train_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "g12vf7oIy24W"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import datetime\n",
    "import random\n",
    "import re\n",
    "import transformers\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from torch.optim import AdamW\n",
    "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler,random_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from transformers import RobertaForSequenceClassification, get_linear_schedule_with_warmup, RobertaTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 275,
     "status": "ok",
     "timestamp": 1697187238877,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "U3RgfzNYgWqO",
    "outputId": "a1b9e1e5-3b9e-4842-e26a-b19750678bea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "pDc9GsT6gpQp"
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"data/codesearchnet_auto.pkl\")\n",
    "len(df)\n",
    "# export the data to a csv file\n",
    "df.to_csv('data/codesearchnet_auto.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_manual = pd.read_pickle(\"data/codesearchnet_manual.pkl\")\n",
    "len(df_manual)\n",
    "# export the data to a csv file\n",
    "df_manual.to_csv('data/codesearchnet_manual.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'repo', 'path', 'url', 'code', 'code_tokens', 'docstring',\n",
      "       'docstring_tokens', 'language', 'partition', 'cosmic_function',\n",
      "       'cosmic_function_number', 'includes_cosmic_function',\n",
      "       'includes_cosmic_function_number'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAIN TEST SPLIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['code'], df['cosmic_function_number'], \n",
    "                                                    test_size=0.2, random_state=42,\n",
    "                                                   stratify=df['cosmic_function_number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data={'code': X_train, 'cosmic_function_number': y_train}).reset_index(drop=True)\n",
    "test_df = pd.DataFrame(data={'code': X_test, 'cosmic_function_number': y_test}).reset_index(drop=True)\n",
    "manual_df = pd.DataFrame(data={'code': df_manual['code'], 'cosmic_function_number': df_manual['cosmic_function_number']}).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>cosmic_function_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>def show_progress(self):\\n        \"\"\"If we are...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>def send_command(self, cmd, sudo=False, stderr...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>def _parse_openssl_req(csr_filename):\\n    '''...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>def load_env(keys=[], name='NT', use_getpass=F...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>def get_setup_version(reponame):\\n    \"\"\"\\n   ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3515</th>\n",
       "      <td>def _combine_w0614(self, messages):\\n        \"...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3516</th>\n",
       "      <td>def read_block(self, block):\\n        \"\"\"Read ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3517</th>\n",
       "      <td>def _append_bytes(self, value, _file):  # pyli...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3518</th>\n",
       "      <td>def get_item(self):\\n        \"\"\"Returns the it...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3519</th>\n",
       "      <td>def get_kernel_message(self, timeout=None, str...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3520 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   code  \\\n",
       "0     def show_progress(self):\\n        \"\"\"If we are...   \n",
       "1     def send_command(self, cmd, sudo=False, stderr...   \n",
       "2     def _parse_openssl_req(csr_filename):\\n    '''...   \n",
       "3     def load_env(keys=[], name='NT', use_getpass=F...   \n",
       "4     def get_setup_version(reponame):\\n    \"\"\"\\n   ...   \n",
       "...                                                 ...   \n",
       "3515  def _combine_w0614(self, messages):\\n        \"...   \n",
       "3516  def read_block(self, block):\\n        \"\"\"Read ...   \n",
       "3517  def _append_bytes(self, value, _file):  # pyli...   \n",
       "3518  def get_item(self):\\n        \"\"\"Returns the it...   \n",
       "3519  def get_kernel_message(self, timeout=None, str...   \n",
       "\n",
       "      cosmic_function_number  \n",
       "0                          0  \n",
       "1                          2  \n",
       "2                          3  \n",
       "3                          3  \n",
       "4                          3  \n",
       "...                      ...  \n",
       "3515                       3  \n",
       "3516                       1  \n",
       "3517                       0  \n",
       "3518                       2  \n",
       "3519                       3  \n",
       "\n",
       "[3520 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>cosmic_function_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>def process_data(self, fname, tokenizer, max_s...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>def get(self, name, default=None):\\n        ''...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>def log_all(self, file):\\n        \"\"\"Log all d...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>def on_consumer_cancelled(self, method_frame):...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>def open_video(specifier):\\n    \"\"\"Return an O...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>def get_nodedata(self, sort_names=False):\\n   ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>def main():\\n    \"\"\"\\n    Example application ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>def get_or_set_hash(uri,\\n        length=8,\\n ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>def to_epw(self, buffer_or_path=None):\\n      ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>def _default_plugins(self):\\n        \"\"\" Get e...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>880 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  code  cosmic_function_number\n",
