02_FineTuning_BERT_Model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Installing Required Libraries\n"
      ],
      "metadata": {
        "id": "LGXrT0FDiX8q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "l5ceeO9LhWkU",
        "outputId": "b460e8fb-92ac-4fa8-dc10-0d0de2c0b8b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate==0.28.0\n",
            "  Downloading accelerate-0.28.0-py3-none-any.whl (290 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/290.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m256.0/290.1 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.1/290.1 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate==0.28.0) (1.25.2)\n",
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            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==0.28.0) (2.2.1+cu121)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.28.0) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.28.0) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.10.0->accelerate==0.28.0)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate==0.28.0"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading and Preparing the Dataset"
      ],
      "metadata": {
        "id": "JeQJHHVCichU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Reads the dataset from a CSV file with\n",
        "\n",
        "* ISO-8859-1 encoding.\n",
        "Retains only the News and Sentiment1 columns for sentiment analysis."
      ],
      "metadata": {
        "id": "VyPmcON3ifui"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vbO17rTZ1oYt"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8HNUE5fXIb-W"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZLuKyvHJ47xK",
        "outputId": "5f9fcec3-2e46-484d-819c-6b3814807fe8"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Sentiment                                               News  Sentiment1\n",
              "0   neutral  According to Gran , the company has no plans t...           1\n",
              "1   neutral  Technopolis plans to develop in stages an area...           1\n",
              "2  negative  The international electronic industry company ...           0\n",
              "3  positive  With the new production plant the company woul...           1\n",
              "4  positive  According to the company 's updated strategy f...           1"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4846,\n  \"fields\": [\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"News\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4838,\n        \"samples\": [\n          \"The Company serves approximately 3,000 customers in over 100 countries .\",\n          \"On Dec. 1 , Grimaldi acquired 1.5 million shares and a 50.1-percent stake in Finnlines .\",\n          \"The extracted filtrates are very high in clarity while the dried filter cakes meet required transport moisture limits (TMLs)for their ore grades .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "  data = pd.read_csv(\"/content/drive/MyDrive/all-data.csv\", encoding='ISO-8859-1')\n",
        "  data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dbrirLG5D0A",
        "outputId": "12e60c39-ef11-45ef-fa20-9cca123833a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sentiment1\n",
              "1    2769\n",
              "0    2077\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data['Sentiment1'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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              "                                                News  Sentiment1\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4846,\n  \"fields\": [\n    {\n      \"column\": \"News\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4838,\n        \"samples\": [\n          \"The Company serves approximately 3,000 customers in over 100 countries .\",\n          \"On Dec. 1 , Grimaldi acquired 1.5 million shares and a 50.1-percent stake in Finnlines .\",\n          \"The extracted filtrates are very high in clarity while the dried filter cakes meet required transport moisture limits (TMLs)for their ore grades .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data = data[['News','Sentiment1']]\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing Libraries"
      ],
      "metadata": {
        "id": "Wtw4lMQ2iqmA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s32nl2kn56cH"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
        "import torch\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from transformers import BertTokenizer, BertForSequenceClassification"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "--Sid5n40UHI",
        "outputId": "064a3f17-2774-47b6-d627-fb85574cd5ff"
      },
      "execution_count": null,
      "outputs": [
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              "                                                News  Sentiment1\n",
              "0  According to Gran , the company has no plans t...           1\n",
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              "2  The international electronic industry company ...           0\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2de553ad-97f2-43e2-b62a-30b9aec98b49 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4846,\n  \"fields\": [\n    {\n      \"column\": \"News\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4838,\n        \"samples\": [\n          \"The Company serves approximately 3,000 customers in over 100 countries .\",\n          \"On Dec. 1 , Grimaldi acquired 1.5 million shares and a 50.1-percent stake in Finnlines .\",\n          \"The extracted filtrates are very high in clarity while the dried filter cakes meet required transport moisture limits (TMLs)for their ore grades .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tokenizing the Data with BERT"
      ],
      "metadata": {
        "id": "XsBCj4zCivlY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Uses the BertTokenizer from the transformers library to tokenize text.\n",
        "* Applies padding and truncation to ensure uniform input length, with a maximum length of 512 tokens.\n",
        "* Splits the data into training and validation sets, stratifying by sentiment labels."
