Text/29NOV22_AvengerCon VII_Text_Complete.ipynb

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   "source": [
    "! pip install textstat transformers category_encoders"
   ]
  },
  {
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   "execution_count": 6,
   "id": "1781b328-1959-4806-8767-9e5fc062bca3",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    }
   ],
   "source": [
    "import os, random, textstat, spacy, re, string, itertools\n",
    "from collections import defaultdict, Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.api.types import CategoricalDtype\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments, pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "import category_encoders as ce\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-whitegrid\")\n",
    "plt.rc(\"figure\", autolayout=True)\n",
    "plt.rc(\n",
    "    \"axes\",\n",
    "    labelweight=\"bold\",\n",
    "    labelsize=\"large\",\n",
    "    titleweight=\"bold\",\n",
    "    titlesize=14,\n",
    "    titlepad=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2DfYSustRE8h",
   "metadata": {
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    "id": "2DfYSustRE8h",
    "outputId": "55dba378-8141-4836-d9e6-ac434997e060"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1o_QH7OB56BBgPFFJLYMBzkbAwKeNj9XC\n",
      "To: /content/text.csv\n",
      "100% 9.29M/9.29M [00:00<00:00, 150MB/s]\n"
     ]
    }
   ],
   "source": [
    "! gdown \"1o_QH7OB56BBgPFFJLYMBzkbAwKeNj9XC\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7b22841-7486-4f72-b9f9-065eca28c4ee",
   "metadata": {
    "id": "f7b22841-7486-4f72-b9f9-065eca28c4ee"
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   "outputs": [],
   "source": [
    "'''Set dataset directory'''\n",
    "ROOT_DIR = \"/content\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6ac1bd-f049-4dec-a682-c915a594487e",
   "metadata": {
    "id": "cc6ac1bd-f049-4dec-a682-c915a594487e"
   },
   "source": [
    "# 1. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212097fb-20c1-425a-860f-3b3d9de58a45",
   "metadata": {
    "id": "212097fb-20c1-425a-860f-3b3d9de58a45"
   },
   "source": [
    "## 1(a) Profile the dataset"
   ]
  },
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       "      <th></th>\n",
       "      <th>text_id</th>\n",
       "      <th>full_text</th>\n",
       "      <th>cohesion</th>\n",
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       "      <th>vocabulary</th>\n",
       "      <th>phraseology</th>\n",
       "      <th>grammar</th>\n",
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       "      <td>3.0</td>\n",
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       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00299B378633</td>\n",
       "      <td>Dear, Principal\\n\\nIf u change the school poli...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0049B1DF5CCC</td>\n",
       "      <td>Small act of kindness can impact in other peop...</td>\n",
       "      <td>2.5</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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      "text/plain": [
       "        text_id                                          full_text  cohesion  \\\n",
       "0  0016926B079C  I think that students would benefit from learn...       3.5   \n",
       "1  0022683E9EA5  When a problem is a change you have to let it ...       2.5   \n",
       "2  00299B378633  Dear, Principal\\n\\nIf u change the school poli...       3.0   \n",
       "3  003885A45F42  The best time in life is when you become yours...       4.5   \n",
       "4  0049B1DF5CCC  Small act of kindness can impact in other peop...       2.5   \n",
       "\n",
       "   syntax  vocabulary  phraseology  grammar  conventions  \n",
       "0     3.5         3.0          3.0      4.0          3.0  \n",
       "1     2.5         3.0          2.0      2.0          2.5  \n",
       "2     3.5         3.0          3.0      3.0          2.5  \n",
       "3     4.5         4.5          4.5      4.0          5.0  \n",
       "4     3.0         3.0          3.0      2.5          2.5  "
      ]
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   ],
   "source": [
    "df = pd.read_csv(os.path.join(ROOT_DIR, \"text.csv\"))\n",
    "\n",
    "display(df.head())"
   ]
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     "output_type": "stream",
     "text": [
      "df shape: (3911, 8)\n"
     ]
    }
   ],
   "source": [
    "print(\"df shape:\", df.shape)"
   ]
  },
  {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3911 entries, 0 to 3910\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   text_id      3911 non-null   object \n",
      " 1   full_text    3911 non-null   object \n",
      " 2   cohesion     3911 non-null   float64\n",
      " 3   syntax       3911 non-null   float64\n",
      " 4   vocabulary   3911 non-null   float64\n",
      " 5   phraseology  3911 non-null   float64\n",
      " 6   grammar      3911 non-null   float64\n",
      " 7   conventions  3911 non-null   float64\n",
      "dtypes: float64(6), object(2)\n",
      "memory usage: 244.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
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   "id": "9927f2b6-8363-43e0-9dcc-880d4117c9ad",
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   "source": [
    "## 1(b) Look at the target variable"
   ]
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "targets = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']\n",
    "\n",
    "fig, axes = plt.subplots(2, len(targets)//2, figsize=(15,6))\n",
    "\n",
    "for i, target in enumerate(targets):\n",
    "    ax = axes.flat[i]\n",
    "    sns.histplot(x=target, data=df, linewidth=1.25, alpha=1, ax=ax, zorder=2)\n",
    "    ax.set_title(target)\n",
    "    ax.set(xlabel=None, ylabel=None)\n",
    "    \n",
    "fig.suptitle('Output Variables')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "723e6602-9e1a-4074-843c-b65a98e7aca5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "id": "723e6602-9e1a-4074-843c-b65a98e7aca5",
    "outputId": "7635e042-0dbc-483e-8e5c-7124be1e1c29"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df[targets].corr()\n",
    "fig, ax = plt.subplots(figsize=(8,8))        \n",
    "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, square=True, annot=True, cmap=\"vlag\", fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9826c8-2450-40dd-9f11-dbfb76ecefa7",