       "0    def process_data(self, fname, tokenizer, max_s...                       1\n",
       "1    def get(self, name, default=None):\\n        ''...                       3\n",
       "2    def log_all(self, file):\\n        \"\"\"Log all d...                       1\n",
       "3    def on_consumer_cancelled(self, method_frame):...                       2\n",
       "4    def open_video(specifier):\\n    \"\"\"Return an O...                       1\n",
       "..                                                 ...                     ...\n",
       "875  def get_nodedata(self, sort_names=False):\\n   ...                       3\n",
       "876  def main():\\n    \"\"\"\\n    Example application ...                       2\n",
       "877  def get_or_set_hash(uri,\\n        length=8,\\n ...                       0\n",
       "878  def to_epw(self, buffer_or_path=None):\\n      ...                       0\n",
       "879  def _default_plugins(self):\\n        \"\"\" Get e...                       1\n",
       "\n",
       "[880 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>cosmic_function_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>def remove_old_message():\\n    \"\"\"\\n    Remove...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>def open_stream(stream):\\n    \"\"\"Opens a strea...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>def read_json(filename, mode='r'):\\n    '''rea...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>def get_sample_trim(p1_data, p2_data):\\n    \"\"...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>def get_data(self):\\n        \"\"\"\\n        retu...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>def _send(self):\\n        \"\"\"\\n        Convert...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>async def reply_voice(self, voice: typing.Unio...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>async def send_voice(self, chat_id: typing.Uni...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>def send(self, *args, **kwargs):\\n        \"\"\"A...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>def send_with_options(self, *, args=None, kwar...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  code  cosmic_function_number\n",
       "0    def remove_old_message():\\n    \"\"\"\\n    Remove...                       1\n",
       "1    def open_stream(stream):\\n    \"\"\"Opens a strea...                       1\n",
       "2    def read_json(filename, mode='r'):\\n    '''rea...                       1\n",
       "3    def get_sample_trim(p1_data, p2_data):\\n    \"\"...                       1\n",
       "4    def get_data(self):\\n        \"\"\"\\n        retu...                       1\n",
       "..                                                 ...                     ...\n",
       "395  def _send(self):\\n        \"\"\"\\n        Convert...                       2\n",
       "396  async def reply_voice(self, voice: typing.Unio...                       2\n",
       "397  async def send_voice(self, chat_id: typing.Uni...                       2\n",
       "398  def send(self, *args, **kwargs):\\n        \"\"\"A...                       2\n",
       "399  def send_with_options(self, *, args=None, kwar...                       2\n",
       "\n",
       "[400 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(manual_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "iifwFcLE-J1S"
   },
   "outputs": [],
   "source": [
    "REMOVE_COMMENTS = True\n",
    "COMMENTS_POSTFIX = \"with_comments\" if not REMOVE_COMMENTS else \"without_comments\"\n",
    "\n",
    "def dedoc(a):\n",
    "    # remove docstrings (between triple quotes or triple double quotes)\n",
    "    a = re.sub(r'\"\"\"(.|\\n)*?\"\"\"', '', a)\n",
    "    a = re.sub(r\"'''(.|\\n)*?'''\", '', a)\n",
    "    if REMOVE_COMMENTS:\n",
    "        a = re.sub(r'#.*', '', a)\n",
    "    return a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 336,
     "status": "ok",
     "timestamp": 1697187264512,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "L6bBePuu-fGK",
    "outputId": "15aab63d-6a04-4158-b913-6791eeaf202b"
   },
   "outputs": [],
   "source": [
    "codes = df.code.apply(dedoc).values\n",
    "test_codes = test_df.code.apply(dedoc).values\n",
    "manual_codes = manual_df.code.apply(dedoc).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177,
     "referenced_widgets": [
      "9f6a5e5a4c974bfab7137ddc6c448380",
      "d6ff3ca1638645999d253dacd9290da5",
      "551c165cc7cc40e39b45d24e4f1e18d5",
      "37f78b40feb54aea9ea0ad800a8d14e8",
      "d0dae16ac9a04e4282c73356440eccea",
      "ff48807053a1421693b97ac48ed937db",
      "113762b8bffc4260a0a34661597ea50b",
      "98c5d3a77df64a93b8972932e993df1b",
      "0693cc24be794558a940d318106d0ea2",
      "b322f96f267641478ef1f89c33a78304",
      "8c18e09740ac4c9e9bbf5f7a20794c08",
      "d54eeffb6ccf4b5d97ef46df33fc5e7a",
      "4a7d2869b0954c09840c5022a78db0ee",
      "13eb51033e50421aa74f0bd24b9573e1",
      "a56b3c52307d4e63949c30f5caf97a81",
      "dbb6f3f67bcf43b98b3427b2d56239be",
      "0586ea854c1c44289c1131568a865f54",
      "337aede2ebe444c4b45b541cbe66ca26",
      "b5c995a5c5924f98bcec705de6dd4651",
      "6bcf159fa38a47e394829bd5433498ce",
      "5d3d6b1502d740baa008e649cafe0d22",
      "1a87d9e7e94e4cb4bb6d47373a644de0",
      "a483c907e8174259b1db12048b2c64ac",
      "0ace5c14604c4cf9a3c8bd4deef0bc64",
      "5659821348674eacb61d7cec3c5d0843",
      "0137cad595e647499c16d3bfe8d0e0e1",
      "c01516ac71f448bf83c20c240ce9d306",
      "5110289ae4324b7788ec33d73eafa0df",
      "ee44a318f13f47f58c78e743a7215b54",
      "fed555945b2546bda52fdf8b6357e6e5",
      "139e62cc973e4eaf89e1429fc31a2e35",
      "3bd8d366db8b4df49d471ca4f8e7a849",
      "f2f7cb749a7f46be981af89dd44b47e7",
      "e1d72867090e4d22ada633d0fd82ca29",
      "1c274c939e3d447db8f8ecd0590279bc",
      "267ccebd9fd648b58458c9f37c3910ed",
      "b3eb4455eb7f4ce9ad02776caefab58c",
      "d98a56732ca84848b621b6cc9c47b89f",
      "1f9acf8722a64d4784dcc56f8bcd983b",
      "e86f8cfabe2c4345a226f2cb927e2a7f",
      "170fa55c0ff648798584b72a3d4bfe86",
      "c78c3abccaa34d49abc0dd5f3c602b56",
      "a0d6bc669f4d4ef5873523ec9b6ee332",
