{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis with Hugging Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hugging Face is an open-source and platform provider of machine learning technologies. You can use install their package to access some interesting pre-built models to use them directly or to fine-tune (retrain it on your dataset leveraging the prior knowledge coming with the first training), then host your trained models on the platform, so that you may use them later on other devices and apps.\n",
    "\n",
    "Please, [go to the website and sign-in](https://huggingface.co/) to access all the features of the platform.\n",
    "\n",
    "[Read more about Text classification with Hugging Face](https://huggingface.co/tasks/text-classification)\n",
    "\n",
    "The Hugging face models are Deep Learning based, so will need a lot of computational GPU power to train them. Please use [Colab](https://colab.research.google.com/) to do it, or your other GPU cloud provider, or a local machine having NVIDIA GPU."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application of Hugging Face Text classification model Fune-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find below a simple example, with just `3 epochs of fine-tuning`. \n",
    "\n",
    "Read more about the fine-tuning concept : [here](https://deeplizard.com/learn/video/5T-iXNNiwIs#:~:text=Fine%2Dtuning%20is%20a%20way,perform%20a%20second%20similar%20task.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install the datasets library\n",
    "# !pip install datasets\n",
    "# !pip install sentencepiece\n",
    "# !pip install transformers datasets\n",
    "# !pip install transformers[torch]\n",
    "# !pip install accelerate\n",
    "# !pip install accelerate>=0.20.1\n",
    "# !pip install huggingface_hub\n",
    "# !pip install  -q transformers datasets\n",
    "# !pip install neattext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from datasets import load_dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "from wordcloud import WordCloud\n",
    "import neattext.functions as nfx\n",
    "import re\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Disabe W&B\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LOADING DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset and display some values\n",
    "df_train = pd.read_csv('../data/Train.csv')\n",
    "\n",
    "# A way to eliminate rows containing NaN values\n",
    "df_train = df_train[~df_train.isna().any(axis=1)]\n",
    "\n",
    "\n",
    "# Load the dataset and display some values\n",
    "df_test = pd.read_csv('../data/Test.csv')\n",
    "\n",
    "# A way to eliminate rows containing NaN values\n",
    "df_test = df_test[~df_test.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "##creating a copy\n",
    "\n",
    "train_data= df_train.copy()\n",
    "test_data= df_test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CRISP-DM Framework\n",
    "\n",
    "- Data Understanding\n",
    "- Data Preparation\n",
    "- Modelling\n",
    "- Evaluation\n",
    "- Deployment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DATA UNDERSTANDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### EXPLORATORY DATA ANALYSIS (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>tweet_id</th>\n",
       "      <th>safe_text</th>\n",
       "      <th>label</th>\n",
       "      <th>agreement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3445</th>\n",
       "      <td>5UIMWY4K</td>\n",
       "      <td>AMERICANS, We make a big issue about a vaccine...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7399</th>\n",
       "      <td>O9OYIGHR</td>\n",
       "      <td>To the Parent of the Unvaccinated Child Who Ex...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8884</th>\n",
       "      <td>3GBNQ2TR</td>\n",
       "      <td>So, If I don't vaccinate my dog, does that mea...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2358</th>\n",
       "      <td>ZRO6XU62</td>\n",
       "      <td>.&lt;user&gt; slays, always has. Vaccinate your ding...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9753</th>\n",
       "      <td>T9PATKBB</td>\n",
       "      <td>“&lt;user&gt; The new and final season of Parks &amp;amp...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tweet_id                                          safe_text  label  \\\n",
       "3445  5UIMWY4K  AMERICANS, We make a big issue about a vaccine...    0.0   \n",
       "7399  O9OYIGHR  To the Parent of the Unvaccinated Child Who Ex...    1.0   \n",
       "8884  3GBNQ2TR  So, If I don't vaccinate my dog, does that mea...    1.0   \n",
       "2358  ZRO6XU62  .<user> slays, always has. Vaccinate your ding...    1.0   \n",
       "9753  T9PATKBB  “<user> The new and final season of Parks &amp...    0.0   \n",
       "\n",
       "      agreement  \n",
       "3445   0.666667  \n",
       "7399   1.000000  \n",
       "8884   1.000000  \n",
       "2358   0.666667  \n",
       "9753   1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>tweet_id</th>\n",
       "      <th>safe_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>FBITB56E</td>\n",
       "      <td>&lt;user&gt; I've heard that vaccines make you artis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2097</th>\n",
       "      <td>EK6LT2QV</td>\n",
       "      <td>“&lt;user&gt; Vaccines Save Lives: We welcome new re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4578</th>\n",
