
+    "tokens = [t for t in tokenizer(items)]\n",
+    "tokens"
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.6.13 64-bit ('data': conda)"
+   "display_name": "Python 3.6.13 64-bit ('data': conda)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -170,11 +173,8 @@
    "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/utils/data.ipynb b/examples/utils/data.ipynb
index 8009e8d5..ded161cf 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,
@@ -18,21 +25,28 @@
      "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,
    "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",
+    "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": [
@@ -50,23 +64,40 @@
    ],
    "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\r\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$2'"
       ]
      },
      "metadata": {},
-     "execution_count": 4
+     "execution_count": 15
     }
    ],
    "metadata": {}
@@ -93,60 +124,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "source": [
-    "dict2str4sif(item, tag_mode=\"head\")"
+    "dict2str4sif(item, add_list_no_tag=False)"
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "'$\\\\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}$'"
       ]
      },
      "metadata": {},
-     "execution_count": 10
+     "execution_count": 9
     }
    ],
    "metadata": {}
diff --git a/examples/vectorization/i2v.ipynb b/examples/vectorization/i2v.ipynb
index 3122fbce..f38ae4fe 100644
--- a/examples/vectorization/i2v.ipynb
+++ b/examples/vectorization/i2v.ipynb
@@ -1,192 +1,8856 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# I2V\n",
-    "\n",
-    "## 概述\n",
-    "\n",
-    "使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。\n",
-    "\n",
-    "- 优点：可以使用自己的模型，另可调整训练参数，灵活性强。"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 导入类"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "source": [
-    "from EduNLP.I2V import D2V"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 输入\n",
-    "\n",
-    "类型：str  \n",
-    "内容：题目文本 （text）"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "source": [
-    "item = {\n",
-    "\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$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",
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+      "        -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": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.6.13",
+   "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 3.6.13 64-bit ('data': conda)"
+  },
+  "interpreter": {
+   "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55"
+  }
+ },
+ "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..4a91634c
--- /dev/null
+++ b/examples/vectorization/total_vector.ipynb
@@ -0,0 +1,557 @@
+{
+ "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": 1,
+   "source": [
+    "from EduNLP.I2V import D2V"
+   ],
+   "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": [
+    "#### 输入\r\n",
+    "\r\n",
+    "类型：str  \r\n",
+    "内容：题目文本 （text）"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "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}$\",\r\n",
+    "r\"2如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$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": 3,
+   "source": [
+    "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[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": [
+      "[ 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": "markdown",
+   "source": [
+    "### W2V"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "from EduNLP.I2V import W2V"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "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",
+    "\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": [
+      "[-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",
+      "[-9.74057533e-04  1.39671087e-03 -2.67836265e-04  3.15364590e-03\n",