      "90518c1d822c4d108d6759aa870b1a3c",
      "6de370de035f4e95aea891f1243beb59",
      "da989b020a6c4ce9bfe803fa654c3858",
      "05cbc80a2a1d45f5a90c0e8994f2655a",
      "404356094c194aeca44732e5bfbf20cd",
      "4e483c66c00f442cb9360de8422d05d1",
      "55762bdb8024488ca7e1dce11909aa44",
      "1f8af38604084cfc867d0643c8860be5",
      "356df375e9c441b2a3569fa3db4c652d",
      "39661d00c6c44f08bf6c0bd6c222cc25",
      "401972209de84cb1840c1a9b01bcf5aa",
      "724096a9c5d245f8a29cade46d33500e"
     ]
    },
    "executionInfo": {
     "elapsed": 1379,
     "status": "ok",
     "timestamp": 1697187271060,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "iN52N7LFiHdN",
    "outputId": "3cf11342-7d8c-4870-bac5-34c1f97da1cc"
   },
   "outputs": [],
   "source": [
    "tokenizer = RobertaTokenizer.from_pretrained(\"microsoft/codebert-base\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "V069svIGoKQX"
   },
   "outputs": [],
   "source": [
    "labels = torch.from_numpy(df.cosmic_function_number.values)\n",
    "test_labels = torch.from_numpy(test_df.cosmic_function_number.values)\n",
    "manual_labels = torch.from_numpy(manual_df.cosmic_function_number.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs that exceed max limit: 77\n"
     ]
    }
   ],
   "source": [
    "max_len = 0\n",
    "n=0\n",
    "for code in manual_codes:\n",
    "\n",
    "    # Tokenize the text and add `[CLS]` and `[SEP]` tokens.\n",
    "    input_ids = tokenizer.encode(code, add_special_tokens=False, max_length=512, truncation=True)\n",
    "\n",
    "    # Update the maximum sentence length.\n",
    "    max_len = max(max_len, len(input_ids))\n",
    "    if len(input_ids) == 512:\n",
    "        n+=1\n",
    "\n",
    "print('Inputs that exceed max limit:', n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5479,
     "status": "ok",
     "timestamp": 1697187336497,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "T7Je1yvQdu-r",
    "outputId": "aec7ac96-612c-485b-e027-ad529a7a6858"
   },
   "outputs": [],
   "source": [
    "input_ids = []\n",
    "attention_masks = []\n",
    "\n",
    "# For every tweet...\n",
    "for code in codes:\n",
    "    # `encode_plus` will:\n",
    "    #   (1) Tokenize the sentence.\n",
    "    #   (2) Prepend the `[CLS]` token to the start.\n",
    "    #   (3) Append the `[SEP]` token to the end.\n",
    "    #   (4) Map tokens to their IDs.\n",
    "    #   (5) Pad or truncate the sentence to `max_length`\n",
    "    #   (6) Create attention masks for [PAD] tokens.\n",
    "    encoded_dict = tokenizer.encode_plus(\n",
    "                        code,                      # Sentence to encode.\n",
    "                        add_special_tokens = False, # Add '[CLS]' and '[SEP]'\n",
    "                        padding = 'max_length',\n",
    "                        return_attention_mask = True,   # Construct attn. masks.\n",
    "                        return_tensors = 'pt',     # Return pytorch tensors.\n",
    "                   )\n",
    "\n",
    "    # Add the encoded sentence to the list.\n",
    "    input_ids.append(encoded_dict['input_ids'])\n",
    "\n",
    "    # And its attention mask (simply differentiates padding from non-padding).\n",
    "    attention_masks.append(encoded_dict['attention_mask'])\n",
    "\n",
    "# Convert the lists into tensors.\n",
    "input_ids = torch.cat(input_ids, dim=0)\n",
    "attention_masks = torch.cat(attention_masks, dim=0)\n",
    "labels = labels.clone().detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 378,
     "status": "ok",
     "timestamp": 1697187346865,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "JYe5sH9enJkD",
    "outputId": "43b15017-f33e-4938-a508-3fbba34fc9e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3520, 512])\n"
     ]
    }
   ],
   "source": [
    "print(input_ids.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 258,
     "status": "ok",
     "timestamp": 1697187349338,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "1alQvyp8fKEt",
    "outputId": "c7e02e91-c3b6-41ad-b9c2-a522c4079cab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,816 training samples\n",
      "  704 validation samples\n"
     ]
    }
   ],
   "source": [
    "# Combine the training inputs into a TensorDataset.\n",
    "dataset = TensorDataset(input_ids, attention_masks, labels)\n",
    "\n",
    "# Create a 90-10 train-validation split.\n",
    "\n",
    "# Calculate the number of samples to include in each set.\n",
    "train_size = int(0.8 * len(dataset))\n",
    "#val_size = int(0.2 * len(dataset))\n",
    "val_size = len(dataset)  - train_size\n",
    "\n",
    "# Divide the dataset by randomly selecting samples.\n",
    "train_dataset, val_dataset = random_split(dataset, [train_size, val_size])\n",
    "\n",
    "print('{:>5,} training samples'.format(train_size))\n",
    "print('{:>5,} validation samples'.format(val_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "iRIgfANFhFjk"
   },
   "outputs": [],
   "source": [
    "# The DataLoader needs to know our batch size for training, so we specify it\n",
    "# here. For fine-tuning BERT on a specific task, the authors recommend a batch\n",
    "# size of 16 or 32.\n",
    "batch_size = 64\n",
    "\n",
    "# Create the DataLoaders for our training and validation sets.\n",
    "# We'll take training samples in random order.\n",
    "train_dataloader = DataLoader(\n",
    "            train_dataset,  # The training samples.\n",
    "            sampler = RandomSampler(train_dataset), # Select batches randomly\n",
    "            batch_size = batch_size # Trains with this batch size.\n",
    "        )\n",
    "\n",
    "# For validation the order doesn't matter, so we'll just read them sequentially.\n",
    "validation_dataloader = DataLoader(\n",
    "            val_dataset, # The validation samples.\n",
    "            sampler = SequentialSampler(val_dataset), # Pull out batches sequentially.\n",
    "            batch_size = batch_size # Evaluate with this batch size.\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 103,
     "referenced_widgets": [
      "fb4d961d058042e495ecc5eaddba0e96",
      "a036053ad54741cb862c1c438b660d4f",