       "      <td>VLRBBGY6</td>\n",
       "      <td>Brayden: \"people are always scared of somethin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>DWPYUSLL</td>\n",
       "      <td>&lt;user&gt; Back to 8th grade!! MMR probably likes it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147</th>\n",
       "      <td>LQG1280L</td>\n",
       "      <td>Outbreaks Fuel a Renewed Push for Vaccinations...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tweet_id                                          safe_text\n",
       "2210  FBITB56E  <user> I've heard that vaccines make you artis...\n",
       "2097  EK6LT2QV  “<user> Vaccines Save Lives: We welcome new re...\n",
       "4578  VLRBBGY6  Brayden: \"people are always scared of somethin...\n",
       "2012  DWPYUSLL   <user> Back to 8th grade!! MMR probably likes it\n",
       "3147  LQG1280L  Outbreaks Fuel a Renewed Push for Vaccinations..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9999 entries, 0 to 10000\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   tweet_id   9999 non-null   object \n",
      " 1   safe_text  9999 non-null   object \n",
      " 2   label      9999 non-null   float64\n",
      " 3   agreement  9999 non-null   float64\n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 390.6+ KB\n",
      "the info df_train dataset are:  \n",
      "\n",
      " None \n",
      "\n",
      " ------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5176 entries, 0 to 5176\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   tweet_id   5176 non-null   object\n",
      " 1   safe_text  5176 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 121.3+ KB\n",
      "the info df_test dataset are:  \n",
      "\n",
      " None \n",
      "\n",
      " ------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "data=[train_data, test_data]\n",
    "names=[\"df_train\", \"df_test\"]\n",
    "\n",
    "for m, i in zip(data, names):\n",
    "  print(f\"the info\", i,\"dataset are: \", \"\\n\\n\", m.info(), \"\\n\\n\", \"---\"*20 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.0    4908\n",
       " 1.0    4053\n",
       "-1.0    1038\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We look at the number of positive, negative and neutral reviews\n",
    "train_data.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of labels\n",
    "label_counts = train_data['label'].value_counts()\n",
    "plt.bar(label_counts.index, label_counts.values)\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000    5866\n",
       "0.666667    3894\n",
       "0.333333     239\n",
       "Name: agreement, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The count of the agrremtns\n",
    "train_data.agreement.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of 'agreement'\n",
    "plt.hist(train_data['agreement'])\n",
    "plt.xlabel('Agreement')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Agreement')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of sentiments in the dataset, as depicted by the count plot, shows the prevalence of different sentiment labels within the Twitter posts related to COVID-19 vaccinations.\n",
    "* Sentiment Label 0 (Neutral):\n",
    "The sentiment label \"0\" (neutral) has the highest count, with approximately 5000 instances. This suggests that a significant portion of the collected tweets exhibit a neutral sentiment when it comes to discussing COVID-19 vaccinations. Neutral sentiments often indicate that the tweets may not strongly express positive or negative opinions but rather present factual information or observations.\n",
    "\n",
    "* Sentiment Label 1 (Positive):\n",
    "The sentiment label \"1\" (positive) follows with around 4000 instances. This indicates that a substantial number of tweets show a positive sentiment towards COVID-19 vaccinations. These tweets might express support for vaccinations, share positive experiences, or provide information about vaccination availability and benefits.\n",
    "\n",
    "* Sentiment Label -1 (Negative):\n",
    "The sentiment label \"-1\" (negative) has the lowest count, with approximately 1000 instances. This suggests that a relatively smaller portion of the collected tweets exhibit a negative sentiment towards COVID-19 vaccinations. Negative sentiments can encompass concerns, skepticism, or criticism about the vaccines, their safety, or potential side effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.1381547908758799\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation between 'label' and 'agreement'\n",
    "correlation = df_train['label'].corr(df_train['agreement'])\n",
    "\n",
    "# Print the correlation value\n",
    "print(f\"Correlation: {correlation}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max review_legnth : 154\n",
      "min review_legnth : 3\n"
     ]
    }
   ],
   "source": [
    "#Checking the length of the reviews \n",
    "review_legnth = train_data.safe_text.str.len()\n",
    "\n",
    "max(review_legnth)\n",
    "\n",
    "#Legnth of the shortest review\n",
    "min(review_legnth)\n",
    "\n",
    "print(f\"max review_legnth : {max(review_legnth)}\")\n",
    "print(f\"min review_legnth : {min(review_legnth)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('<url>', 4612), ('<user>', 4517), ('to', 3407), ('the', 3388), ('of', 2196), ('a', 2133), ('in', 1897), ('and', 1827), ('measles', 1747), ('I', 1604)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Having a word count\n",