+      "  2.96666636e-04  2.81736051e-04 -2.63743475e-03  1.52303779e-03\n",
+      "  1.01379235e-03  1.57282199e-03 -1.71113803e-04  8.02559836e-04\n",
+      "  2.57097790e-03  1.81893981e-03  2.63088616e-03  3.40178027e-04\n",
+      " -2.11292668e-03 -2.50976160e-03 -1.20709895e-03  1.67239667e-03\n",
+      " -2.58512655e-03 -1.26207829e-03  1.39700493e-03  1.95603608e-03\n",
+      "  2.89038429e-03 -2.39552581e-03 -3.91247275e-04 -3.21114226e-03\n",
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+      " -2.73981970e-03  1.85640890e-03 -8.50516954e-04  2.05107126e-03\n",
+      "  3.25771095e-03 -2.02651741e-03 -2.99406121e-03 -2.29128683e-03\n",
+      "  7.10814027e-04 -2.45556026e-03  3.29233892e-03 -1.30950764e-03\n",
+      " -1.89368729e-03  1.27877074e-03  2.70718103e-03 -2.21936312e-03\n",
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+      "  9.29941365e-04  9.57158394e-04 -4.04856197e-04  7.77039502e-04\n",
+      "  2.93451315e-03  1.50868180e-03 -2.39667180e-03  1.94984837e-03\n",
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+      " -5.66801231e-04  7.95386615e-04 -2.41047610e-03  1.00338063e-03\n",
+      "  2.82178726e-03 -2.43772753e-03  5.73688361e-04  1.23452744e-03\n",
+      " -3.01872566e-03 -1.07384368e-03  3.28231254e-03 -5.47548116e-04\n",
+      "  2.18831864e-03 -3.27980524e-04  1.68147963e-03  2.44990899e-03\n",
+      " -2.45229807e-03  1.02455064e-03 -2.29938584e-03  2.91304989e-03\n",
+      "  1.60753564e-03  1.12590473e-03  3.00752558e-03  1.44218525e-03\n",
+      "  3.16225761e-03 -1.64008932e-04 -5.27421653e-04  1.06547831e-03\n",
+      "  1.11937604e-03  4.77150286e-04 -2.42268969e-03 -3.12998053e-03\n",
+      "  4.55578178e-04  2.90129334e-03 -3.05265025e-03 -1.28805637e-03\n",
+      " -2.08641519e-03  3.26466886e-03 -2.95106089e-03 -2.08173040e-03\n",
+      " -1.99576933e-03  4.53641405e-04 -2.70907651e-03 -2.34400504e-03\n",
+      "  1.16086320e-03 -2.44718627e-03  9.25636268e-05  9.73496411e-04\n",
+      " -1.01899146e-03 -3.02827288e-03  9.58363991e-04 -3.27257067e-03\n",
+      "  2.40717572e-03  1.20117664e-04  1.10580446e-03 -4.41495577e-05\n",
+      " -2.85318610e-03  1.54357916e-03  3.11869616e-03  6.44255488e-04\n",
+      " -1.31027703e-03 -1.52749463e-03  8.79097788e-04  7.01892364e-04\n",
+      "  1.34046946e-03 -8.91715696e-04 -1.93791394e-03 -4.34041809e-04\n",
+      "  1.12010317e-03 -2.24535773e-03 -1.76302914e-03 -1.11521804e-03\n",
+      " -2.68377946e-03 -2.55486579e-03  2.67607206e-03 -2.09635729e-03\n",
+      "  4.45536774e-04  4.23340796e-04 -2.36946181e-03  1.01201690e-03\n",
+      "  2.53369007e-03 -8.69231240e-04 -2.23573043e-05  4.58726077e-04\n",
+      "  9.46683111e-04 -1.58690137e-03  1.31600059e-03  2.19468423e-03\n",
+      " -1.69886113e-03  1.71214901e-03 -1.43307843e-04 -1.10225752e-03\n",
+      "  3.13180522e-03  1.78616366e-03 -4.65679186e-05 -1.40959187e-03\n",
+      " -6.96717121e-04 -5.70511795e-04 -1.54102559e-03 -2.30318774e-03\n",
+      "  2.54784338e-03 -1.62216101e-03  3.14650533e-04 -2.94532534e-03\n",
+      " -1.02099183e-03  2.99499906e-03 -6.38728146e-04 -2.72372481e-03\n",
+      "  3.22340080e-03 -1.49127806e-03  2.27723271e-03  2.73366761e-03\n",
+      "  2.62600114e-03  2.68271845e-03  3.20440996e-03 -4.97240224e-04\n",
+      " -1.02938886e-03  3.26999027e-04  9.46711691e-04  1.76053529e-03\n",
+      "  1.74157624e-03  1.49760721e-03 -3.09546776e-05  2.48821010e-03\n",
+      "  2.15774146e-03  2.42709951e-03 -2.46135960e-03  1.82637456e-03\n",
+      " -3.11999000e-03 -2.49591586e-03 -3.27967643e-03 -1.17016002e-03\n",
+      "  6.43555308e-04  3.32132494e-03 -2.58475146e-03 -7.75608991e-04\n",
+      "  3.30572366e-03  6.71840506e-04 -2.23828160e-04  2.99876463e-03\n",
+      "  3.10293835e-05 -1.25048554e-03  2.48837401e-03 -4.16146126e-04\n",
+      " -8.01149989e-04 -2.19148802e-04 -2.70171487e-03  1.73141161e-04\n",
