FineTuning.ipynb

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        "<a href=\"https://colab.research.google.com/github/Prathamesh01110/Bert-Finetuning/blob/main/FineTuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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        "id": "O5bQVXdKl3pw",
        "outputId": "fcee78f2-e13b-4a23-91f9-65a1d1729a61"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.45.2-py3-none-any.whl.metadata (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Collecting tokenizers<0.21,>=0.20 (from transformers)\n",
            "  Downloading tokenizers-0.20.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n",
            "Downloading transformers-4.45.2-py3-none-any.whl (9.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.9/9.9 MB\u001b[0m \u001b[31m96.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tokenizers-0.20.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m94.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.19.1\n",
            "    Uninstalling tokenizers-0.19.1:\n",
            "      Successfully uninstalled tokenizers-0.19.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.44.2\n",
            "    Uninstalling transformers-4.44.2:\n",
            "      Successfully uninstalled transformers-4.44.2\n",
            "Successfully installed tokenizers-0.20.1 transformers-4.45.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers -U"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "zM1elnAfmdFa"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import kagglehub\n",
        "path = kagglehub.dataset_download(\"reihanenamdari/youtube-toxicity-data\")\n",
        "\n",
        "print(\"Path to dataset files:\", path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vq-r3tVoOx3",
        "outputId": "edc3b9d4-fa06-439e-adcd-f814d1329136"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Path to dataset files: /root/.cache/kagglehub/datasets/reihanenamdari/youtube-toxicity-data/versions/1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Get the current working directory\n",
        "current_path = os.getcwd()\n",
        "print(\"Current Path:\", current_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q_QgAlqoU2j",
        "outputId": "35511e1c-4561-4fed-bb9d-a6b533398c05"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current Path: /content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "csv_file_path = '/root/.cache/kagglehub/datasets/reihanenamdari/youtube-toxicity-data/versions/1/youtoxic_english_1000.csv'\n",
        "data = pd.read_csv(csv_file_path)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "A7fTAXj5pKpY",
        "outputId": "e911f92d-a629-4e98-f0a7-f9b36bd1c659"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              CommentId      VideoId  \\\n",
              "0  Ugg2KwwX0V8-aXgCoAEC  04kJtp6pVXI   \n",
              "1  Ugg2s5AzSPioEXgCoAEC  04kJtp6pVXI   \n",
              "2  Ugg3dWTOxryFfHgCoAEC  04kJtp6pVXI   \n",
              "3  Ugg7Gd006w1MPngCoAEC  04kJtp6pVXI   \n",
              "4  Ugg8FfTbbNF8IngCoAEC  04kJtp6pVXI   \n",
              "\n",
              "                                                Text  IsToxic  IsAbusive  \\\n",
              "0  If only people would just take a step back and...    False      False   \n",
              "1  Law enforcement is not trained to shoot to app...     True       True   \n",
              "2  \\nDont you reckon them 'black lives matter' ba...     True       True   \n",
              "3  There are a very large number of people who do...    False      False   \n",
              "4  The Arab dude is absolutely right, he should h...    False      False   \n",
              "\n",
              "   IsThreat  IsProvocative  IsObscene  IsHatespeech  IsRacist  IsNationalist  \\\n",
              "0     False          False      False         False     False          False   \n",
              "1     False          False      False         False     False          False   \n",
              "2     False          False       True         False     False          False   \n",
              "3     False          False      False         False     False          False   \n",
              "4     False          False      False         False     False          False   \n",
              "\n",
              "   IsSexist  IsHomophobic  IsReligiousHate  IsRadicalism  \n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"CommentId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"UgjxVU0ok82_bXgCoAEC\",\n          \"Uggma-nkAiK-eXgCoAEC\",\n          \"UggQG0KuU_-llXgCoAEC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VideoId\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"TZxEyoplYbI\",\n          \"Dt9-byUhPdg\",\n          \"04kJtp6pVXI\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 997,\n        \"samples\": [\n          \"The music references sex, drugs, stealing, murder, etc.\\n\\n...ya, pretty much all rap music is like that\",\n          \"That community should be marching for peace hand in hand for the girl that was killed sitting in her home doing homework. She deserves at least that or society has let her down. Maybe Al Sharpton will lead ...\\n\",\n          \"Fucking savages! Protest sumthing real... Like how your government is robbing you and sendin your kids to an elegal WAR! You are the laughing stock of America congrats!