bert_for_AG_News.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## <font color='blue'>**BERT (Bidirectional Encoder Representations from Transformers)**</font>\n",
        "\n",
        "https://arxiv.org/pdf/1810.04805.pdf\n",
        "\n",
        "### <font color='red'>**Overview:** </font>\n",
        "BERT is a pre-trained model that learns contextual word representations by considering both left and right context in a sentence. Unlike RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks), which process sequential data sequentially, BERT utilizes the Transformer architecture, enabling efficient computation through parallel processing.\n",
        "\n",
        "### <font color='red'>**Pretraining:** </font>\n",
        "During pretraining, BERT learns contextual representations through masked language modeling and next sentence prediction tasks, leveraging large-scale unlabeled data.\n",
        "### <font color='red'>**Fine-tuning:** </font>\n",
        "After pretraining, BERT can be fine-tuned on downstream tasks by adding task-specific layers and training on labeled data. Fine-tuning BERT's pretrained representations often leads to state-of-the-art performance on various natural language processing tasks."
      ],
      "metadata": {
        "id": "75qAyvC1st2z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Pre-training and Fine-tuning](https://github.com/Rekha215/Pytorch-Basics/blob/main/Screenshot%202024-04-04%20130426.png?raw=true)\n",
        "\n"
      ],
      "metadata": {
        "id": "ltFFibAQCSd5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Embeddings](https://github.com/Rekha215/Pytorch-Basics/blob/main/Screenshot%202024-04-04%20130513.png?raw=true)"
      ],
      "metadata": {
        "id": "O12sP_wtC2xf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color='green'>**Tutorial components** </font>\n",
        "\n",
        "**1. Predictions using existing fine-tuned model**\n",
        "\n",
        "**2. Fine-tuning of bert-base-uncased using AG_NEWS dataset**\n",
        "\n",
        "  2.1 Load the dataset from datasets\n",
        "\n",
        "  2.2 Tokenizing samples\n",
        "\n",
        "  2.3 Instantiating bert model (bert-base-uncased or bert-large-uncased)\n",
        "\n",
        "  2.4 Creating data loaders\n",
        "\n",
        "  2.5 Training\n",
        "\n",
        "  2.6 Evaluation"
      ],
      "metadata": {
        "id": "Xne06ZL8u1mP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip -q install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HP_6Qx_YAsiQ",
        "outputId": "5df72b07-e6f8-4807-e72e-a6e94042f5ea"
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      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from datasets import load_dataset\n",
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "from tqdm import tqdm\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "oWN1T5bUIL84"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. Predictions using existing Fine-Tuned model on AG_NEWS dataset**\n",
        "\n",
        "https://huggingface.co/fabriceyhc/bert-base-uncased-ag_news\n"
      ],
      "metadata": {
        "id": "hMyPiaS7sa9R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "model_name = \"fabriceyhc/bert-base-uncased-ag_news\"\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
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        "outputId": "97a4a38b-f112-419e-d9c0-bd6266dd8ac2"
      },
      "execution_count": 3,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/919 [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example news articles\n",
        "news_articles = [\n",
        "    \"President Trump visits Japan for a summit meeting.\",\n",
        "    \"The new iPhone is expected to be released next month.\",\n",
        "    \"Scientists discover a new species of dinosaur in Argentina.\",\n",
        "    \"Stock market experiences a major crash, causing panic among investors.\"\n",
        "]"
      ],
      "metadata": {
        "id": "-1qqJndVzpUx"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# getting tokens of example news articles\n",
        "for i in range(len(news_articles)):\n",
        "  tokens = tokenizer.tokenize(news_articles[i])\n",
        "  print(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jP8113iwzrzM",
        "outputId": "88e4ec86-31f4-46df-da99-6e2aab077b4c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['president', 'trump', 'visits', 'japan', 'for', 'a', 'summit', 'meeting', '.']\n",
            "['the', 'new', 'iphone', 'is', 'expected', 'to', 'be', 'released', 'next', 'month', '.']\n",
            "['scientists', 'discover', 'a', 'new', 'species', 'of', 'dinosaur', 'in', 'argentina', '.']\n",
            "['stock', 'market', 'experiences', 'a', 'major', 'crash', ',', 'causing', 'panic', 'among', 'investors', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize the news articles\n",
        "encoded_articles = tokenizer(news_articles, truncation=True, padding=True, return_tensors='pt')\n",
        "\n",
        "print(\"Encoded Articles \\n\", encoded_articles)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTsqCxiIzupn",
        "outputId": "a3a5aec1-f452-44ae-ada1-b2e21f5d0ed9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoded Articles \n",
            " {'input_ids': tensor([[  101,  2343,  8398,  7879,  2900,  2005,  1037,  6465,  3116,  1012,\n",