   "metadata": {
    "id": "bb9826c8-2450-40dd-9f11-dbfb76ecefa7"
   },
   "source": [
    "## 1(c) Text Analysis\n",
    "- look at word and document statistics, especially as they relate to the target variables\n",
    "- look at common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "680cbf86-1360-4217-92a8-4ed9720e26bb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "id": "680cbf86-1360-4217-92a8-4ed9720e26bb",
    "outputId": "52c2aae0-73e6-42f4-a3c6-b289ffc53477"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 5))\n",
    "df['num_words'] = df['full_text'].apply(lambda x: len(x.split()))\n",
    "sns.histplot(data=df, x=\"num_words\")\n",
    "plt.title(\"Number of Words\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcf07477-31d5-45dc-9fa9-8b9e25a5f9e6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "id": "bcf07477-31d5-45dc-9fa9-8b9e25a5f9e6",
    "outputId": "eb8b71aa-b3c7-4504-81a6-1b0dfb0c1def"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 5))\n",
    "df['mean_word_len'] = df['full_text'].str.split().apply(lambda x : np.mean([len(i) for i in x]))\n",
    "sns.histplot(data=df, x=\"mean_word_len\")\n",
    "plt.title(\"Average Word Length\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec8050e5-d3ca-4557-b426-0cf6b7fa7903",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "id": "ec8050e5-d3ca-4557-b426-0cf6b7fa7903",
    "outputId": "ff54fce5-d9b3-4a15-eabd-c3856af62527"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 5))\n",
    "df['grade_level'] = df['full_text'].apply(lambda x: textstat.text_standard(x, float_output=True))\n",
    "sns.histplot(data=df, x=\"grade_level\")\n",
    "plt.title(\"Grade Levels\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1da878f5-0b7c-4fce-ad89-e41fa7505670",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "id": "1da878f5-0b7c-4fce-ad89-e41fa7505670",
    "outputId": "58133e69-2be1-4421-c1dd-c77a0a999be0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 5))\n",
    "df['reading_ease'] = df['full_text'].apply(lambda x: textstat.flesch_reading_ease(x))\n",
    "sns.histplot(data=df, x=\"reading_ease\")\n",
    "plt.title(\"Reading Ease\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72d50cc7-0898-4a17-b197-1e9fe21db24b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "72d50cc7-0898-4a17-b197-1e9fe21db24b",
    "outputId": "bff7edc9-d623-4887-f511-eea888b1dc79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Being yourself doesn't depends in how people see you, it depends on you because you are the only one that can choose to change or not, it makes no difference if you did something right or wrong the society always will discriminate and see that at the way they think it is,and you can't do anything about it, you are doing the things that you feel that are correct and how is the way how is has to be, however being in a world that is trying to make you something else is a great accomplishment because there is not way people make you change;\n",
      "\n",
      "you had to live your life at the way you want it, you have the opportunity to express your feelings everywhere, and you are in the world to be happy and not to satisfy every single person\n",
      "\n",
      "Sometimes it's hard for you to be deal in the society with opinions that hurt you and make you feel bad about it, and that is something that you cannot change, but it is something that help you to learn how to be more responsible when you are growing up and to be overconfident about what are your thoughts, you have to know that in the world are many people want to see you go down and change the way you are, in that situation you have to be with your head up and be who you really want to be, you can do the difference in your life, but not in others person life\n",
      "\n",
      "In addition, you have to be proud and brave everywhere you go to because you have the opportunity to express what are yours feelings, there is not way that somebody can change what is in you heart, only you choose who can hurt you and if you want that continue, try to stay positive all the time not matter what is going on in your life, do not let someone know what are your debilities and how you feel about things go in on in your life that person can hurt you every time he/she want it, do the things that make you feel better with yourself\n",
      "\n",
      "Finally, be happy all the time with the things that you have because it might be the little things, but are things for which you work really hard to have it, you have to be brave especially when you have done something that probably other person will see that bad, however that doesn't mean you are wrong is just what they think about it, you need to be how you feel comfortable and not to maintain happy the society, reach your goals how you want it, don't let anyone destroy your dreams because if that happen you are not going to be cheerful about it\n",
      "\n",
      "In conclusion, live your life how you want it because is only one, do what you think is correct and follow your dreams, since the moment you let the time past by doing what other people want; you will not be happy nor proud about what you really want to achieve, it's difficult to trust people that one day made you feel safe and in peace and then did things that they knew that it hurt you; if you really want something good for somebody else you will do everything to protect that person and make it feel unique, simply be yourself without get involved in other person's life. \n"
     ]
    }
   ],
   "source": [
    "'''Look at worst reading ease'''\n",
    "print(df[df['reading_ease']==df['reading_ease'].min()]['full_text'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9b08121b-2688-470e-99d5-7a763cd2d6fa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9b08121b-2688-470e-99d5-7a763cd2d6fa",
    "outputId": "fd6cd678-de54-4529-e4af-53ac25ed8023"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Would you like to have more time at school? I would like to have more time at school. Setting at class and learning more things. Getting work done and relaxing. I have three reason why we should have more time at school.\n",
      "\n",
      "My first reason why we should have more time at5 school is getting work done. If theres more time at school,then theres more time to do your homework at school. Getting your homework done so you dont have to do ot at home because you finish your work at school. My reason is getting work done.\n",