      ],
      "metadata": {
        "id": "24heCZvKi63l"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1JlB3eYiV8a",
        "outputId": "b682662b-8ee5-485c-d3a6-f62fb203ba05"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', '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 BertTokenizer, BertForSequenceClassification\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-cased',num_labels=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPKswImmYUjO",
        "outputId": "c6566635-cf79-4241-b3dc-da23a7fc6d05"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(28996, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nCIO165RiZfC"
      },
      "outputs": [],
      "source": [
        "model = model.to('cuda')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WyWcZBQuid2V"
      },
      "outputs": [],
      "source": [
        "X = list(data['News'])\n",
        "y = list(data['Sentiment1'])\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2,stratify=y)\n",
        "X_train_tokenized = tokenizer(X_train, padding=True, truncation=True, max_length=512)\n",
        "X_val_tokenized = tokenizer(X_val, padding=True, truncation=True, max_length=512)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ll_YffE1rrKV",
        "outputId": "2f335f58-0579-4d81-d9da-2fd750f28397"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "X_train_tokenized.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5TGibGZIAZT",
        "outputId": "df369be6-9b52-4bc8-feab-acc60825ccea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ],
      "source": [
        "print(X_train_tokenized['attention_mask'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2bs25fZish6",
        "outputId": "22f046db-8894-4bf9-c8b3-8f8be0c42021"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3876, 970)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "len(X_train),len(X_val)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating a Custom Dataset Class"
      ],
      "metadata": {
        "id": "rsYCqZUljJA0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Defines a PyTorch dataset class to convert tokenized data into tensor format for BERT."
      ],
      "metadata": {
        "id": "5d_gzMSRjKWB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Dq__AdmjiEh"
      },
      "outputs": [],
      "source": [
        "# Create torch dataset\n",
        "class Dataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels=None):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        if self.labels:\n",
        "            item[\"labels\"] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.encodings[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparing the Training and Validation Datasets"
      ],
      "metadata": {
        "id": "_ZeiTbxejPju"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Converts tokenized data into instances of the custom dataset class.\n"
      ],
      "metadata": {
        "id": "BeD-85ODjVZb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "52bjWWitkMRw"
      },
      "outputs": [],
      "source": [
        "train_dataset = Dataset(X_train_tokenized, y_train)\n",
        "val_dataset = Dataset(X_val_tokenized, y_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqrFeE7Trgtd",
        "outputId": "b51f8cf9-9c41-4059-a1c2-beea6105bae0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([  101, 13623,  1116,  1132,  1136, 23617,   119,   102,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0]),\n",
              " 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
              " 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
              " 'labels': tensor(0)}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "train_dataset[5]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the Metrics Function"
      ],
      "metadata": {
        "id": "jvnhFG_sjbWy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Defines a compute_metrics function to calculate accuracy, precision, recall, and F1-score for evaluation."
      ],
      "metadata": {
        "id": "jQW535Cnjdcu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rSYfH_aIkOwf"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(p):\n",
        "    print(type(p))\n",
        "    pred, labels = p\n",
        "    pred = np.argmax(pred, axis=1)\n",
        "\n",
        "    accuracy = accuracy_score(y_true=labels, y_pred=pred)\n",
        "    recall = recall_score(y_true=labels, y_pred=pred)\n",
        "    precision = precision_score(y_true=labels, y_pred=pred)\n",
        "    f1 = f1_score(y_true=labels, y_pred=pred)\n",
        "\n",
        "    return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setting Training Arguments"
      ],
      "metadata": {
        "id": "gl4RJY07jkR6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFcXgFFNkRDP",
        "outputId": "2976d391-54bd-468f-9fd8-f403b7aeb0dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "args = TrainingArguments(\n",
        "     output_dir=\"output\",\n",
        "     num_train_epochs=5,\n",
        "     per_device_train_batch_size=8\n",
        "\n",
        " )\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the Model Using Trainer API"
      ],
      "metadata": {
        "id": "OyIAmkERjpQJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Uses the Trainer API from transformers to handle the training and evaluation processes."
      ],
      "metadata": {
        "id": "agL234-tjq3D"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "topNq2I6kTnX",
        "outputId": "eec46edd-796b-4ee1-f9c2-7f0f6016a764"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2425' max='2425' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2425/2425 09:54, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.616400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.504900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.373200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.243600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2425, training_loss=0.38363179314996776, metrics={'train_runtime': 596.8015, 'train_samples_per_second': 32.473, 'train_steps_per_second': 4.063, 'total_flos': 1543670506242000.0, 'train_loss': 0.38363179314996776, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "Ia14Y_UmN7qM",
        "outputId": "95e2b823-c50d-43cf-aa8a-85ba732538b4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='122' max='122' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [122/122 00:06]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'transformers.trainer_utils.EvalPrediction'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 1.7454335689544678,\n",
              " 'eval_accuracy': 0.6618556701030928,\n",
              " 'eval_precision': 0.7084870848708487,\n",
              " 'eval_recall': 0.6931407942238267,\n",
              " 'eval_f1': 0.7007299270072993,\n",
              " 'eval_runtime': 6.1856,\n",
              " 'eval_samples_per_second': 156.815,\n",
              " 'eval_steps_per_second': 19.723,\n",
              " 'epoch': 5.0}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving the Model to Google Drive"
      ],
      "metadata": {
        "id": "r8lmOaDijwwB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "# Define the directory path in Google Drive\n",
        "model_directory = '/content/drive/MyDrive/bert_model'\n",
        "\n",
        "# Create the directory if it doesn't exist\n",
        "if not os.path.exists(model_directory):\n",
        "    os.makedirs(model_directory)\n",
        "\n",
        "# Save the model to the specified directory\n",
        "model.save_pretrained(model_directory)"
      ],
      "metadata": {
        "id": "dbJCNqza3aKY"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}