    "\n",
    "# Concatenate all the 'safe_text' into a single string\n",
    "text = ' '.join(train_data['safe_text'])\n",
    "\n",
    "# Split the text into words\n",
    "words = text.split()\n",
    "\n",
    "# Count the frequency of each word\n",
    "word_counts = Counter(words)\n",
    "\n",
    "# Display the most common words\n",
    "print(word_counts.most_common(10))\n",
    "\n",
    "# Generate the word cloud with a white background\n",
    "cloud_two_cities = WordCloud(width=800, height=400, background_color='white').generate(text)\n",
    "\n",
    "# Display the word cloud\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.imshow(cloud_two_cities, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.tight_layout(pad=1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the length of each text in 'safe_text'\n",
    "text_lengths = train_data['safe_text'].apply(len)\n",
    "\n",
    "# Plot the distribution of text lengths\n",
    "plt.hist(text_lengths)\n",
    "plt.xlabel('Text Length')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Text Lengths')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Issues to treat:\n",
    "\n",
    "\n",
    "*   Remove unneccesary columns.\n",
    "*   Remove emojis and other characters from safe text column.\n",
    "*   Remove punctuations from the safe text column\n",
    "*   Changing all text to lower cases.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the missing values in the df_train dataset are:  \n",
      "\n",
      " tweet_id     0\n",
      "safe_text    0\n",
      "label        0\n",
      "agreement    0\n",
      "dtype: int64 \n",
      "\n",
      " ------------------------------------------------------------\n",
      "the missing values in the df_test dataset are:  \n",
      "\n",
      " tweet_id     0\n",
      "safe_text    0\n",
      "dtype: int64 \n",
      "\n",
      " ------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "data=[train_data, test_data]\n",
    "names=[\"df_train\", \"df_test\"]\n",
    "\n",
    "for m, i in zip(data, names):\n",
    "  print(f\"the missing values in the\", i,\"dataset are: \", \"\\n\\n\", m.isna().sum(), \"\\n\\n\", \"---\"*20 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicates \n",
    "train_data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    me amp the big homie meanboy3000 meanboy mb mb...\n",
       "1    im 100 thinking of devoting my career to provi...\n",
       "2    whatcausesautism vaccines do not vaccinate you...\n",
       "3    i mean if they immunize my kid with something ...\n",
       "4    thanks to user catch me performing at la nuit ...\n",
       "5    user a nearly 67 year old study when mental he...\n",
       "6    study of more than 95000 kids finds no link be...\n",
       "7                      psa vaccinate your fucking kids\n",
       "8    coughing extra on the shuttle and everyone thi...\n",
       "9    aids vaccine created at oregon health amp scie...\n",
       "Name: safe_text, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean the 'safe_text' column (example: remove URLs and special characters)\n",
    "train_data['safe_text'] = train_data['safe_text'].str.replace(r'<url>', '')  # Remove <url> tag\n",
    "test_data['safe_text'] = test_data['safe_text'].str.replace(r'<url>', '')  # Remove <url> tag\n",
    "\n",
    "# Remove emojis and other special characters\n",
    "emojis = re.compile(r'[^\\w\\s@#$%^*()<>/|}{~:&]')\n",
    "train_data[\"safe_text\"] = train_data[\"safe_text\"].str.replace(emojis, '')\n",
    "test_data[\"safe_text\"] = test_data[\"safe_text\"].str.replace(emojis, '')\n",
    "\n",
    "# # Remove punctuation\n",
    "punctuation = string.punctuation\n",
    "train_data[\"safe_text\"] = train_data[\"safe_text\"].str.translate(str.maketrans('', '', punctuation))\n",
    "test_data[\"safe_text\"] = test_data[\"safe_text\"].str.translate(str.maketrans('', '', punctuation))\n",
    "\n",
    "# remove hashtags \n",
    "train_data['safe_text'] = train_data['safe_text'].apply(nfx.remove_hashtags)\n",
    "test_data['safe_text'] = test_data['safe_text'].apply(nfx.remove_hashtags)\n",
    "\n",
    "# Turn the safe_text column into lowercase\n",
    "train_data[\"safe_text\"] = train_data[\"safe_text\"].str.lower()\n",
    "test_data[\"safe_text\"] = test_data[\"safe_text\"].str.lower()\n",
    "\n",
    "# remove multiple white spaces\n",
    "def stripSpace(text):\n",
    "    return text.strip()\n",
    "train_data['safe_text'] = train_data['safe_text'].apply(nfx.remove_multiple_spaces)\n",
    "train_data['safe_text'] = train_data['safe_text'].apply(stripSpace)\n",
    "\n",
    "# remove RT and user handles\n",
    "def removeRT(text):\n",
    "    return text.replace(\"RT\" , \"\")\n",
    "train_data['safe_text'] = train_data['safe_text'].apply(lambda x: nfx.remove_userhandles(x))\n",
    "train_data['safe_text'] = train_data['safe_text'].apply(removeRT)\n",
    "\n",
    "#Preview of the safe text column\n",
    "train_data['safe_text'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#REMOVING STOPWORDS\n",
    "# Download the stop words (only required for the first time)\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove stop words\n",
    "stop_words = set(stopwords.words('english'))\n",
    "train_data['safe_text'] = train_data['safe_text'].apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in stop_words]))\n",