      "\n",
      "My second reason why we should have more time at school is your friends. School can get boring so thats why you have friends so you can hangout with them at school. Your friends can be your work buddy so you and your friends can work together at school so you can finish your work. You can have fun with your friends at school so it wont be so boring i school. My other reason is your friends.\n",
      "\n",
      "My third reason why we should have more time at school is your grades. Is your grades bad? Well you can make it up because theres more time at school. If you have more work and if you get a good grade on it,the your grade will go up. Theres more time at school so you can make up work that you did bad on because theres more time at school. My last one is about grades.\n",
      "\n",
      "So there you go,I gave you three reason why we should have more time at school. I think there good reasons. I hop you area what I wrote. If you dont area its ok. Stay in school kids.\n"
     ]
    }
   ],
   "source": [
    "'''Look at best reading ease'''\n",
    "print(df[df['reading_ease']==df['reading_ease'].max()]['full_text'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9eb88680-ba1d-4479-9832-659c16753ac2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9eb88680-ba1d-4479-9832-659c16753ac2",
    "outputId": "f6bc16dc-b591-41ee-9e4d-6fc6859464a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my name is Generic_Name and my story is about cars\n",
      "\n",
      "I bay a car and it new i live that car so mush so i stirt taking caer to it and befor I usto have a buskel and i usto love that baskel so mauh but i gat older so i stop riding a biskel and i stirt looking for a beges thengs whan i was a kid i usto so my dad driveng a car and i usto haveng fun in it\n",
      "\n",
      ";and my drimm was to bay a car win i gro up and i stil looking for that drimm seccer is the only sport i like i use to play seccer win i weas 11 years old and i stil play that geame and i love it so mush but my lieg get hert so i stop playing it any moer and then i stirt fling better so i want to go bake to play my favert game seccer so i try ot for school and i stirt making frinds and play with them my favert game evry day i even like a gril becoes that game so l like that gril and i stirt playing wiht her and taking wihe her and i wanted her number so i stirt beang cloes to her so it bean a year so i finly say it i told her that i love her and she say yes and she my grilfrind new so this year im in 11 grad and i play sceer for Generic_School and it coold but we have to phet so we can get bater so it ban 3years whan the last time i see my dad and mom becase thay stiy back ther in my cantery ariq and i relly weant them to be besid me in USA\n",
      "\n",
      "and about my life i have 2brthers and 1 sister.\n"
     ]
    }
   ],
   "source": [
    "'''Look at highest grade level'''\n",
    "print(df[df['grade_level']==df['grade_level'].max()]['full_text'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "526e55f1-0885-49e1-88d8-3e2ca21e8437",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "526e55f1-0885-49e1-88d8-3e2ca21e8437",
    "outputId": "289b8939-95cd-4ea7-e558-ede2b1f29e8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The year book is for to not forget anything and it helps you lot will somtimes people don't like it but for me it helps me lot but we don't the have it any of thoes no more it lets you to not to foget to do your homework and what do have torrmorw and the next week But we have those any more because we are now 8th grades but the kids what year book to show thier family what spots they did even to show kids went to school even to show how there friends looks like and one day there family can met them even I agree to do it like what to show there family what did and there school looks like even for they can rember but also if there friends moves away and we will still rember even to love them so much but still I agree for to do the years book and I'am ok with it and what the year books still and I still love them and miss them lot I want them back and it actlly look good But like we only get them an a year it takes for ever to get and I think to pay to get your own one but I like and the fotes to.       \n"
     ]
    }
   ],
   "source": [
    "'''Look at lowest grade level'''\n",
    "print(df[df['grade_level']==df['grade_level'].min()]['full_text'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1a7e196-13bb-4f07-bdc6-029608fcda8b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "id": "d1a7e196-13bb-4f07-bdc6-029608fcda8b",
    "outputId": "079a1161-c09e-4781-85f0-37b43eed2c7d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "\n",
    "stop_words = set(stopwords.words('english'))\n",
    "corpus = ''.join(df.full_text).split()\n",
    "\n",
    "dic = defaultdict(int)\n",
    "for word in corpus:\n",
    "    if word in stop_words:\n",
    "        dic[word] += 1\n",
    "top_words = sorted(dic.items(), key=lambda x:x[1], reverse=True)[:15] \n",
    "x, y = zip(*top_words)\n",
    "plt.bar(x, y)\n",
    "plt.title('Common Stopwords')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9d463394-79b4-40be-b54b-5689b19e0eb9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "id": "9d463394-79b4-40be-b54b-5689b19e0eb9",
    "outputId": "40a5d287-7e11-46b9-b449-85dfd2fc26a1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "\n",
    "counter = Counter(corpus)\n",
    "words = counter.most_common()\n",
    "\n",
    "x, y = [], []\n",
    "i = 0\n",
    "for word, count in words:\n",
    "    if (word not in stop_words):\n",
    "        x.append(word)\n",
    "        y.append(count)  \n",
    "        i += 1\n",
    "    if i > 15:\n",
    "        break\n",
    "plt.bar(x, y)\n",
    "plt.title('Common Words')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2f-fkdwTOho",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "d2f-fkdwTOho",
    "outputId": "74e2eddb-e533-4965-e630-68ec6a87136b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([False,  True]), array([2804, 1107]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "special_characters = '''@#$%^*()-+_=<>'''\n",
    "fig = plt.figure(figsize=(16, 5))\n",
    "df['special_chars'] = df['full_text'].apply(lambda x: any(c in special_characters for c in x))\n",