      "aa228d6d7f8743f7b302b3482ac73325",
      "c6558dc03cb54dc398b9252dbba9b51b",
      "06c07f482ed242c4a946280c3a773dc0",
      "2d64fa07f41442cd9bad0d825ffafc61",
      "4769117ffbd74a68bfdf0fe4d5e03af6",
      "9060d79581c44b23bd05091d99c157ff",
      "d49427252fb249e0b5c741867b6cb15f",
      "284e7cbf4fd346c3b37cd37a64185186",
      "b4225ed36ee441ef88bba2c93b9276de"
     ]
    },
    "executionInfo": {
     "elapsed": 13938,
     "status": "ok",
     "timestamp": 1697187369863,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "1xGDKz1ohMnk",
    "outputId": "c7b307a4-4309-408c-b862-1c095d419611"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/codebert-base were not used when initializing RobertaForSequenceClassification: ['pooler.dense.weight', 'pooler.dense.bias']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at microsoft/codebert-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.out_proj.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": [
    "# Load the pretrained codebert model with a single linear classification layer on top.\n",
    "model = RobertaForSequenceClassification.from_pretrained(\n",
    "    \"microsoft/codebert-base\", # Use the 12-layer BERT model, with an uncased vocab.\n",
    "    num_labels = 4, # The number of output labels=2 for binary classification.\n",
    "                    # You can increase this for multi-class tasks.\n",
    "    output_attentions = False, # Whether the model returns attentions weights.\n",
    "    output_hidden_states = False, # Whether the model returns all hidden-states.\n",
    ")\n",
    "\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 236,
     "status": "ok",
     "timestamp": 1697187372366,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "bVYFC7RvVtg0",
    "outputId": "9b0ab559-af8d-4049-8ee4-96c0d418deb5"
   },
   "outputs": [],
   "source": [
    "optimizer = AdamW(model.parameters(),\n",
    "                  lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5\n",
    "                  eps = 1e-8 # args.adam_epsilon  - default is 1e-8.\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "id": "mKqMf7H1Vzwu"
   },
   "outputs": [],
   "source": [
    "# Number of training epochs. The BERT authors recommend between 2 and 4.\n",
    "# We chose to run for 4, but we'll see later that this may be over-fitting the\n",
    "# training data.\n",
    "epochs = 5\n",
    "\n",
    "# Total number of training steps is [number of batches] x [number of epochs].\n",
    "# (Note that this is not the same as the number of training samples).\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "\n",
    "# Create the learning rate scheduler.\n",
    "scheduler = get_linear_schedule_with_warmup(optimizer,\n",
    "                                            num_warmup_steps = 0, # Default value in run_glue.py\n",
    "                                            num_training_steps = total_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "id": "ehUHKroHV7Og"
   },
   "outputs": [],
   "source": [
    "# Function to calculate the accuracy of our predictions vs labels\n",
    "def flat_accuracy(preds, labels):\n",
    "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
    "    labels_flat = labels.flatten()\n",
    "    return np.sum(pred_flat == labels_flat) / len(labels_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "CAkeOHolV86z"
   },
   "outputs": [],
   "source": [
    "def format_time(elapsed):\n",
    "    '''\n",
    "    Takes a time in seconds and returns a string hh:mm:ss\n",
    "    '''\n",
    "    # Round to the nearest second.\n",
    "    elapsed_rounded = int(round((elapsed)))\n",
    "    # Format as hh:mm:ss\n",
    "    return str(datetime.timedelta(seconds=elapsed_rounded))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RUN TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "sEaFV8kjV_-V",
    "outputId": "a6c144f7-6ade-4f18-c2b4-68163f14408a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======== Epoch 1 of 5 ========\n",
      "Training...\n",
      "\n",
      "  Average training loss: 1.34\n",
      "  Training epoch took: 0:00:49\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.54\n",
      "\n",
      "======== Epoch 2 of 5 ========\n",
      "Training...\n",
      "\n",
      "  Average training loss: 0.87\n",
      "  Training epoch took: 0:00:52\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.73\n",
      "\n",
      "======== Epoch 3 of 5 ========\n",
      "Training...\n",
      "\n",
      "  Average training loss: 0.68\n",
      "  Training epoch took: 0:00:53\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.73\n",
      "\n",
      "======== Epoch 4 of 5 ========\n",
      "Training...\n",
      "\n",
      "  Average training loss: 0.61\n",
      "  Training epoch took: 0:00:53\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.75\n",
      "\n",
      "======== Epoch 5 of 5 ========\n",
      "Training...\n",
      "\n",
      "  Average training loss: 0.56\n",
      "  Training epoch took: 0:00:53\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.74\n",
      "\n",
      "Training complete!\n",
      "Total training took 0:04:47 (h:mm:ss)\n"
     ]
    }
   ],
   "source": [
    "seed_val = 42\n",
    "random.seed(seed_val)\n",
    "np.random.seed(seed_val)\n",
    "torch.manual_seed(seed_val)\n",
    "torch.cuda.manual_seed_all(seed_val)\n",
    "training_stats = []\n",
    "\n",
    "# Create empty csv to write training statistics\n",
    "with open(f'training_stats_{COMMENTS_POSTFIX}.csv', 'w') as f:\n",
    "    f.write('epoch,TrainLoss,ValidLoss,ValidAcc,TrainTime,ValidTime\\n')\n",
    "\n",
    "# Measure the total training time for the whole run.\n",
    "total_t0 = time.time()\n",
    "\n",
    "# For each epoch...\n",
    "for epoch_i in range(0, epochs):\n",
    "\n",
    "    # ========================================\n",
    "    #               Training\n",
    "    # ========================================\n",
    "    # Perform one full pass over the training set.\n",
    "    print('\\n======== Epoch {:} of {:} ========'.format(epoch_i + 1, epochs))\n",
    "    print('Training...')\n",
    "    # Measure how long the training epoch takes.\n",