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsToxic\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsAbusive\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsThreat\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsProvocative\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsObscene\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsHatespeech\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsRacist\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsNationalist\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsSexist\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsHomophobic\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsReligiousHate\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsRadicalism\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['IsToxic'].value_counts() #True or False\n",
        "data['IsToxic'] = data['IsToxic'].astype(int) #it make the true as 1 and false as 0\n",
        "data['IsToxic'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "i6BL8Za7pKnp",
        "outputId": "0a88537c-7ad3-472c-f0de-1e1a98533a90"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsToxic\n",
              "0    538\n",
              "1    462\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IsToxic</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>462</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[['Text', 'IsToxic']]\n",
        "data.head()\n",
        "dimensions = data.shape\n",
        "print(f\"Total Count: {dimensions[0]}\")  # Total number of rows\n",
        "print(f\"Number of Columns: {dimensions[1]}\")  # Number of columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h08ES4INrNdD",
        "outputId": "301c7ddb-4108-4cf0-9455-d334533bade3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Count: 1000\n",
            "Number of Columns: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "DIClAu5gr-lp"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased',num_labels=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336,
          "referenced_widgets": [
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        "id": "xsm8JgK91W8g",
        "outputId": "e1c23a0f-2211-445f-f8e8-ddc43fbce4d0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b6b808e03454d8793166e7b78041158"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "507ecd8b32f84e9ab19144d81aad2c14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f34657403e7499682a4c6a3af14f0e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "631235a0659141f4b818effe0124ce95"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27b5328b7d5248bcb085004eed81b890"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LUHJwsk252r",
        "outputId": "b2cf5cd3-2ee3-4a43-885b-9147c5525a4f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 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): BertSdpaSelfAttention(\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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.to('cuda')"
      ],
      "metadata": {
        "id": "fejwyp0926BT"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_data = [\"i am prathamesh\",\"i am using BERT\",\"i am learnig finetuning\",\"A B C D E F G H I J K L\"]\n",
        "tokenizer(sample_data, padding=True, truncation=True, max_length=512)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bqw1UROJ5hbQ",
        "outputId": "74b57b46-ae49-4143-a548-fd6690173828"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[101, 1045, 2572, 10975, 8988, 14074, 4095, 102, 0, 0, 0, 0, 0, 0], [101, 1045, 2572, 2478, 14324, 102, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1045, 2572, 4553, 8004, 2986, 8525, 5582, 102, 0, 0, 0, 0, 0], [101, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 102]], 'token_type_ids': [[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]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X= list(data[\"Text\"])\n",
        "y= list(data[\"IsToxic\"])\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)\n",
        "X_train_tokenized.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9Y-qwIx59Un",
        "outputId": "a00e9eb9-cf72-439b-806e-8cf8f8056963"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(X_train_tokenized['input_ids'])\n",
        "# print(X_train_tokenized['attention_mask'])\n",
        "# len(X_train),len(X_val)"
      ],
      "metadata": {
        "id": "numwqh4I7t4F"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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\"])"
      ],
      "metadata": {
        "id": "anU5pAtC7t6y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = Dataset(X_train_tokenized,y_train)\n",
        "val_dataset = Dataset(X_val_tokenized, y_val)"
      ],
      "metadata": {
        "id": "AOHjmbGh7t9r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z54wqI4X7t_L",
        "outputId": "69da4b8f-a612-424b-dba8-ce4c0f5d75eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([  101,  1045,  2293,  2023,  3203,   999,   999,   999,  2057,  2342,\n",
              "          2062,  2844,  2308,  2066, 14911, 16580,   999,   999,   102,     0,\n",
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              "             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",