    "np.unique(df['special_chars'], return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "X3aFmGWbUimc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X3aFmGWbUimc",
    "outputId": "e2e9665f-b764-4db1-d8cb-389a5b683645"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dear Principal,\n",
      "\n",
      "Our school should have a community center. The reasons why, are so students can learn what our community needs, how to make our community better place, and why is community important for students to know. Its a great to have a community center to know how we can make things better.\n",
      "\n",
      "Students think community center takes their time away. but they have to learn what our community needs. students will participate in a group of students making a list what our community needs, therefore students will learn what our community needs! students will present their list of things our community needs! due to that students will be giving extra credit for the ones who have low grades!\n",
      "\n",
      "Some students don't participate because their friends say its waste of time. it would not be waste of time when you get to know how our community can be a better place for us. students should know that the program is about our own lives, because if our community is bad well our lives are going to be bad. due to that students will want to participate and will want to make our community a better place for us.\n",
      "\n",
      "some student might say why is the community important anyways, were fine nothing is wrong. but when get to know what our community is, they would want to know about and would want to participate and know why is our community important. students will receive a good grade if they participate in this community because is like an extra credit. due to that their friends would want to enjoy the program because their friend told them about it.\n",
      "\n",
      "so principal the community center will be a great place for students to learn lots of things. so i guess this will be a good idea for our school. students will learn what our community needs, how to make our community a better place, and why is our community important!\n",
      "\n",
      "Sincerely, STUDENT_NAME\n"
     ]
    }
   ],
   "source": [
    "print(df[df['special_chars']==True]['full_text'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "899d5b27-0e1e-4594-914d-914235a2147e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 570
    },
    "id": "899d5b27-0e1e-4594-914d-914235a2147e",
    "outputId": "a4ea69b6-acfa-4c33-ed24-7c498c1bbc4d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df[targets+['grade_level', 'reading_ease', 'mean_word_len', 'num_words']].corr()\n",
    "fig, ax = plt.subplots(figsize=(8,8))        \n",
    "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, square=True, annot=True, cmap=\"vlag\", fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33nsmcjWg3N9",
   "metadata": {
    "id": "33nsmcjWg3N9"
   },
   "source": [
    "## 1(d) Look at possible labelings of the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "38YuE-ay-9Ol",
   "metadata": {
    "id": "38YuE-ay-9Ol"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3563a06097c4134916e5445357f5f67",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "edf717bf96fa4cf48b1e71c948a3602f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "430fb423734749048cd4ab918eb7bb21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24dd7cbc5a5e403d9cae73fadde1655c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1954757e89ca4ee8a37b73f9b7ab47d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28ea769da8054d50a5740548f542fb3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zsc = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\", device=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "hVTFKC92_Sk8",
   "metadata": {
    "id": "hVTFKC92_Sk8"
   },
   "outputs": [],
   "source": [
    "candidate_labels = [\"remote learning\", \"homework\", \"hobbies\", \"jobs\", \"vacation\", \"teachers\", \"bullying\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "IJh2KQ0fHJyA",
   "metadata": {
    "id": "IJh2KQ0fHJyA"
   },
   "outputs": [],
   "source": [
    "sample_df = df.sample(n=50)\n",
    "results = zsc(sample_df[\"full_text\"].tolist(), candidate_labels, batch_size=22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "OXOJl30Tf45L",
   "metadata": {
    "id": "OXOJl30Tf45L"
   },
   "outputs": [],
   "source": [
    "zsc_labels = []\n",
    "for result in results:\n",
    "  label = result['labels'][np.argmax(result['scores'])]\n",
    "  zsc_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "rv5aSMYugTDF",
   "metadata": {
    "id": "rv5aSMYugTDF"
   },
   "outputs": [],
   "source": [
    "sample_df[\"zsc_label\"] = zsc_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "PAQ0sk40BsHd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "id": "PAQ0sk40BsHd",
    "outputId": "ffabef5e-145a-482c-bc2d-f9c88c392dae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "sns.histplot(data=sample_df, x=\"zsc_label\")\n",
    "plt.title(\"Zero-shot Topics\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add7a021-e896-4123-aaec-e15cd0bb98a8",
   "metadata": {},
   "source": [
    "## 1(e) Look at any anomolous texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7967aab7-e0af-435c-bd2e-667729bbf6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "anom_df = df[(df[\"grade_level\"] > 20)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1e17846d-6be4-486a-81de-439de06ba4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(169, 13)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anom_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d9191edc-3df4-45b6-bf64-fd8df2993b9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "targets = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']\n",
    "\n",
    "fig, axes = plt.subplots(2, len(targets)//2, figsize=(15,6))\n",
    "\n",
    "for i, target in enumerate(targets):\n",
    "    ax = axes.flat[i]\n",
    "    sns.histplot(x=target, data=anom_df, linewidth=1.25, alpha=1, ax=ax, zorder=2)\n",
    "    ax.set_title(target)\n",
    "    ax.set(xlabel=None, ylabel=None)\n",
    "    \n",
    "fig.suptitle('Output Variables')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8306509-c95b-4f5f-9068-ba6ad238afad",
   "metadata": {
    "id": "f8306509-c95b-4f5f-9068-ba6ad238afad"