    "test_data['safe_text'] = test_data['safe_text'].apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in stop_words]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export DataFrame as CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save df_train\n",
    "train_data.to_csv('../data/train_data.csv', index=False)\n",
    "\n",
    "# Save df_test\n",
    "test_data.to_csv('../data/test_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IMPORTING CLEANED DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Disabe W&B\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset and display some values\n",
    "df = pd.read_csv('../data/train_data.csv')\n",
    "\n",
    "# A way to eliminate rows containing NaN values\n",
    "df = df[~df.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I manually split the training set to have a training subset ( a dataset the model will learn on), and an evaluation subset ( a dataset the model with use to compute metric scores to help use to avoid some training problems like [the overfitting](https://www.ibm.com/cloud/learn/overfitting) one ). \n",
    "\n",
    "There are multiple ways to do split the dataset. You'll see two commented line showing you another one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TRAIN TEST SPLIT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the train data => {train, eval}\n",
    "train, eval = train_test_split(df, test_size=0.2, random_state=42, stratify=df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>tweet_id</th>\n",
       "      <th>safe_text</th>\n",
       "      <th>label</th>\n",
       "      <th>agreement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9303</th>\n",
       "      <td>YMRMEDME</td>\n",
       "      <td>mickeys measles gone international</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3907</th>\n",
       "      <td>5GV8NEZS</td>\n",
       "      <td>s1256 new extends exemption charitable immunit...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>EI10PS46</td>\n",
       "      <td>user ignorance vaccines isnt dangerous innocen...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5791</th>\n",
       "      <td>OM26E6DG</td>\n",
       "      <td>pakistan partly suspends polio vaccination pro...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3431</th>\n",
       "      <td>NBBY86FX</td>\n",
       "      <td>news ive gone like 1000 mmr</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tweet_id                                          safe_text  label  \\\n",
       "9303  YMRMEDME                 mickeys measles gone international    0.0   \n",
       "3907  5GV8NEZS  s1256 new extends exemption charitable immunit...    0.0   \n",
       "795   EI10PS46  user ignorance vaccines isnt dangerous innocen...    1.0   \n",
       "5791  OM26E6DG  pakistan partly suspends polio vaccination pro...    0.0   \n",
       "3431  NBBY86FX                        news ive gone like 1000 mmr    0.0   \n",
       "\n",
       "      agreement  \n",
       "9303   1.000000  \n",
       "3907   1.000000  \n",
       "795    0.666667  \n",
       "5791   1.000000  \n",
       "3431   1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>tweet_id</th>\n",
       "      <th>safe_text</th>\n",
       "      <th>label</th>\n",
       "      <th>agreement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>R7JPIFN7</td>\n",
       "      <td>childrens museum houston offer free vaccinations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>2DD250VN</td>\n",
       "      <td>user properly immunized prior performance kid ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3325</th>\n",
       "      <td>ESEVBTFN</td>\n",
       "      <td>user thx posting vaccinations imperative dear ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>S17ZU0LC</td>\n",
       "      <td>baby exactly everyone needs vaccinate via user</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>IIN5D33V</td>\n",
       "      <td>meeting tonight 830pm room 322 student center ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tweet_id                                          safe_text  label  \\\n",
       "6569  R7JPIFN7   childrens museum houston offer free vaccinations    1.0   \n",
       "1754  2DD250VN  user properly immunized prior performance kid ...    1.0   \n",
       "3325  ESEVBTFN  user thx posting vaccinations imperative dear ...    1.0   \n",
       "1485  S17ZU0LC     baby exactly everyone needs vaccinate via user    1.0   \n",
       "4175  IIN5D33V  meeting tonight 830pm room 322 student center ...    1.0   \n",
       "\n",
       "      agreement  \n",
       "6569   1.000000  \n",
       "1754   1.000000  \n",
       "3325   1.000000  \n",
       "1485   0.666667  \n",
       "4175   1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new dataframe shapes: train is (7999, 4), eval is (2000, 4)\n"
     ]
    }
   ],
   "source": [
    "print(f\"new dataframe shapes: train is {train.shape}, eval is {eval.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SAVING THE TRAIN AND EVAL SUBSET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save splitted subsets\n",
    "train.to_csv(\"../data/train_subset.csv\", index=False)\n",
    "eval.to_csv(\"../data/eval_subset.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e7ec2e4933d474a93bbcabd7107d518",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8dcc7ac5bae4f6c897af61d8772cb98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b0e345d71574faebac68be4d355b5cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fed601b38896418fa81304069fdd424b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating eval split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = load_dataset('csv',\n",