            "           102,     0,     0,     0],\n",
            "        [  101,  1996,  2047, 18059,  2003,  3517,  2000,  2022,  2207,  2279,\n",
            "          3204,  1012,   102,     0],\n",
            "        [  101,  6529,  7523,  1037,  2047,  2427,  1997, 15799,  1999,  5619,\n",
            "          1012,   102,     0,     0],\n",
            "        [  101,  4518,  3006,  6322,  1037,  2350,  5823,  1010,  4786,  6634,\n",
            "          2426,  9387,  1012,   102]]), 'token_type_ids': tensor([[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],\n",
            "        [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]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get model predictions\n",
        "with torch.no_grad():\n",
        "    outputs = model(**encoded_articles)\n",
        "\n",
        "print(\"Outputs \\n\", outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uumhq0kezxyr",
        "outputId": "c4df81dd-50d4-4680-8420-95adf53c05ad"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outputs \n",
            " SequenceClassifierOutput(loss=None, logits=tensor([[ 5.7878, -2.2241, -1.8262, -2.2474],\n",
            "        [-0.7577, -4.3739, -0.8240,  4.6821],\n",
            "        [ 0.3683, -4.0031, -1.6276,  4.1006],\n",
            "        [ 0.8006, -4.8784,  3.0927, -1.0170]]), hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the predicted class indices\n",
        "predicted_indices = torch.argmax(outputs.logits, dim=1)"
      ],
      "metadata": {
        "id": "3QYdl_IWlHN9"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(predicted_indices)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvZS7vB4lzL6",
        "outputId": "5e05f8e8-8dfc-4729-c857-e0e418a187ee"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0, 3, 3, 2])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. BERT-base-uncased Fine-Tuning using ag_news dataset**\n",
        "\n",
        "Dataset Link - https://huggingface.co/datasets/ag_news\n",
        "\n",
        "Bert-base-uncased paper Link - https://arxiv.org/pdf/1810.04805.pdf\n"
      ],
      "metadata": {
        "id": "ZbtV4RBunLKP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.1 Load the dataset from datasets**"
      ],
      "metadata": {
        "id": "tMfFWpXv05G0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load AG News dataset\n",
        "dataset = load_dataset(\"ag_news\")\n",
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
          "referenced_widgets": [
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        },
        "id": "EWeX1-OdIOT5",
        "outputId": "697be050-95ab-467f-924f-74fd5eb04392"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.07k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c27ffa027b5c4c418a75480a41c2cda3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/18.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62fa2c589f1e49b48715f640634d232d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.23M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "478530150564426daefc523093a97468"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/120000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce2ac6741e94492aa9efae10ffea38d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/7600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1326b2a5a2b446fba4ea83673d30c9d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['text', 'label'],\n",
            "        num_rows: 120000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['text', 'label'],\n",
            "        num_rows: 7600\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract train, validation, and test data\n",
        "train_texts = dataset[\"train\"][\"text\"]\n",
        "train_labels = dataset[\"train\"][\"label\"]\n",
        "val_texts = dataset[\"test\"][\"text\"]\n",
        "val_labels = dataset[\"test\"][\"label\"]\n",
        "test_texts = dataset[\"test\"][\"text\"]\n",
        "test_labels = dataset[\"test\"][\"label\"]"
      ],
      "metadata": {
        "id": "ae5ObBtYIhGF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.2 Tokenizing samples**"
      ],
      "metadata": {
        "id": "WkU0z4Vq0_2n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a simple dataset class for AG News\n",
        "class AGNewsDataset(Dataset):\n",
        "    def __init__(self, tokenizer, texts, labels):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = self.texts[idx]\n",
        "        label = self.labels[idx]\n",
        "\n",
        "        encoded_inputs = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            add_special_tokens=True,\n",
        "            padding='max_length',\n",
        "            max_length=128,\n",
        "            truncation=True,\n",
        "            return_tensors='pt'\n",
        "        )\n",
        "\n",
        "        input_ids = encoded_inputs['input_ids'].squeeze()\n",
        "        attention_mask = encoded_inputs['attention_mask'].squeeze()\n",
        "\n",
        "        return {\n",
        "            'input_ids': input_ids,\n",
        "            'attention_mask': attention_mask,\n",
        "            'label': label\n",
        "        }"
      ],
      "metadata": {
        "id": "GrW8PoegIYDU"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### **2.3 Instantiating bert model (bert-base-uncased or bert-large-uncased)**"