   },
   "source": [
    "# 2. Import and Preprocess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "887b7ac0-a56c-474e-8756-76899b63f835",
   "metadata": {
    "id": "887b7ac0-a56c-474e-8756-76899b63f835"
   },
   "outputs": [],
   "source": [
    "def preprocess_data(df,  cat_features=None):\n",
    "    \n",
    "    # Specify categorical variables\n",
    "    if cat_features is not None:\n",
    "        for name in cat_features:\n",
    "            df[name] = df[name].astype(\"category\")\n",
    "            # Add a None category for missing values\n",
    "            if \"None\" not in df[name].cat.categories:\n",
    "                df[name].cat.add_categories(\"None\", inplace=True)\n",
    "                \n",
    "\n",
    "    # create test data set\n",
    "    train, test = train_test_split(df, test_size=0.2)\n",
    "\n",
    "\n",
    "    return train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca2b8030-d61a-4c8e-b65a-19551a62d713",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ca2b8030-d61a-4c8e-b65a-19551a62d713",
    "outputId": "0e1cb299-05c8-4634-df49-fb84ef64bfe8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pandas/core/arrays/categorical.py:2631: FutureWarning: The `inplace` parameter in pandas.Categorical.add_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  res = method(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(ROOT_DIR, \"text.csv\"), index_col=\"text_id\")\n",
    "train, test = preprocess_data(df, cat_features=['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0jVaxIYmVAYP",
   "metadata": {
    "id": "0jVaxIYmVAYP"
   },
   "outputs": [],
   "source": [
    "'''encode the target variable'''\n",
    "enc = ce.one_hot.OneHotEncoder()\n",
    "y_train = enc.fit_transform(train[['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']])\n",
    "y_test = enc.transform(test[['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "uvDAZDvKWVpo",
   "metadata": {
    "id": "uvDAZDvKWVpo"
   },
   "outputs": [],
   "source": [
    "train = train.join(y_train)\n",
    "test = test.join(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137c66e6-4130-4c5c-8c34-5a5e144fadc9",
   "metadata": {
    "id": "137c66e6-4130-4c5c-8c34-5a5e144fadc9"
   },
   "source": [
    "# 3. Set up Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7abadf82-cb44-442b-bb90-f0b2039aaa59",
   "metadata": {
    "id": "7abadf82-cb44-442b-bb90-f0b2039aaa59"
   },
   "outputs": [],
   "source": [
    "config = {\n",
    "    'model': 'distilbert-base-uncased',\n",
    "    'num_classes' :54,\n",
    "    'dropout': 0.,\n",
    "    'batch_size': 6,\n",
    "    'epochs': 2,\n",
    "    'lr': 2e-5,\n",
    "    'weight_decay': 0.01,\n",
    "    'warmup_steps' : 100,\n",
    "    'device': 'cuda' if torch.cuda.is_available() else 'cpu',\n",
    "    'tokenizer' :{\n",
    "        'max_length': 512,\n",
    "        'truncation':True\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591f11b1-9640-4fb1-a04e-4af22af75f8c",
   "metadata": {
    "id": "591f11b1-9640-4fb1-a04e-4af22af75f8c"
   },
   "source": [
    "# 4. Create Datasets and Dataloaders\n",
    "- set the classes for the dataset module\n",
    "- define the tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "295da403-f8ea-44e2-aa83-b606d13e93dd",
   "metadata": {
    "id": "295da403-f8ea-44e2-aa83-b606d13e93dd"
   },
   "outputs": [],
   "source": [
    "class TextDataset:\n",
    "    def __init__(self, df, config, classes=None, tokenizer=None, is_test=False):\n",
    "        self.df = df.reset_index(drop=True)\n",
    "        self.classes = classes\n",
    "        self.max_len = config['tokenizer']['max_length']\n",
    "        self.truncation = config['tokenizer']['truncation']\n",
    "        self.tokenizer = tokenizer\n",
    "        self.is_test = is_test\n",
    "        \n",
    "    def __getitem__(self,idx):\n",
    "        sample = self.df['full_text'][idx]\n",
    "        tokenized = tokenizer.encode_plus(sample,\n",
    "                                          None,\n",
    "                                          add_special_tokens=True,\n",
    "                                          max_length=self.max_len,\n",
    "                                          truncation=self.truncation,\n",
    "                                          padding='max_length'\n",
    "                                         )\n",
    "        inputs = {\n",
    "            \"input_ids\": torch.tensor(tokenized['input_ids'], dtype=torch.long),\n",
    "            #\"token_type_ids\": torch.tensor(tokenized['token_type_ids'], dtype=torch.long),\n",
    "            \"attention_mask\": torch.tensor(tokenized['attention_mask'], dtype=torch.long)\n",
    "        }\n",
    "        \n",
    "        if self.is_test == True:\n",
    "            return inputs\n",
    "        \n",
    "        label = self.df.loc[idx,self.classes].to_list()\n",
    "        \n",
    "        '''NEED to check this for correct output'''\n",
    "        inputs[\"labels\"] = torch.tensor(label, dtype=torch.long)\n",
    "        \n",
    "        return inputs\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf87134e-921d-4c2f-86e3-156ac673ecf6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bf87134e-921d-4c2f-86e3-156ac673ecf6",
    "outputId": "0ddaa6e8-075e-4d4e-e80c-27d64c5d5a84"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-uncased/snapshots/1c4513b2eedbda136f57676a34eea67aba266e5c/config.json\n",
      "Model config DistilBertConfig {\n",
      "  \"_name_or_path\": \"distilbert-base-uncased\",\n",
      "  \"activation\": \"gelu\",\n",
      "  \"architectures\": [\n",
      "    \"DistilBertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.1,\n",
      "  \"dim\": 768,\n",
      "  \"dropout\": 0.1,\n",
      "  \"hidden_dim\": 3072,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"distilbert\",\n",
      "  \"n_heads\": 12,\n",
      "  \"n_layers\": 6,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"qa_dropout\": 0.1,\n",
      "  \"seq_classif_dropout\": 0.2,\n",
      "  \"sinusoidal_pos_embds\": false,\n",
      "  \"tie_weights_\": true,\n",
      "  \"transformers_version\": \"4.24.0\",\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--distilbert-base-uncased/snapshots/1c4513b2eedbda136f57676a34eea67aba266e5c/vocab.txt\n",
      "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-uncased/snapshots/1c4513b2eedbda136f57676a34eea67aba266e5c/tokenizer.json\n",