    "    t0 = time.time()\n",
    "    total_train_loss = 0\n",
    "    model.train()\n",
    "    for step, batch in enumerate(train_dataloader):\n",
    "        # Unpack this training batch from our dataloader.\n",
    "        #\n",
    "        # As we unpack the batch, we'll also copy each tensor to the device using the\n",
    "        # `to` method.\n",
    "        #\n",
    "        # `batch` contains three pytorch tensors:\n",
    "        #   [0]: input ids\n",
    "        #   [1]: attention masks\n",
    "        #   [2]: labels\n",
    "        b_input_ids = batch[0].to(device)\n",
    "        b_input_mask = batch[1].to(device)\n",
    "        b_labels = batch[2].to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(b_input_ids,\n",
    "                             token_type_ids=None,\n",
    "                             attention_mask=b_input_mask,\n",
    "                             labels=b_labels)\n",
    "        loss = output.loss\n",
    "        total_train_loss += loss.item()\n",
    "        # Perform a backward pass to calculate the gradients.\n",
    "        loss.backward()\n",
    "        # Clip the norm of the gradients to 1.0.\n",
    "        # This is to help prevent the \"exploding gradients\" problem.\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "        # Update parameters and take a step using the computed gradient.\n",
    "        # The optimizer dictates the \"update rule\"--how the parameters are\n",
    "        # modified based on their gradients, the learning rate, etc.\n",
    "        optimizer.step()\n",
    "        # Update the learning rate.\n",
    "        scheduler.step()\n",
    "        #print(step)\n",
    "\n",
    "    # Calculate the average loss over all of the batches.\n",
    "    avg_train_loss = total_train_loss / len(train_dataloader)\n",
    "\n",
    "    # Measure how long this epoch took.\n",
    "    training_time = format_time(time.time() - t0)\n",
    "\n",
    "    print(\"\\n  Average training loss: {0:.2f}\".format(avg_train_loss))\n",
    "    print(\"  Training epoch took: {:}\".format(training_time))\n",
    "    # ========================================\n",
    "    #               Validation\n",
    "    # ========================================\n",
    "    # After the completion of each training epoch, measure our performance on  our validation set.\n",
    "    print(\"\\nRunning Validation...\")\n",
    "    t0 = time.time()\n",
    "    # Put the model in evaluation mode--the dropout layers behave differently during evaluation.\n",
    "    model.eval()\n",
    "    # Tracking variables\n",
    "    total_eval_accuracy = 0\n",
    "    best_eval_accuracy = 0\n",
    "    total_eval_loss = 0\n",
    "    nb_eval_steps = 0\n",
    "    # Evaluate data for one epoch\n",
    "    for batch in validation_dataloader:\n",
    "        b_input_ids = batch[0].to(device)\n",
    "        b_input_mask = batch[1].to(device)\n",
    "        b_labels = batch[2].to(device)\n",
    "        # Tell pytorch not to bother with constructing the compute graph during\n",
    "        # the forward pass, since this is only needed for backprop (training).\n",
    "        with torch.no_grad():\n",
    "            output= model(b_input_ids,\n",
    "                                   token_type_ids=None,\n",
    "                                   attention_mask=b_input_mask,\n",
    "                                   labels=b_labels)\n",
    "        loss = output.loss\n",
    "        total_eval_loss += loss.item()\n",
    "        # Move logits and labels to CPU if we are using GPU\n",
    "        logits = output.logits\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        label_ids = b_labels.to('cpu').numpy()\n",
    "        # Calculate the accuracy for this batch of test sentences, and accumulate it over all batches.\n",
    "        total_eval_accuracy += flat_accuracy(logits, label_ids)\n",
    "    # Report the final accuracy for this validation run.\n",
    "    avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n",
    "    print(\"  Accuracy: {0:.2f}\".format(avg_val_accuracy))\n",
    "    # Calculate the average loss over all of the batches.\n",
    "    avg_val_loss = total_eval_loss / len(validation_dataloader)\n",
    "    # Measure how long the validation run took.\n",
    "    validation_time = format_time(time.time() - t0)\n",
    "    if avg_val_accuracy > best_eval_accuracy:\n",
    "        torch.save(model, f'checkpoints/codebert_best_model_{COMMENTS_POSTFIX}.pt')\n",
    "        best_eval_accuracy = avg_val_accuracy\n",
    "\n",
    "    # Record all statistics from this epoch.\n",
    "    training_stats.append(\n",
    "        {\n",
    "            'epoch': epoch_i + 1,\n",
    "            'TrainLoss': avg_train_loss,\n",
    "            'ValidLoss': avg_val_loss,\n",
    "            'ValidAcc': avg_val_accuracy,\n",
    "            'TrainTime': training_time,\n",
    "            'ValidTime': validation_time\n",
    "        }\n",
    "    )\n",
    "    # Append the training statistics to the CSV file\n",
    "    with open(f'training_stats_{COMMENTS_POSTFIX}.csv', 'a') as f:\n",
    "        f.write('results/{},{:.4f},{:.4f},{:.4f},{},{}\\n'.format(epoch_i + 1, avg_train_loss, \n",
    "                                                                 avg_val_loss, avg_val_accuracy, \n",
    "                                                                 training_time, validation_time))\n",
    "\n",
    "print(\"\\nTraining complete!\")\n",
    "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAD AND TEST TRAINED MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "vIeZj3GlwC6D"
   },
   "outputs": [],
   "source": [
    "model = torch.load(f'checkpoints/codebert_best_model_{COMMENTS_POSTFIX}.pt')\n",
    "selected_codes = manual_codes\n",
    "selected_labels = manual_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1000,
     "status": "ok",
     "timestamp": 1697144989110,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "2oaMtP1uce16",
    "outputId": "53f745d3-5eeb-4e48-b5ba-6e390580d8f9"
   },
   "outputs": [],
   "source": [
    "test_input_ids = []\n",
    "test_attention_masks = []\n",