    "                        data_files={'train': '../data/train_subset.csv',\n",
    "                        'eval': '../data/eval_subset.csv'}, encoding = \"ISO-8859-1\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf3141c536f245afa6700047e4f50001",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/760 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\file_download.py:133: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\user\\.cache\\huggingface\\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31fd47b0088f4429b0b31c6e98b5f11d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/798k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9126dd6db53476a91549c3c6c0407e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.38M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocess text (username and link placeholders)\n",
    "def preprocess(text):\n",
    "    new_text = []\n",
    "    for t in text.split(\" \"):\n",
    "        t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
    "        t = 'http' if t.startswith('http') else t\n",
    "        new_text.append(t)\n",
    "    return \" \".join(new_text)\n",
    "\n",
    "# \"cardiffnlp/twitter-xlm-roberta-base-sentiment\"\n",
    "# \"roberta-base\"\n",
    "# \"xlnet-base-cased\"\n",
    "# \"bert-base-uncased\"\n",
    "\n",
    "\n",
    "from transformers import AutoTokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained('xlnet-base-cased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a08e54061bea42cbb31ae1b0061c8824",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d343365ca2745fc8c68638a51ff401e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "721695fa571e4ebd9426ceb2c31868cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0d3416e051c45ef86de5dcad9a23dd4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to transform labels\n",
    "def transform_labels(label):\n",
    "\n",
    "    label = label['label']\n",
    "    num = 0\n",
    "    if label == -1: #'Negative'\n",
    "        num = 0\n",
    "    elif label == 0: #'Neutral'\n",
    "        num = 1\n",
    "    elif label == 1: #'Positive'\n",
    "        num = 2\n",
    "\n",
    "    return {'labels': num}\n",
    "\n",
    "# Function to tokenize data\n",
    "def tokenize_data(example):\n",
    "    return tokenizer(example['safe_text'], padding='max_length')\n",
    "\n",
    "# Change the tweets to tokens that the models can exploit\n",
    "dataset = dataset.map(tokenize_data, batched=True)\n",
    "\n",
    "# Transform\tlabels and remove the useless columns\n",
    "remove_columns = ['tweet_id', 'label', 'safe_text', 'agreement']\n",
    "dataset = dataset.map(transform_labels, remove_columns=remove_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 7999\n",
       "    })\n",
       "    eval: Dataset({\n",
       "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 2000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    }
   ],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "# Configure the trianing parameters like `num_train_epochs`: \n",
    "# the number of time the model will repeat the training loop over the dataset\n",
    "training_args = TrainingArguments(\n",
    "    \"test_trainer\", \n",
    "    num_train_epochs=10, \n",
    "    load_best_model_at_end=True,\n",
    "    save_strategy=\"epoch\",\n",
    "    push_to_hub=True,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    logging_strategy=\"epoch\",\n",
    "    logging_steps=100,\n",
    "    per_device_train_batch_size=16\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LOADING PRETRAINED MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2aedebd48a2647ef922cb03278c69820",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/467M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of XLNetForSequenceClassification were not initialized from the model checkpoint at xlnet-base-cased and are newly initialized: ['logits_proj.weight', 'logits_proj.bias', 'sequence_summary.summary.weight', 'sequence_summary.summary.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification\n",
    "\n",
    "# Loading a pretrain model while specifying the number of labels in our dataset for fine-tuning\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"xlnet-base-cased\", num_labels=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SPLITTING TRAIN SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = dataset['train'].shuffle(seed=10) #.select(range(40000)) # to select a part\n",
    "eval_dataset = dataset['eval'].shuffle(seed=10)\n",
    "\n",
    "## other way to split the train set ... in the range you must use: \n",
    "# # int(num_rows*.8 ) for [0 - 80%] and  int(num_rows*.8 ),num_rows for the 20% ([80 - 100%])\n",
    "# train_dataset = dataset['train'].shuffle(seed=10).select(range(40000))\n",
    "# eval_dataset = dataset['train'].shuffle(seed=10).select(range(40000, 41000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EVALUATION METRIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    return {\"rmse\": mean_squared_error(labels, predictions, squared=False)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub.hf_api import HfFolder; HfFolder.save_token('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')                           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MODEL TRAINING SETUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "RepositoryNotFoundError",