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              "         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, 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]),\n",
              " 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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, 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]),\n",
              " 'labels': tensor(0)}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "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}"
      ],
      "metadata": {
        "id": "1puwPTxA7uBz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Trainer\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"output\",\n",
        "    num_train_epochs=10,\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",
        ")"
      ],
      "metadata": {
        "id": "ljKI3y5R96lW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "id": "FlCkOYFG-J9Q",
        "outputId": "bdad9815-2636-4da0-ac54-4538a72ea1d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1000/1000 12:10, Epoch 10/10]\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>0.171800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.000200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1000, training_loss=0.08598063711076975, metrics={'train_runtime': 731.0631, 'train_samples_per_second': 10.943, 'train_steps_per_second': 1.368, 'total_flos': 2104888442880000.0, 'train_loss': 0.08598063711076975, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "SNBUmoCx-gP2",
        "outputId": "1c3cf920-8130-4f31-8128-b95989395e0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='25' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [25/25 00:02]\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': 1.789967656135559,\n",
              " 'eval_accuracy': 0.755,\n",
              " 'eval_precision': 0.7171717171717171,\n",
              " 'eval_recall': 0.7717391304347826,\n",
              " 'eval_f1': 0.743455497382199,\n",
              " 'eval_runtime': 3.1662,\n",
              " 'eval_samples_per_second': 63.167,\n",
              " 'eval_steps_per_second': 7.896,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.set_printoptions(suppress=True)"
      ],
      "metadata": {
        "id": "8Kt-cplSEWuy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"this video is bad\"\n",
        "inputs = tokenizer(text,padding = True, truncation = True, return_tensors='pt').to('cuda')\n",
        "outputs = model(**inputs)\n",
        "print(outputs)\n",
        "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
        "print(predictions)\n",
        "predictions = predictions.cpu().detach().numpy()\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3wHk9JeP-0ON",
        "outputId": "fe299147-1a21-4f58-86af-2139283a6795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SequenceClassifierOutput(loss=None, logits=tensor([[-3.3533,  3.4936]], device='cuda:0', grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n",
            "tensor([[0.0011, 0.9989]], device='cuda:0', grad_fn=<SoftmaxBackward0>)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00106159, 0.9989384 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model('Finetuneon10epochs')"
      ],
      "metadata": {
        "id": "-u1uIVii-0RF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2 = BertForSequenceClassification.from_pretrained(\"Finetuneon10epochs\")\n",
        "model_2.to('cuda')"
      ],
      "metadata": {
        "id": "8cz1QiZY-0Td",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd438d69-9b11-44b5-b3b0-8545cb2c0c3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 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): BertSdpaSelfAttention(\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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# text = \"That was good point\"\n",
        "text = \"this video is hell\"\n",
        "inputs = tokenizer(text,padding = True, truncation = True, return_tensors='pt').to('cuda')\n",
        "outputs = model_2(**inputs)\n",
        "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
        "predictions = predictions.cpu().detach().numpy()\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OD7svcOmFiVp",
        "outputId": "8357becf-9496-4495-f6e4-5583b5e26fb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00008366, 0.9999163 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"this video is good\"\n",
        "inputs = tokenizer(text,padding = True, truncation = True, return_tensors='pt').to('cuda')\n",
        "outputs = model_2(**inputs)\n",
        "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
        "predictions = predictions.cpu().detach().numpy()\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79Ip3m1aFiWu",
        "outputId": "3093a8dc-181e-4148-f5c3-99633c6f16e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.9999242 , 0.00007582]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# trainer.save_model('/content/drive/MyDrive/Youtube Tutorials/toxic')\n",
        "# model_2 = BertForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/Youtube Tutorials/toxic\")\n",
        "# model_2.to('cuda')"
      ],
      "metadata": {
        "id": "lm9ffK0-Ht_U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RCD8Tf1cHrsN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}