      ],
      "metadata": {
        "id": "YtZ1BaLP1EtM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize tokenizer and model\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=4).to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213,
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        "id": "lSBfyRRg1-uu",
        "outputId": "8b921efd-acd9-4b08-b4f1-81136ac3efe7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "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,
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          },
          "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,
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          },
          "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,
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          "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": "7d5188bd33ee485c91f099eb5d3b1237"
            }
          },
          "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": "4f449ad9814c4c4ca96eecb078a85f7f"
            }
          },
          "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": "markdown",
      "source": [
        "### **2.4 Creating data loaders**"
      ],
      "metadata": {
        "id": "gWpU7dOK2EHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create train dataset and dataloader\n",
        "train_dataset = AGNewsDataset(tokenizer, train_texts, train_labels)\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
        "\n",
        "# Create validation dataset and dataloader\n",
        "val_dataset = AGNewsDataset(tokenizer, val_texts, val_labels)\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
        "\n",
        "# Create test dataset and dataloader\n",
        "test_dataset = AGNewsDataset(tokenizer, test_texts, test_labels)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)"
      ],
      "metadata": {
        "id": "MEj-jkT_IqQc"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.5 Training**"
      ],
      "metadata": {
        "id": "uFTga4sB1ik1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5, weight_decay=0.1, eps=1e-8)"
      ],
      "metadata": {
        "id": "cFYeHBlIIv3X"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0S5W5hYDAp30",
        "outputId": "c4eff8d3-9599-4523-cf8f-27ebb0844003"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1/3 - Training: 100%|██████████| 1875/1875 [11:31<00:00,  2.71it/s, loss=0.246]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1/3 - Training Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9365    0.9111    0.9236     30000\n",
            "           1     0.9728    0.9780    0.9754     30000\n",
            "           2     0.8845    0.8832    0.8838     30000\n",
            "           3     0.8836    0.9042    0.8938     30000\n",
            "\n",
            "    accuracy                         0.9191    120000\n",
            "   macro avg     0.9194    0.9191    0.9192    120000\n",
            "weighted avg     0.9194    0.9191    0.9192    120000\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1/3 - Validation: 100%|██████████| 119/119 [00:20<00:00,  5.94it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1/3 - Validation Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9559    0.9479    0.9519      1900\n",
            "           1     0.9792    0.9911    0.9851      1900\n",
            "           2     0.9176    0.9084    0.9130      1900\n",
            "           3     0.9132    0.9189    0.9161      1900\n",
            "\n",
            "    accuracy                         0.9416      7600\n",
            "   macro avg     0.9415    0.9416    0.9415      7600\n",
            "weighted avg     0.9415    0.9416    0.9415      7600\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 2/3 - Training: 100%|██████████| 1875/1875 [11:29<00:00,  2.72it/s, loss=0.0788]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 2/3 - Training Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9671    0.9497    0.9583     30000\n",
            "           1     0.9879    0.9917    0.9898     30000\n",
            "           2     0.9288    0.9152    0.9219     30000\n",
            "           3     0.9150    0.9414    0.9280     30000\n",
            "\n",
            "    accuracy                         0.9495    120000\n",
            "   macro avg     0.9497    0.9495    0.9495    120000\n",
            "weighted avg     0.9497    0.9495    0.9495    120000\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 2/3 - Validation: 100%|██████████| 119/119 [00:20<00:00,  5.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 2/3 - Validation Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9698    0.9463    0.9579      1900\n",
            "           1     0.9879    0.9905    0.9892      1900\n",
            "           2     0.9314    0.9000    0.9154      1900\n",
            "           3     0.8978    0.9474    0.9219      1900\n",
            "\n",
            "    accuracy                         0.9461      7600\n",
            "   macro avg     0.9467    0.9461    0.9461      7600\n",
            "weighted avg     0.9467    0.9461    0.9461      7600\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3/3 - Training: 100%|██████████| 1875/1875 [11:30<00:00,  2.71it/s, loss=0.185]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 3/3 - Training Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9780    0.9614    0.9697     30000\n",