     "evalue": "404 Client Error. (Request ID: Root=1-64f3025d-1c0756023bc85d1204869818;d89f1676-1660-4932-8ac3-02c74d9257ee)\n\nRepository Not Found for url: https://huggingface.co/api/models/test_trainer.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\utils\\_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[1;34m(response, endpoint_name)\u001b[0m\n\u001b[0;32m    260\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 261\u001b[1;33m         \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    262\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\requests\\models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1020\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1021\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1022\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mHfHubHTTPError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[1;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware)\u001b[0m\n\u001b[0;32m   2307\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2308\u001b[1;33m             \u001b[0mhf_raise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2309\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\utils\\_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[1;34m(response, endpoint_name)\u001b[0m\n\u001b[0;32m    302\u001b[0m         \u001b[1;31m# as well (request id and/or server error message)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 303\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mHfHubHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    304\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHfHubHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create (Request ID: Root=1-64f3025d-08ecd56f4c840c46653c0d92;6d031b70-b362-4736-ab2a-67b07365855c)\n\nYou don't have the rights to create a model under this namespace",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\utils\\_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[1;34m(response, endpoint_name)\u001b[0m\n\u001b[0;32m    260\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 261\u001b[1;33m         \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    262\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\requests\\models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1020\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1021\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1022\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/api/models/test_trainer",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mRepositoryNotFoundError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_6024\\2312574654.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTrainer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# Model Training Setup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m trainer = Trainer(\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_args\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\transformers\\trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, model, args, data_collator, train_dataset, eval_dataset, tokenizer, model_init, compute_metrics, callbacks, optimizers, preprocess_logits_for_metrics)\u001b[0m\n\u001b[0;32m    555\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    556\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_hf_repo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    558\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_save\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m             \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\transformers\\trainer.py\u001b[0m in \u001b[0;36minit_hf_repo\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   3433\u001b[0m             \u001b[0mrepo_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhub_model_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3435\u001b[1;33m         \u001b[0mrepo_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_repo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrepo_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhub_token\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhub_private_repo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3436\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrepo_url\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3437\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpush_in_progress\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\utils\\_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 118\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[1;31m# type: ignore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[1;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware)\u001b[0m\n\u001b[0;32m   2314\u001b[0m                 \u001b[1;31m# No write permission on the namespace but repo might already exist\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2315\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2316\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrepo_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrepo_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrepo_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2317\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mrepo_type\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mrepo_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mREPO_TYPE_MODEL\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2318\u001b[0m                         \u001b[1;32mreturn\u001b[0m \u001b[0mRepoUrl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{self.endpoint}/{repo_id}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\utils\\_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 118\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[1;31m# type: ignore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\hf_api.py\u001b[0m in \u001b[0;36mrepo_info\u001b[1;34m(self, repo_id, revision, repo_type, timeout, files_metadata, token)\u001b[0m\n\u001b[0;32m   1866\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1867\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Unsupported repo type.