+      " -2.53586681e-03  3.11773620e-03  1.13646187e-04  2.82005151e-03\n",
+      " -3.23787535e-04  1.52152695e-03  3.21076158e-03 -2.29426223e-04\n",
+      " -2.22376501e-03 -3.26833175e-03  5.72812569e-04  3.06089874e-03\n",
+      "  8.33402446e-04  1.29480439e-03  1.32911524e-03  2.61883345e-03\n",
+      " -2.53178203e-03  6.48000219e-04  2.66361074e-03 -3.05172289e-03\n",
+      " -9.23413434e-04 -2.13261833e-03  8.54914193e-04 -1.48963137e-03\n",
+      " -1.95632223e-03 -7.69955339e-04 -3.29735363e-03  1.98830920e-03\n",
+      "  1.31162966e-03  1.10320176e-03 -3.22533771e-03  2.04978790e-03\n",
+      " -5.25970478e-04 -1.89223525e-03  2.42309878e-03  8.27315671e-04\n",
+      "  9.63741913e-04  8.84156208e-04  1.02529768e-03 -1.41616585e-03\n",
+      "  6.75518531e-04 -6.47147477e-04  2.71809031e-03  2.17319001e-03\n",
+      "  9.71910951e-04 -2.93364283e-03  2.43404706e-04  1.14709849e-03\n",
+      " -1.99730392e-04  3.82491737e-04 -3.08531453e-03 -2.20424891e-03\n",
+      "  2.87708524e-03  1.51069486e-03  9.24036489e-04 -1.09619542e-03\n",
+      "  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"
+     ]
+    }
+   ],
+   "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": 6,
+   "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": 7,
+   "source": [
+    "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"
+   ],
+   "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：    \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": 8,
+   "source": [
+    "i2v = get_pretrained_i2v(\"d2v_sci_256\", model_dir=\"./d2v\")"
+   ],
+   "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 d2v\\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": 9,
+   "source": [
+    "item_vectors, token_vectors = i2v(items)\r\n",
+    "print(item_vectors)\r\n",
+    "print(token_vectors)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[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"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
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',
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_sif/test_sif.py b/tests/test_sif/test_sif.py
index 210441d2..02d30132 100644
--- a/tests/test_sif/test_sif.py
+++ b/tests/test_sif/test_sif.py
@@ -31,6 +31,12 @@ def test_to_sif():
     siftext = to_sif(text)
     print(siftext)
 
+    ret = is_sif(text, return_parser=True)
+    assert ret[0] == 0
+    if ret[0] is not True:
+        siftext = to_sif(text, parser=ret[1])
+    print(siftext)
+
 
 def test_sci4sif(figure0, figure1, figure0_base64, figure1_base64):
     repr(sif4sci(
@@ -57,3 +63,17 @@ 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$\SIFBlank$。$\FigureID{1}$", mode=0
+    ))
+    repr(sif4sci(
+        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$\SIFBlank$。$\FigureID{1}$", mode=2
+    ))
+
+    with pytest.raises(KeyError):
+        repr(sif4sci(
+            r"如图所示，则$\bigtriangleup ABC$的面积是$\SIFBlank$。$\FigureID{1}$", mode=3
+        ))
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_vec/test_bert.py b/tests/test_vec/test_bert.py
new file mode 100644
index 00000000..3046a9ea
--- /dev/null
+++ b/tests/test_vec/test_bert.py
@@ -0,0 +1,74 @@
+import torch
+import numpy as np
+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")
+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
+    )
+    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_i2v(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
+    )
+
+    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
+
+    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
diff --git a/tests/test_vec/test_vec.py b/tests/test_vec/test_vec.py
index d97210d8..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
@@ -110,7 +111,7 @@ 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 +188,7 @@ 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