            "           1     0.9924    0.9953    0.9938     30000\n",
            "           2     0.9463    0.9343    0.9403     30000\n",
            "           3     0.9319    0.9568    0.9442     30000\n",
            "\n",
            "    accuracy                         0.9619    120000\n",
            "   macro avg     0.9621    0.9619    0.9620    120000\n",
            "weighted avg     0.9621    0.9619    0.9620    120000\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3/3 - Validation: 100%|██████████| 119/119 [00:20<00:00,  5.93it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 3/3 - Validation Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9667    0.9474    0.9569      1900\n",
            "           1     0.9863    0.9884    0.9874      1900\n",
            "           2     0.9160    0.9121    0.9140      1900\n",
            "           3     0.9104    0.9305    0.9204      1900\n",
            "\n",
            "    accuracy                         0.9446      7600\n",
            "   macro avg     0.9449    0.9446    0.9447      7600\n",
            "weighted avg     0.9449    0.9446    0.9447      7600\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Training loop\n",
        "epochs = 3\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    model.train()\n",
        "    train_predicted_labels = []\n",
        "    train_true_labels = []\n",
        "\n",
        "    progress_bar = tqdm(train_dataloader, desc=f\"Epoch {epoch + 1}/{epochs} - Training\")\n",
        "    for batch in progress_bar:\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        labels = batch['label'].to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
        "        loss = criterion(outputs.logits, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        train_predicted_label = torch.argmax(outputs.logits, dim=1)\n",
        "        train_predicted_labels.extend(train_predicted_label.cpu().numpy())\n",
        "        train_true_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "        progress_bar.set_postfix({'loss': loss.item()})\n",
        "\n",
        "    train_classification_rep = classification_report(train_true_labels, train_predicted_labels, digits=4)\n",
        "    print(f\"\\nEpoch {epoch + 1}/{epochs} - Training Classification Report:\\n{train_classification_rep}\")\n",
        "\n",
        "    # Validation loop\n",
        "    model.eval()\n",
        "    val_predicted_labels = []\n",
        "    val_true_labels = []\n",
        "\n",
        "    progress_bar = tqdm(val_dataloader, desc=f\"Epoch {epoch + 1}/{epochs} - Validation\")\n",
        "    with torch.no_grad():\n",
        "        for batch in progress_bar:\n",
        "            input_ids = batch['input_ids'].to(device)\n",
        "            attention_mask = batch['attention_mask'].to(device)\n",
        "            labels = batch['label'].to(device)\n",
        "\n",
        "            outputs = model(input_ids, attention_mask=attention_mask)\n",
        "            val_predicted_label = torch.argmax(outputs.logits, dim=1)\n",
        "            val_predicted_labels.extend(val_predicted_label.cpu().numpy())\n",
        "            val_true_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "    val_classification_rep = classification_report(val_true_labels, val_predicted_labels, digits=4)\n",
        "    print(f\"\\nEpoch {epoch + 1}/{epochs} - Validation Classification Report:\\n{val_classification_rep}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.6 Evaluation**"
      ],
      "metadata": {
        "id": "VvUt78fU1oCJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Test set evaluation\n",
        "test_predicted_labels = []\n",
        "test_true_labels = []\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    for batch in tqdm(test_dataloader, desc=\"Test\"):\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        labels = batch['label'].to(device)\n",
        "\n",
        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
        "        test_predicted_label = torch.argmax(outputs.logits, dim=1)\n",
        "\n",
        "        test_predicted_labels.extend(test_predicted_label.cpu().numpy())\n",
        "        test_true_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "test_classification_rep = classification_report(test_true_labels, test_predicted_labels, digits=4)\n",
        "print(f\"\\nTest Set Classification Report:\\n{test_classification_rep}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqhACJZBIWUv",
        "outputId": "6c57ba16-810e-4ea4-a36e-08fe6274eac2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Test: 100%|██████████| 7600/7600 [01:26<00:00, 87.98it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test Set Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9667    0.9474    0.9569      1900\n",
            "           1     0.9863    0.9884    0.9874      1900\n",
            "           2     0.9160    0.9121    0.9140      1900\n",
            "           3     0.9104    0.9305    0.9204      1900\n",
            "\n",
            "    accuracy                         0.9446      7600\n",
            "   macro avg     0.9449    0.9446    0.9447      7600\n",
            "weighted avg     0.9449    0.9446    0.9447      7600\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    }
  ]
}