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1868\u001b[1;33m         return method(\n\u001b[0m\u001b[0;32m   1869\u001b[0m             \u001b[0mrepo_id\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1870\u001b[0m             \u001b[0mrevision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\utils\\_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 118\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[1;31m# type: ignore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\hf_api.py\u001b[0m in \u001b[0;36mmodel_info\u001b[1;34m(self, repo_id, revision, timeout, securityStatus, files_metadata, token)\u001b[0m\n\u001b[0;32m   1676\u001b[0m             \u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"blobs\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1677\u001b[0m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1678\u001b[1;33m         \u001b[0mhf_raise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1679\u001b[0m         \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1680\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mModelInfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub\\utils\\_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[1;34m(response, endpoint_name)\u001b[0m\n\u001b[0;32m    291\u001b[0m                 \u001b[1;34m\" make sure you are authenticated.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m             )\n\u001b[1;32m--> 293\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mRepositoryNotFoundError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m400\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRepositoryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-64f3025d-1c0756023bc85d1204869818;d89f1676-1660-4932-8ac3-02c74d9257ee)\n\nRepository Not Found for url: https://huggingface.co/api/models/test_trainer.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated."
     ]
    }
   ],
   "source": [
    "from transformers import Trainer\n",
    "# Model Training Setup\n",
    "trainer = Trainer(\n",
    "    model=model, \n",
    "    args=training_args, \n",
    "    train_dataset=train_dataset, \n",
    "    eval_dataset=eval_dataset,\n",
    "    #tokenizer=tokenizer,\n",
    "    compute_metrics=compute_metrics,\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running training *****\n",
      "  Num examples = 7999\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 3000\n",
      "                                                   \n",
      "  1%|          | 16/3000 [4:25:07<6:59:23,  8.43s/it] Saving model checkpoint to test_trainer/checkpoint-500\n",
      "Configuration saved in test_trainer/checkpoint-500/config.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.7607, 'learning_rate': 4.166666666666667e-05, 'epoch': 0.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model weights saved in test_trainer/checkpoint-500/pytorch_model.bin\n",
      "                                                     \n",
      "  1%|          | 16/3000 [7:16:40<6:59:23,  8.43s/it]  Saving model checkpoint to test_trainer/checkpoint-1000\n",
      "Configuration saved in test_trainer/checkpoint-1000/config.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.6572, 'learning_rate': 3.3333333333333335e-05, 'epoch': 1.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model weights saved in test_trainer/checkpoint-1000/pytorch_model.bin\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/transformers/trainer.py:1498\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1493\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_wrapped \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m   1495\u001b[0m inner_training_loop \u001b[39m=\u001b[39m find_executable_batch_size(\n\u001b[1;32m   1496\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_inner_training_loop, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_train_batch_size, args\u001b[39m.\u001b[39mauto_find_batch_size\n\u001b[1;32m   1497\u001b[0m )\n\u001b[0;32m-> 1498\u001b[0m \u001b[39mreturn\u001b[39;00m inner_training_loop(\n\u001b[1;32m   1499\u001b[0m     args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m   1500\u001b[0m     resume_from_checkpoint\u001b[39m=\u001b[39;49mresume_from_checkpoint,\n\u001b[1;32m   1501\u001b[0m     trial\u001b[39m=\u001b[39;49mtrial,\n\u001b[1;32m   1502\u001b[0m     ignore_keys_for_eval\u001b[39m=\u001b[39;49mignore_keys_for_eval,\n\u001b[1;32m   1503\u001b[0m )\n",