      "loading file added_tokens.json from cache at None\n",
      "loading file special_tokens_map.json from cache at None\n",
      "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-uncased/snapshots/1c4513b2eedbda136f57676a34eea67aba266e5c/tokenizer_config.json\n",
      "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-uncased/snapshots/1c4513b2eedbda136f57676a34eea67aba266e5c/config.json\n",
      "Model config DistilBertConfig {\n",
      "  \"_name_or_path\": \"distilbert-base-uncased\",\n",
      "  \"activation\": \"gelu\",\n",
      "  \"architectures\": [\n",
      "    \"DistilBertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.1,\n",
      "  \"dim\": 768,\n",
      "  \"dropout\": 0.1,\n",
      "  \"hidden_dim\": 3072,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"distilbert\",\n",
      "  \"n_heads\": 12,\n",
      "  \"n_layers\": 6,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"qa_dropout\": 0.1,\n",
      "  \"seq_classif_dropout\": 0.2,\n",
      "  \"sinusoidal_pos_embds\": false,\n",
      "  \"tie_weights_\": true,\n",
      "  \"transformers_version\": \"4.24.0\",\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(config['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bc54a5f-1a65-4339-8282-34fbb89b0bad",
   "metadata": {
    "id": "0bc54a5f-1a65-4339-8282-34fbb89b0bad"
   },
   "outputs": [],
   "source": [
    "train_ds = TextDataset(train, config, classes= y_train.columns, tokenizer=tokenizer)\n",
    "test_ds = TextDataset(test, config, tokenizer=tokenizer, is_test=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471dc259-a06e-40f8-bd1c-87080629533f",
   "metadata": {
    "id": "471dc259-a06e-40f8-bd1c-87080629533f"
   },
   "source": [
    "# 5. Define the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a1c292b-442b-40a7-a954-b5fbf69acf4f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8a1c292b-442b-40a7-a954-b5fbf69acf4f",
    "outputId": "8bb53667-81fd-4247-e5bf-12ee8170be9c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-uncased/snapshots/1c4513b2eedbda136f57676a34eea67aba266e5c/config.json\n",
      "Model config DistilBertConfig {\n",
      "  \"_name_or_path\": \"distilbert-base-uncased\",\n",
      "  \"activation\": \"gelu\",\n",
      "  \"architectures\": [\n",
      "    \"DistilBertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.1,\n",
      "  \"dim\": 768,\n",
      "  \"dropout\": 0.1,\n",
      "  \"hidden_dim\": 3072,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"LABEL_0\",\n",
      "    \"1\": \"LABEL_1\",\n",
      "    \"2\": \"LABEL_2\",\n",
      "    \"3\": \"LABEL_3\",\n",
      "    \"4\": \"LABEL_4\",\n",
      "    \"5\": \"LABEL_5\",\n",
      "    \"6\": \"LABEL_6\",\n",
      "    \"7\": \"LABEL_7\",\n",
      "    \"8\": \"LABEL_8\",\n",
      "    \"9\": \"LABEL_9\",\n",
      "    \"10\": \"LABEL_10\",\n",
      "    \"11\": \"LABEL_11\",\n",
      "    \"12\": \"LABEL_12\",\n",
      "    \"13\": \"LABEL_13\",\n",
      "    \"14\": \"LABEL_14\",\n",
      "    \"15\": \"LABEL_15\",\n",
      "    \"16\": \"LABEL_16\",\n",
      "    \"17\": \"LABEL_17\",\n",
      "    \"18\": \"LABEL_18\",\n",
      "    \"19\": \"LABEL_19\",\n",
      "    \"20\": \"LABEL_20\",\n",
      "    \"21\": \"LABEL_21\",\n",
      "    \"22\": \"LABEL_22\",\n",
      "    \"23\": \"LABEL_23\",\n",
      "    \"24\": \"LABEL_24\",\n",
      "    \"25\": \"LABEL_25\",\n",
      "    \"26\": \"LABEL_26\",\n",
      "    \"27\": \"LABEL_27\",\n",
      "    \"28\": \"LABEL_28\",\n",
      "    \"29\": \"LABEL_29\",\n",
      "    \"30\": \"LABEL_30\",\n",
      "    \"31\": \"LABEL_31\",\n",
      "    \"32\": \"LABEL_32\",\n",
      "    \"33\": \"LABEL_33\",\n",
      "    \"34\": \"LABEL_34\",\n",
      "    \"35\": \"LABEL_35\",\n",
      "    \"36\": \"LABEL_36\",\n",
      "    \"37\": \"LABEL_37\",\n",
      "    \"38\": \"LABEL_38\",\n",
      "    \"39\": \"LABEL_39\",\n",
      "    \"40\": \"LABEL_40\",\n",
      "    \"41\": \"LABEL_41\",\n",
      "    \"42\": \"LABEL_42\",\n",
      "    \"43\": \"LABEL_43\",\n",
      "    \"44\": \"LABEL_44\",\n",
      "    \"45\": \"LABEL_45\",\n",
      "    \"46\": \"LABEL_46\",\n",
      "    \"47\": \"LABEL_47\",\n",
      "    \"48\": \"LABEL_48\",\n",
      "    \"49\": \"LABEL_49\",\n",
      "    \"50\": \"LABEL_50\",\n",
      "    \"51\": \"LABEL_51\",\n",
      "    \"52\": \"LABEL_52\",\n",
      "    \"53\": \"LABEL_53\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"label2id\": {\n",
      "    \"LABEL_0\": 0,\n",
      "    \"LABEL_1\": 1,\n",
      "    \"LABEL_10\": 10,\n",
      "    \"LABEL_11\": 11,\n",
      "    \"LABEL_12\": 12,\n",
      "    \"LABEL_13\": 13,\n",
      "    \"LABEL_14\": 14,\n",
      "    \"LABEL_15\": 15,\n",
      "    \"LABEL_16\": 16,\n",
      "    \"LABEL_17\": 17,\n",
      "    \"LABEL_18\": 18,\n",
      "    \"LABEL_19\": 19,\n",
      "    \"LABEL_2\": 2,\n",
      "    \"LABEL_20\": 20,\n",
      "    \"LABEL_21\": 21,\n",
      "    \"LABEL_22\": 22,\n",
      "    \"LABEL_23\": 23,\n",
      "    \"LABEL_24\": 24,\n",
      "    \"LABEL_25\": 25,\n",
      "    \"LABEL_26\": 26,\n",
      "    \"LABEL_27\": 27,\n",
      "    \"LABEL_28\": 28,\n",
      "    \"LABEL_29\": 29,\n",
      "    \"LABEL_3\": 3,\n",
      "    \"LABEL_30\": 30,\n",
      "    \"LABEL_31\": 31,\n",
      "    \"LABEL_32\": 32,\n",
      "    \"LABEL_33\": 33,\n",
      "    \"LABEL_34\": 34,\n",
      "    \"LABEL_35\": 35,\n",
      "    \"LABEL_36\": 36,\n",
      "    \"LABEL_37\": 37,\n",
      "    \"LABEL_38\": 38,\n",
      "    \"LABEL_39\": 39,\n",
      "    \"LABEL_4\": 4,\n",
      "    \"LABEL_40\": 40,\n",
      "    \"LABEL_41\": 41,\n",
      "    \"LABEL_42\": 42,\n",
      "    \"LABEL_43\": 43,\n",
      "    \"LABEL_44\": 44,\n",
      "    \"LABEL_45\": 45,\n",
      "    \"LABEL_46\": 46,\n",
      "    \"LABEL_47\": 47,\n",
      "    \"LABEL_48\": 48,\n",
      "    \"LABEL_49\": 49,\n",
      "    \"LABEL_5\": 5,\n",
      "    \"LABEL_50\": 50,\n",
      "    \"LABEL_51\": 51,\n",
      "    \"LABEL_52\": 52,\n",
      "    \"LABEL_53\": 53,\n",
      "    \"LABEL_6\": 6,\n",
      "    \"LABEL_7\": 7,\n",
      "    \"LABEL_8\": 8,\n",
      "    \"LABEL_9\": 9\n",
      "  },\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"distilbert\",\n",
      "  \"n_heads\": 12,\n",