    "for code in selected_codes:\n",
    "    encoded_dict = tokenizer.encode_plus(\n",
    "                        code,\n",
    "                        add_special_tokens = True,\n",
    "                        # max_length = max_len,\n",
    "                        padding = 'max_length',\n",
    "                        return_attention_mask = True,\n",
    "                        return_tensors = 'pt',\n",
    "                        truncation = True\n",
    "                   )\n",
    "    test_input_ids.append(encoded_dict['input_ids'])\n",
    "    test_attention_masks.append(encoded_dict['attention_mask'])\n",
    "test_input_ids = torch.cat(test_input_ids, dim=0)\n",
    "test_attention_masks = torch.cat(test_attention_masks, dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "id": "QGJ3iV6fcjzT"
   },
   "outputs": [],
   "source": [
    "test_dataset = TensorDataset(test_input_ids, test_attention_masks)\n",
    "test_dataloader = DataLoader(\n",
    "            test_dataset, # The validation samples.\n",
    "            sampler = SequentialSampler(test_dataset), # Pull out batches sequentially.\n",
    "            batch_size = batch_size # Evaluate with this batch size.\n",
    "        )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GET PREDICTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "zIP3MzVdcmK3"
   },
   "outputs": [],
   "source": [
    "predictions = []\n",
    "for batch in test_dataloader:\n",
    "        b_input_ids = batch[0].to(device)\n",
    "        b_input_mask = batch[1].to(device)\n",
    "        with torch.no_grad():\n",
    "            output= model(b_input_ids,\n",
    "                                   token_type_ids=None,\n",
    "                                   attention_mask=b_input_mask)\n",
    "            logits = output.logits\n",
    "            logits = logits.detach().cpu().numpy()\n",
    "            pred_flat = np.argmax(logits, axis=1).flatten()\n",
    "\n",
    "            predictions.extend(list(pred_flat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "Dzc_ZZP0coUu"
   },
   "outputs": [],
   "source": [
    "df_output = pd.DataFrame()\n",
    "df_output['code'] = selected_codes\n",
    "df_output['label'] = selected_labels\n",
    "df_output['prediction'] = predictions\n",
    "df_output.to_csv(f'results/predictions_{len(df_output)}_{COMMENTS_POSTFIX}.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1697144941396,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "2qsecbOpgpam",
    "outputId": "94629238-7f4e-425b-fa18-478851da1bb1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>label</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>def remove_old_message():\\n    \\n    global g_...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>def open_stream(stream):\\n    \\n    global str...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>def read_json(filename, mode='r'):\\n    \\n    ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>def get_sample_trim(p1_data, p2_data):\\n    \\n...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>def get_data(self):\\n        \\n        with se...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>def _send(self):\\n        \\n        while len(...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>async def reply_voice(self, voice: typing.Unio...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>async def send_voice(self, chat_id: typing.Uni...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>def send(self, *args, **kwargs):\\n        \\n  ...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>def send_with_options(self, *, args=None, kwar...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  code  label  prediction\n",
       "0    def remove_old_message():\\n    \\n    global g_...      1           3\n",
       "1    def open_stream(stream):\\n    \\n    global str...      1           1\n",
       "2    def read_json(filename, mode='r'):\\n    \\n    ...      1           1\n",
       "3    def get_sample_trim(p1_data, p2_data):\\n    \\n...      1           3\n",
       "4    def get_data(self):\\n        \\n        with se...      1           1\n",
       "..                                                 ...    ...         ...\n",
       "395  def _send(self):\\n        \\n        while len(...      2           2\n",
       "396  async def reply_voice(self, voice: typing.Unio...      2           2\n",
       "397  async def send_voice(self, chat_id: typing.Uni...      2           2\n",
       "398  def send(self, *args, **kwargs):\\n        \\n  ...      2           2\n",
       "399  def send_with_options(self, *, args=None, kwar...      2           2\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 263,
     "status": "ok",
     "timestamp": 1697145016282,
     "user": {
      "displayName": "emre dikenelli",
      "userId": "17966046540608968956"
     },
     "user_tz": -180
    },
    "id": "f4UAJZcqeVuk",
    "outputId": "7eb10a45-c7b6-4366-c1d2-69554ecc0784"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.845\n"
     ]
    }
   ],
   "source": [
    "acc = np.sum(df_output['label'] == df_output['prediction']) / len(df_output['label'])\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           W     0.8800    0.8800    0.8800       100\n",
      "           R     0.8065    0.7500    0.7772       100\n",
      "           X     0.8796    0.9500    0.9135       100\n",
      "           E     0.8081    0.8000    0.8040       100\n",
      "\n",
      "    accuracy                         0.8450       400\n",
      "   macro avg     0.8435    0.8450    0.8437       400\n",
      "weighted avg     0.8435    0.8450    0.8437       400\n",
      "\n",
      "[[88 10  1  1]\n",
      " [ 7 75  5 13]\n",
      " [ 0  0 95  5]\n",
      " [ 5  8  7 80]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "target_names = ['W', 'R', 'X', 'E']\n",
    "print(classification_report(df_output['label'], df_output['prediction'], target_names=target_names, digits=4))\n",
    "# write to txt file\n",
    "with open(f'results/classification_report_{len(df_output)}_{COMMENTS_POSTFIX}.txt', 'w') as f:\n",