      "File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/transformers/trainer.py:1740\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1738\u001b[0m         tr_loss_step \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining_step(model, inputs)\n\u001b[1;32m   1739\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1740\u001b[0m     tr_loss_step \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtraining_step(model, inputs)\n\u001b[1;32m   1742\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m   1743\u001b[0m     args\u001b[39m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m   1744\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m is_torch_tpu_available()\n\u001b[1;32m   1745\u001b[0m     \u001b[39mand\u001b[39;00m (torch\u001b[39m.\u001b[39misnan(tr_loss_step) \u001b[39mor\u001b[39;00m torch\u001b[39m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m   1746\u001b[0m ):\n\u001b[1;32m   1747\u001b[0m     \u001b[39m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m   1748\u001b[0m     tr_loss \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m tr_loss \u001b[39m/\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mglobal_step \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_globalstep_last_logged)\n",
      "File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/transformers/trainer.py:2488\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2486\u001b[0m     loss \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdeepspeed\u001b[39m.\u001b[39mbackward(loss)\n\u001b[1;32m   2487\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 2488\u001b[0m     loss\u001b[39m.\u001b[39;49mbackward()\n\u001b[1;32m   2490\u001b[0m \u001b[39mreturn\u001b[39;00m loss\u001b[39m.\u001b[39mdetach()\n",
      "File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/torch/_tensor.py:396\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    387\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39mself\u001b[39m):\n\u001b[1;32m    388\u001b[0m     \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    389\u001b[0m         Tensor\u001b[39m.\u001b[39mbackward,\n\u001b[1;32m    390\u001b[0m         (\u001b[39mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    394\u001b[0m         create_graph\u001b[39m=\u001b[39mcreate_graph,\n\u001b[1;32m    395\u001b[0m         inputs\u001b[39m=\u001b[39minputs)\n\u001b[0;32m--> 396\u001b[0m torch\u001b[39m.\u001b[39;49mautograd\u001b[39m.\u001b[39;49mbackward(\u001b[39mself\u001b[39;49m, gradient, retain_graph, create_graph, inputs\u001b[39m=\u001b[39;49minputs)\n",
      "File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/torch/autograd/__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    168\u001b[0m     retain_graph \u001b[39m=\u001b[39m create_graph\n\u001b[1;32m    170\u001b[0m \u001b[39m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m    171\u001b[0m \u001b[39m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m    172\u001b[0m \u001b[39m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 173\u001b[0m Variable\u001b[39m.\u001b[39;49m_execution_engine\u001b[39m.\u001b[39;49mrun_backward(  \u001b[39m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m    174\u001b[0m     tensors, grad_tensors_, retain_graph, create_graph, inputs,\n\u001b[1;32m    175\u001b[0m     allow_unreachable\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, accumulate_grad\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Launch the learning process: training \n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from datasets import load_metric\n",
    "\n",
    "metric = load_metric(\"accuracy\")\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    return metric.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trainer = Trainer(\n",
    "#     model=model,\n",
    "#     args=training_args,\n",
    "#     train_dataset=train_dataset,\n",
    "#     eval_dataset=eval_dataset,\n",
    "#     compute_metrics=compute_metrics,\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Downloading builder script: 4.21kB [00:00, 932kB/s]                    \n",
      "***** Running Evaluation *****\n",
      "  Num examples = 2000\n",
      "  Batch size = 8\n",
      "\n",
      "\u001b[A\n",
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      "100%|██████████| 250/250 [09:04<00:00,  2.18s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.6274272203445435,\n",
       " 'eval_accuracy': 0.7665,\n",
       " 'eval_runtime': 546.3013,\n",
       " 'eval_samples_per_second': 3.661,\n",
       " 'eval_steps_per_second': 0.458}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Launch the final evaluation \n",
    "\n",
    "import numpy as np\n",
    "from datasets import load_metric\n",
    "\n",
    "metric = load_metric(\"accuracy\")\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    return metric.compute(predictions=predictions, references=labels)\n",
    "\n",
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some checkpoints of the model are automatically saved locally in `test_trainer/` during the training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may also upload the model on the Hugging Face Platform... [Read more](https://huggingface.co/docs/hub/models-uploading)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is inspired by an article: [Fine-Tuning Bert for Tweets Classification ft. Hugging Face](https://medium.com/mlearning-ai/fine-tuning-bert-for-tweets-classification-ft-hugging-face-8afebadd5dbf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do not hesitaite to read more and to ask questions, the Learning is a lifelong activity."
   ]
  }
 ],
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