      "  \"n_layers\": 6,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"qa_dropout\": 0.1,\n",
      "  \"seq_classif_dropout\": 0.2,\n",
      "  \"sinusoidal_pos_embds\": false,\n",
      "  \"tie_weights_\": true,\n",
      "  \"transformers_version\": \"4.24.0\",\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--distilbert-base-uncased/snapshots/1c4513b2eedbda136f57676a34eea67aba266e5c/pytorch_model.bin\n",
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.bias', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(config[\"model\"], num_labels=config[\"num_classes\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7703c76-9e7e-4095-bb6d-3896e4f849c8",
   "metadata": {
    "id": "b7703c76-9e7e-4095-bb6d-3896e4f849c8"
   },
   "source": [
    "# 6. Model Training\n",
    "\n",
    "https://huggingface.co/transformers/v3.2.0/custom_datasets.html\n",
    "https://colab.research.google.com/github/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb#scrollTo=I4R39UTxNKTk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "TZEILPZkam91",
   "metadata": {
    "id": "TZEILPZkam91"
   },
   "outputs": [],
   "source": [
    "class CustomTrainer(Trainer):\n",
    "    def compute_loss(self, model, inputs, return_outputs=False):\n",
    "        outputs = model(\n",
    "            input_ids=inputs['input_ids'],\n",
    "            attention_mask=inputs['attention_mask'],\n",
    "            #token_type_ids=inputs['token_type_ids']\n",
    "        )\n",
    "        loss = nn.BCEWithLogitsLoss()(outputs['logits'],\n",
    "                                          inputs['labels'].float())\n",
    "        return (loss, outputs) if return_outputs else loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97d09327-1d92-4853-957f-5541e0c2c279",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "97d09327-1d92-4853-957f-5541e0c2c279",
    "outputId": "47380aab-f93b-4288-928b-f52a9a30718f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir='./results',         \n",
    "    num_train_epochs=config['epochs'],              \n",
    "    per_device_train_batch_size=config['batch_size'],  \n",
    "    per_device_eval_batch_size=config['batch_size'],   \n",
    "    warmup_steps=config['warmup_steps'],               \n",
    "    weight_decay=config['weight_decay'],              \n",
    ")\n",
    "\n",
    "trainer = CustomTrainer(\n",
    "    model=model,                         \n",
    "    args=training_args,                  \n",
    "    train_dataset=train_ds,         \n",
    "    eval_dataset=test_ds           \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32b7c1ba-7a96-4796-86f4-e178e88b99ad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 557
    },
    "id": "32b7c1ba-7a96-4796-86f4-e178e88b99ad",
    "outputId": "be236b33-b0c6-4218-ffc7-d1df5e2f8a8b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 3128\n",
      "  Num Epochs = 2\n",
      "  Instantaneous batch size per device = 6\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 6\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1044\n",
      "  Number of trainable parameters = 66994998\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1044' max='1044' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1044/1044 05:25, Epoch 2/2]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.331200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.257700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to ./results/checkpoint-500\n",
      "Configuration saved in ./results/checkpoint-500/config.json\n",
      "Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n",
      "Saving model checkpoint to ./results/checkpoint-1000\n",
      "Configuration saved in ./results/checkpoint-1000/config.json\n",
      "Model weights saved in ./results/checkpoint-1000/pytorch_model.bin\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1044, training_loss=0.29242026760203627, metrics={'train_runtime': 326.2927, 'train_samples_per_second': 19.173, 'train_steps_per_second': 3.2, 'total_flos': 829484552650752.0, 'train_loss': 0.29242026760203627, 'epoch': 2.0})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Pr637FZUepcY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pr637FZUepcY",
    "outputId": "ca59b4a6-a145-4ea1-9e6a-ac74523badbd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to trained_text_model\n",
      "Configuration saved in trained_text_model/config.json\n",
      "Model weights saved in trained_text_model/pytorch_model.bin\n"
     ]
    }
   ],
   "source": [
    "trainer.save_model(\"trained_text_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53f0bf01-5aa0-4708-aa57-f04d9a0457fd",
   "metadata": {
    "id": "53f0bf01-5aa0-4708-aa57-f04d9a0457fd"
   },
   "source": [
    "# 7. Check Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6904cd3-e10c-4e53-bd3e-1d210b509248",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "b6904cd3-e10c-4e53-bd3e-1d210b509248",
    "outputId": "c5654a83-fe89-4683-c83b-98b97caa6185"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file ./trained_text_model/config.json\n",
      "Model config DistilBertConfig {\n",
      "  \"_name_or_path\": \"./trained_text_model\",\n",
      "  \"activation\": \"gelu\",\n",
      "  \"architectures\": [\n",
      "    \"DistilBertForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.1,\n",
      "  \"dim\": 768,\n",
      "  \"dropout\": 0.1,\n",
      "  \"hidden_dim\": 3072,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"LABEL_0\",\n",
      "    \"1\": \"LABEL_1\",\n",
      "    \"2\": \"LABEL_2\",\n",
      "    \"3\": \"LABEL_3\",\n",
      "    \"4\": \"LABEL_4\",\n",
      "    \"5\": \"LABEL_5\",\n",
      "    \"6\": \"LABEL_6\",\n",
      "    \"7\": \"LABEL_7\",\n",
      "    \"8\": \"LABEL_8\",\n",
      "    \"9\": \"LABEL_9\",\n",
      "    \"10\": \"LABEL_10\",\n",
      "    \"11\": \"LABEL_11\",\n",
      "    \"12\": \"LABEL_12\",\n",
      "    \"13\": \"LABEL_13\",\n",
      "    \"14\": \"LABEL_14\",\n",
      "    \"15\": \"LABEL_15\",\n",
      "    \"16\": \"LABEL_16\",\n",
      "    \"17\": \"LABEL_17\",\n",
      "    \"18\": \"LABEL_18\",\n",
      "    \"19\": \"LABEL_19\",\n",
      "    \"20\": \"LABEL_20\",\n",
      "    \"21\": \"LABEL_21\",\n",
      "    \"22\": \"LABEL_22\",\n",
      "    \"23\": \"LABEL_23\",\n",
      "    \"24\": \"LABEL_24\",\n",
      "    \"25\": \"LABEL_25\",\n",
      "    \"26\": \"LABEL_26\",\n",