    "    f.write(classification_report(df_output['label'], df_output['prediction'], target_names=target_names, digits=4))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "print(confusion_matrix(df_output['label'], df_output['prediction']))\n",
    "# append to txt file\n",
    "with open(f'results/classification_report_{len(df_output)}_{COMMENTS_POSTFIX}.txt', 'a') as f:\n",
    "    f.write(str(confusion_matrix(df_output['label'], df_output['prediction'])))\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[88 10  1  1]\n",
      " [ 7 75  5 13]\n",
      " [ 0  0 95  5]\n",
      " [ 5  8  7 80]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import numpy as np\n",
    "\n",
    "# read matrix from classification report txt file\n",
    "\n",
    "with open(f'results/classification_report_{len(df_output)}_{COMMENTS_POSTFIX}.txt', 'r') as f:\n",
    "    file_content = f.read()\n",
    "    # get text between [[ and ]]\n",
    "    matrix = file_content[file_content.find('[[')+1:file_content.find(']]')+1]\n",
    "    # remove [ and ]\n",
    "    matrix = matrix.replace('[', '').replace(']', '')\n",
    "    # split by space or multiple spaces\n",
    "    matrix = re.split(r'\\s+', matrix)\n",
    "    # convert to numpy matrix\n",
    "    matrix = np.array(matrix).astype(int).reshape(4, 4)\n",
    "    \n",
    "print(matrix)\n",
    "target_names = ['W', 'R', 'X', 'E']\n",
    "\n",
    "# set font size\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.title('Manually Labeled')\n",
    "sns.heatmap(matrix, annot=True, fmt='d', cmap='Blues', xticklabels=target_names, yticklabels=target_names)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.savefig(f'results/confusion_matrix_{len(df_output)}_{COMMENTS_POSTFIX}.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyP1uLuNLyLbTerPXZwlMv5E",
   "mount_file_id": "1q57Bj47VkNrtLhLyQRKz6t0AbH7wpQ-b",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.16"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "0137cad595e647499c16d3bfe8d0e0e1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_3bd8d366db8b4df49d471ca4f8e7a849",
      "placeholder": "​",
      "style": "IPY_MODEL_f2f7cb749a7f46be981af89dd44b47e7",
      "value": " 456k/456k [00:00&lt;00:00, 16.3MB/s]"
     }
    },
    "0586ea854c1c44289c1131568a865f54": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "05cbc80a2a1d45f5a90c0e8994f2655a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_356df375e9c441b2a3569fa3db4c652d",
      "max": 498,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_39661d00c6c44f08bf6c0bd6c222cc25",
      "value": 498
     }
    },
    "0693cc24be794558a940d318106d0ea2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "06c07f482ed242c4a946280c3a773dc0": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "0ace5c14604c4cf9a3c8bd4deef0bc64": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_5110289ae4324b7788ec33d73eafa0df",
      "placeholder": "​",
      "style": "IPY_MODEL_ee44a318f13f47f58c78e743a7215b54",
      "value": "Downloading (…)olve/main/merges.txt: 100%"
     }
    },
    "113762b8bffc4260a0a34661597ea50b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "139e62cc973e4eaf89e1429fc31a2e35": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "13eb51033e50421aa74f0bd24b9573e1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_b5c995a5c5924f98bcec705de6dd4651",
      "max": 898822,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_6bcf159fa38a47e394829bd5433498ce",
      "value": 898822
     }
    },
    "170fa55c0ff648798584b72a3d4bfe86": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "1a87d9e7e94e4cb4bb6d47373a644de0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "1c274c939e3d447db8f8ecd0590279bc": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_1f9acf8722a64d4784dcc56f8bcd983b",
      "placeholder": "​",
      "style": "IPY_MODEL_e86f8cfabe2c4345a226f2cb927e2a7f",
      "value": "Downloading (…)cial_tokens_map.json: 100%"
     }
    },
    "1f8af38604084cfc867d0643c8860be5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "1f9acf8722a64d4784dcc56f8bcd983b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "267ccebd9fd648b58458c9f37c3910ed": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_170fa55c0ff648798584b72a3d4bfe86",
      "max": 150,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_c78c3abccaa34d49abc0dd5f3c602b56",
      "value": 150
     }
    },
    "284e7cbf4fd346c3b37cd37a64185186": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "2d64fa07f41442cd9bad0d825ffafc61": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "337aede2ebe444c4b45b541cbe66ca26": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "356df375e9c441b2a3569fa3db4c652d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "37f78b40feb54aea9ea0ad800a8d14e8": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_b322f96f267641478ef1f89c33a78304",
      "placeholder": "​",
      "style": "IPY_MODEL_8c18e09740ac4c9e9bbf5f7a20794c08",
      "value": " 25.0/25.0 [00:00&lt;00:00, 1.58kB/s]"
     }
    },
    "39661d00c6c44f08bf6c0bd6c222cc25": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "3bd8d366db8b4df49d471ca4f8e7a849": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "401972209de84cb1840c1a9b01bcf5aa": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "404356094c194aeca44732e5bfbf20cd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_401972209de84cb1840c1a9b01bcf5aa",
      "placeholder": "​",
      "style": "IPY_MODEL_724096a9c5d245f8a29cade46d33500e",
      "value": " 498/498 [00:00&lt;00:00, 32.2kB/s]"
     }
    },
    "4769117ffbd74a68bfdf0fe4d5e03af6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "4a7d2869b0954c09840c5022a78db0ee": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_0586ea854c1c44289c1131568a865f54",
      "placeholder": "​",
      "style": "IPY_MODEL_337aede2ebe444c4b45b541cbe66ca26",
      "value": "Downloading (…)olve/main/vocab.json: 100%"
     }
    },
    "4e483c66c00f442cb9360de8422d05d1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "5110289ae4324b7788ec33d73eafa0df": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "551c165cc7cc40e39b45d24e4f1e18d5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_98c5d3a77df64a93b8972932e993df1b",