      "    \"27\": \"LABEL_27\",\n",
      "    \"28\": \"LABEL_28\",\n",
      "    \"29\": \"LABEL_29\",\n",
      "    \"30\": \"LABEL_30\",\n",
      "    \"31\": \"LABEL_31\",\n",
      "    \"32\": \"LABEL_32\",\n",
      "    \"33\": \"LABEL_33\",\n",
      "    \"34\": \"LABEL_34\",\n",
      "    \"35\": \"LABEL_35\",\n",
      "    \"36\": \"LABEL_36\",\n",
      "    \"37\": \"LABEL_37\",\n",
      "    \"38\": \"LABEL_38\",\n",
      "    \"39\": \"LABEL_39\",\n",
      "    \"40\": \"LABEL_40\",\n",
      "    \"41\": \"LABEL_41\",\n",
      "    \"42\": \"LABEL_42\",\n",
      "    \"43\": \"LABEL_43\",\n",
      "    \"44\": \"LABEL_44\",\n",
      "    \"45\": \"LABEL_45\",\n",
      "    \"46\": \"LABEL_46\",\n",
      "    \"47\": \"LABEL_47\",\n",
      "    \"48\": \"LABEL_48\",\n",
      "    \"49\": \"LABEL_49\",\n",
      "    \"50\": \"LABEL_50\",\n",
      "    \"51\": \"LABEL_51\",\n",
      "    \"52\": \"LABEL_52\",\n",
      "    \"53\": \"LABEL_53\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"label2id\": {\n",
      "    \"LABEL_0\": 0,\n",
      "    \"LABEL_1\": 1,\n",
      "    \"LABEL_10\": 10,\n",
      "    \"LABEL_11\": 11,\n",
      "    \"LABEL_12\": 12,\n",
      "    \"LABEL_13\": 13,\n",
      "    \"LABEL_14\": 14,\n",
      "    \"LABEL_15\": 15,\n",
      "    \"LABEL_16\": 16,\n",
      "    \"LABEL_17\": 17,\n",
      "    \"LABEL_18\": 18,\n",
      "    \"LABEL_19\": 19,\n",
      "    \"LABEL_2\": 2,\n",
      "    \"LABEL_20\": 20,\n",
      "    \"LABEL_21\": 21,\n",
      "    \"LABEL_22\": 22,\n",
      "    \"LABEL_23\": 23,\n",
      "    \"LABEL_24\": 24,\n",
      "    \"LABEL_25\": 25,\n",
      "    \"LABEL_26\": 26,\n",
      "    \"LABEL_27\": 27,\n",
      "    \"LABEL_28\": 28,\n",
      "    \"LABEL_29\": 29,\n",
      "    \"LABEL_3\": 3,\n",
      "    \"LABEL_30\": 30,\n",
      "    \"LABEL_31\": 31,\n",
      "    \"LABEL_32\": 32,\n",
      "    \"LABEL_33\": 33,\n",
      "    \"LABEL_34\": 34,\n",
      "    \"LABEL_35\": 35,\n",
      "    \"LABEL_36\": 36,\n",
      "    \"LABEL_37\": 37,\n",
      "    \"LABEL_38\": 38,\n",
      "    \"LABEL_39\": 39,\n",
      "    \"LABEL_4\": 4,\n",
      "    \"LABEL_40\": 40,\n",
      "    \"LABEL_41\": 41,\n",
      "    \"LABEL_42\": 42,\n",
      "    \"LABEL_43\": 43,\n",
      "    \"LABEL_44\": 44,\n",
      "    \"LABEL_45\": 45,\n",
      "    \"LABEL_46\": 46,\n",
      "    \"LABEL_47\": 47,\n",
      "    \"LABEL_48\": 48,\n",
      "    \"LABEL_49\": 49,\n",
      "    \"LABEL_5\": 5,\n",
      "    \"LABEL_50\": 50,\n",
      "    \"LABEL_51\": 51,\n",
      "    \"LABEL_52\": 52,\n",
      "    \"LABEL_53\": 53,\n",
      "    \"LABEL_6\": 6,\n",
      "    \"LABEL_7\": 7,\n",
      "    \"LABEL_8\": 8,\n",
      "    \"LABEL_9\": 9\n",
      "  },\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"distilbert\",\n",
      "  \"n_heads\": 12,\n",
      "  \"n_layers\": 6,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"qa_dropout\": 0.1,\n",
      "  \"seq_classif_dropout\": 0.2,\n",
      "  \"sinusoidal_pos_embds\": false,\n",
      "  \"tie_weights_\": true,\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.24.0\",\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "loading weights file ./trained_text_model/pytorch_model.bin\n",
      "All model checkpoint weights were used when initializing DistilBertForSequenceClassification.\n",
      "\n",
      "All the weights of DistilBertForSequenceClassification were initialized from the model checkpoint at ./trained_text_model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForSequenceClassification for predictions without further training.\n",
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "***** Running Prediction *****\n",
      "  Num examples = 783\n",
      "  Batch size = 6\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loading the model you previously trained\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"./trained_text_model\")\n",
    "\n",
    "# arguments for Trainer\n",
    "test_args = TrainingArguments(\n",
    "    output_dir = \".\",\n",
    "    do_train = False,\n",
    "    do_predict = True,\n",
    "    per_device_eval_batch_size = config[\"batch_size\"],   \n",
    "    dataloader_drop_last = False    \n",
    ")\n",
    "\n",
    "# init trainer\n",
    "trainer = Trainer(\n",
    "              model = model, \n",
    "              args = test_args\n",
    "              )\n",
    "\n",
    "test_results = trainer.predict(test_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "543752f0-7b27-4b9d-bb9c-30ad418cbfce",
   "metadata": {
    "id": "543752f0-7b27-4b9d-bb9c-30ad418cbfce"
   },
   "outputs": [],
   "source": [
    "mapping = {0:1.0, 1:1.5, 2:2.0, 3:2.5, 4:3.0, 5:3.5, 6:4.0, 7:4.5, 8:5.0,}\n",
    "\n",
    "preds = [[mapping[pos] for pos in np.argmax(i.reshape(6,9), axis=1)] for i in test_results[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "BfzdSyCIhciC",
   "metadata": {
    "id": "BfzdSyCIhciC"
   },
   "outputs": [],
   "source": [
    "labels = test[['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "LkEsQYHOl635",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "LkEsQYHOl635",
    "outputId": "925012d9-1d54-46b6-9672-67eced2b8e1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f6b170e9750>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(np.sum((preds-labels)**2, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e7tR0mbmpaJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "9e7tR0mbmpaJ",
    "outputId": "c601719a-f477-44a2-feb7-89ace04668e7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "targets = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']\n",
    "preds_df = pd.DataFrame(preds, columns = targets).astype(\"category\")\n",
    "\n",
    "fig, axes = plt.subplots(2, len(targets)//2, figsize=(15,6))\n",
    "\n",
    "for i, target in enumerate(targets):\n",
    "    ax = axes.flat[i]\n",
    "    sns.histplot(x=target, data=test, alpha=0.3, ax=ax, color=\"blue\")\n",
    "    sns.histplot(x=target, data=preds_df, alpha=0.3, ax=ax, color=\"orange\")\n",
    "    ax.set_title(target)\n",
    "    ax.set(xlabel=None, ylabel=None)\n",
    "    \n",
    "fig.suptitle('Output Variables')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "FwrG3P74nufK",
   "metadata": {
    "id": "FwrG3P74nufK"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}