      "max": 25,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_0693cc24be794558a940d318106d0ea2",
      "value": 25
     }
    },
    "55762bdb8024488ca7e1dce11909aa44": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "5659821348674eacb61d7cec3c5d0843": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_fed555945b2546bda52fdf8b6357e6e5",
      "max": 456318,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_139e62cc973e4eaf89e1429fc31a2e35",
      "value": 456318
     }
    },
    "5d3d6b1502d740baa008e649cafe0d22": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6bcf159fa38a47e394829bd5433498ce": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "6de370de035f4e95aea891f1243beb59": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_da989b020a6c4ce9bfe803fa654c3858",
       "IPY_MODEL_05cbc80a2a1d45f5a90c0e8994f2655a",
       "IPY_MODEL_404356094c194aeca44732e5bfbf20cd"
      ],
      "layout": "IPY_MODEL_4e483c66c00f442cb9360de8422d05d1"
     }
    },
    "724096a9c5d245f8a29cade46d33500e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "8c18e09740ac4c9e9bbf5f7a20794c08": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "90518c1d822c4d108d6759aa870b1a3c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "9060d79581c44b23bd05091d99c157ff": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "98c5d3a77df64a93b8972932e993df1b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9f6a5e5a4c974bfab7137ddc6c448380": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_d6ff3ca1638645999d253dacd9290da5",
       "IPY_MODEL_551c165cc7cc40e39b45d24e4f1e18d5",
       "IPY_MODEL_37f78b40feb54aea9ea0ad800a8d14e8"
      ],
      "layout": "IPY_MODEL_d0dae16ac9a04e4282c73356440eccea"
     }
    },
    "a036053ad54741cb862c1c438b660d4f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_2d64fa07f41442cd9bad0d825ffafc61",
      "placeholder": "​",
      "style": "IPY_MODEL_4769117ffbd74a68bfdf0fe4d5e03af6",
      "value": "Downloading pytorch_model.bin: 100%"
     }
    },
    "a0d6bc669f4d4ef5873523ec9b6ee332": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a483c907e8174259b1db12048b2c64ac": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_0ace5c14604c4cf9a3c8bd4deef0bc64",
       "IPY_MODEL_5659821348674eacb61d7cec3c5d0843",
       "IPY_MODEL_0137cad595e647499c16d3bfe8d0e0e1"
      ],
      "layout": "IPY_MODEL_c01516ac71f448bf83c20c240ce9d306"
     }
    },
    "a56b3c52307d4e63949c30f5caf97a81": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_5d3d6b1502d740baa008e649cafe0d22",
      "placeholder": "​",
      "style": "IPY_MODEL_1a87d9e7e94e4cb4bb6d47373a644de0",
      "value": " 899k/899k [00:00&lt;00:00, 9.55MB/s]"
     }
    },
    "aa228d6d7f8743f7b302b3482ac73325": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_9060d79581c44b23bd05091d99c157ff",
      "max": 498627950,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_d49427252fb249e0b5c741867b6cb15f",
      "value": 498627950
     }
    },
    "b322f96f267641478ef1f89c33a78304": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b3eb4455eb7f4ce9ad02776caefab58c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_a0d6bc669f4d4ef5873523ec9b6ee332",
      "placeholder": "​",
      "style": "IPY_MODEL_90518c1d822c4d108d6759aa870b1a3c",
      "value": " 150/150 [00:00&lt;00:00, 8.51kB/s]"
     }
    },
    "b4225ed36ee441ef88bba2c93b9276de": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b5c995a5c5924f98bcec705de6dd4651": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c01516ac71f448bf83c20c240ce9d306": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c6558dc03cb54dc398b9252dbba9b51b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_284e7cbf4fd346c3b37cd37a64185186",
      "placeholder": "​",
      "style": "IPY_MODEL_b4225ed36ee441ef88bba2c93b9276de",
      "value": " 499M/499M [00:04&lt;00:00, 37.2MB/s]"
     }
    },
    "c78c3abccaa34d49abc0dd5f3c602b56": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "d0dae16ac9a04e4282c73356440eccea": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d49427252fb249e0b5c741867b6cb15f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "d54eeffb6ccf4b5d97ef46df33fc5e7a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_4a7d2869b0954c09840c5022a78db0ee",
       "IPY_MODEL_13eb51033e50421aa74f0bd24b9573e1",
       "IPY_MODEL_a56b3c52307d4e63949c30f5caf97a81"
      ],
      "layout": "IPY_MODEL_dbb6f3f67bcf43b98b3427b2d56239be"
     }
    },
    "d6ff3ca1638645999d253dacd9290da5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ff48807053a1421693b97ac48ed937db",
      "placeholder": "​",
      "style": "IPY_MODEL_113762b8bffc4260a0a34661597ea50b",
      "value": "Downloading (…)okenizer_config.json: 100%"
     }
    },
    "d98a56732ca84848b621b6cc9c47b89f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "da989b020a6c4ce9bfe803fa654c3858": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_55762bdb8024488ca7e1dce11909aa44",
      "placeholder": "​",
      "style": "IPY_MODEL_1f8af38604084cfc867d0643c8860be5",
      "value": "Downloading (…)lve/main/config.json: 100%"
     }
    },
    "dbb6f3f67bcf43b98b3427b2d56239be": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e1d72867090e4d22ada633d0fd82ca29": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_1c274c939e3d447db8f8ecd0590279bc",
       "IPY_MODEL_267ccebd9fd648b58458c9f37c3910ed",
       "IPY_MODEL_b3eb4455eb7f4ce9ad02776caefab58c"
      ],
      "layout": "IPY_MODEL_d98a56732ca84848b621b6cc9c47b89f"
     }
    },
    "e86f8cfabe2c4345a226f2cb927e2a7f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "ee44a318f13f47f58c78e743a7215b54": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "f2f7cb749a7f46be981af89dd44b47e7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "fb4d961d058042e495ecc5eaddba0e96": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_a036053ad54741cb862c1c438b660d4f",
       "IPY_MODEL_aa228d6d7f8743f7b302b3482ac73325",
       "IPY_MODEL_c6558dc03cb54dc398b9252dbba9b51b"
      ],
      "layout": "IPY_MODEL_06c07f482ed242c4a946280c3a773dc0"
     }
    },
    "fed555945b2546bda52fdf8b6357e6e5": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ff48807053a1421693b97ac48ed937db": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}