BERT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a href=\"https://colab.research.google.com/github/adamelkholyy/imdb-sentiment-analysis/blob/main/BERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0KW48QAgdy3"
      },
      "source": [
        "# BERT for Sentiment Analysis on IMDB Movie Review Dataset\n",
        "<strong>Adam El Kholy</strong> \\\n",
        "<strong>University of Bath</strong> \\\n",
        "Last Updated: <strong>02/12/2023</strong>\n",
        "\n",
        "Free to use under the Apache 2.0 license \\\n",
        "For use in Google Colab with the [IMDB reviews](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) dataset available on Kaggle \n",
        "\n",
        "The following notebook allows you to train and evaluate the BERT cased and uncased models for the task of sentiment analysis using on the IMDB movie dataset\n",
        "\n",
        "See ```main.ipynb``` for further model evaluation on the IMDB dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import torch\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Loading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EsoGsikIgdy6"
      },
      "outputs": [],
      "source": [
        "def read_corpus(directory):\n",
        "    files = [f for f in os.listdir(directory) if f.endswith('.txt')]\n",
        "    corpus = []\n",
        "    for file in files:\n",
        "        with open(os.path.join(directory, file), 'r', encoding='utf-8') as f:\n",
        "            document = f.read()\n",
        "            corpus.append(document)\n",
        "    return corpus"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "I've long wanted to see this film, being a fan of both Peter Cushing and David McCallum. I agree that the romantic sub-plot was a waste of time, but the talent of McCallum shines through this juvie role\n",
            "While this film certainly does possess the stench of a bad film, it's surprisingly watchable on several levels. First, for old movie fans, it's interesting to see the leading role played by Dean Jagger \n"
          ]
        }
      ],
      "source": [
        "# unpack data from drive to google Colab due \n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "positive_corpus = read_corpus(\"drive/MyDrive/nlp/data/pos/\")\n",
        "negative_corpus = read_corpus(\"drive/MyDrive/nlp/data/neg/\")\n",
        "corpus = positive_corpus + negative_corpus\n",
        "positive_labels = len(positive_corpus)\n",
        "negative_labels = len(negative_corpus)\n",
        "corpus_length = len(corpus)\n",
        "\n",
        "# sanity check\n",
        "print(positive_corpus[0][:202])\n",
        "print(negative_corpus[1][:202])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fNWGRRmKgdzC"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "r_seed = 563\n",
        "np.random.seed(r_seed)\n",
        "\n",
        "def get_test_train_dev_split(X):\n",
        "    y = np.concatenate([np.ones(positive_labels), np.zeros(negative_labels)])\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80,\n",
        "                                                        shuffle=True, random_state=r_seed)\n",
        "\n",
        "    X_train, X_dev, y_train, y_dev = train_test_split(X_train, y_train,\n",
        "                                                        test_size=0.15,\n",
        "                                                       shuffle=True, random_state=r_seed)\n",
        "\n",
        "    # 68% train, 12% validation, 20% test\n",
        "    return X_train, y_train, X_test, y_test, X_dev, y_dev"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zD7haFzrgdzD"
      },
      "source": [
        "# Training BERT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmKHbNjDgdzH"
      },
      "source": [
        "<strong>References</strong> <br>\n",
        "[1] https://huggingface.co/transformers/v3.2.0/custom_datasets.html <br>\n",
        "[2] https://huggingface.co/docs/transformers/tasks/sequence_classification <br>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLihv0uBfXtH",
        "outputId": "727c4185-056e-4d1f-d6b1-d4564d339dab"
      },
      "outputs": [],
      "source": [
        "! pip install -U accelerate\n",
        "! pip install -U transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dLNwLtoxgdzI"
      },
      "outputs": [],
      "source": [
        "X_train, y_train, X_test, y_test, X_dev, y_dev = get_test_train_dev_split(corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        },
        "id": "2A74k08wgdzJ",
        "outputId": "bd23e7a8-eb69-4d66-f7fd-d03f17acc35e"
      },
      "outputs": [],
      "source": [
        "import accelerate\n",
        "import transformers\n",
        "from transformers import DistilBertTokenizerFast\n",
        "\n",
        "\"\"\"\n",
        "# uncased tokenizer\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased') \n",
        "\"\"\"\n",
        "\n",
        "# tokenise and encode the dataset\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')\n",
        "train_encodings = tokenizer(X_train, truncation=True, padding=True)\n",
        "val_encodings = tokenizer(X_dev, truncation=True, padding=True)\n",
        "test_encodings = tokenizer(X_test, truncation=True, padding=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7WLeh--lgdzK"
      },
      "outputs": [],
      "source": [
        "# loads the set into the class wrapper for use by BERT\n",
        "class IMDbDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\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",
        "        item['labels'] = torch.tensor(self.labels[idx]).long()\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "train_dataset = IMDbDataset(train_encodings, y_train)\n",
        "val_dataset = IMDbDataset(val_encodings, y_dev)\n",
        "test_dataset = IMDbDataset(test_encodings, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iA00bDt3lK57",
        "outputId": "fbbfcd53-a40b-4998-96d7-869e2238f5ad"
      },
      "outputs": [
        {
          "data": {
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              " 'labels': tensor(0)}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_dataset[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cP0BI7zBgdzM"
      },
      "outputs": [],
      "source": [
        "id2label = {0: \"NEGATIVE\", 1: \"POSITIVE\"}\n",
        "label2id = {\"NEGATIVE\": 0, \"POSITIVE\": 1}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TrainOutput(\n",
            "global_step=510, \n",
            "training_loss=0.3883705898827198, \n",
            "metrics={\n",
            "    'train_runtime': 374.1151, \n",
            "    'train_samples_per_second': 21.811, \n",
            "    'train_steps_per_second': 1.363, \n",
            "    'total_flos': 1080933973032960.0, \n",
            "    'train_loss': 0.3883705898827198, \n",
            "    'epoch': 3.0\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',          \n",
        "    num_train_epochs=3,              \n",
        "    per_device_train_batch_size=16,  \n",
        "    per_device_eval_batch_size=64,   \n",
        "    warmup_steps=500,                \n",
        "    weight_decay=0.01,              \n",
        "    logging_dir='./logs',            \n",
        "    logging_steps=10,\n",
        ")\n",
        "\n",
        "\"\"\"\n",
        "# uncased model\n",
        "model = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\") \n",
        "\"\"\"\n",
        "model = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-cased\")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,                         \n",
        "    args=training_args,                  \n",
        "    train_dataset=train_dataset,        \n",
        "    eval_dataset=val_dataset             \n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Evaluating BERT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-pXfzmqngdzT"
      },
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      "source": [
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "def evaluate_model( test_labels, predictions):\n",
        "    print(f\"accuracy: {accuracy_score(test_labels, predictions)}\")\n",
        "    print(f\"precision: {precision_score(test_labels, predictions)}\")\n",
        "    print(f\"recall: {recall_score(test_labels, predictions)}\")\n",
        "    print(f\"f1 score: {f1_score(test_labels, predictions)}\")\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "accuracy: 0.84375\n",
            "precision: 0.8032786885245902\n",
            "recall: 0.8789237668161435\n",
            "f1 score: 0.8394004282655245\n"
          ]
        }
      ],
      "source": [
        "# development set evaluation\n",
        "predictions = trainer.predict(val_dataset)\n",
        "predicted_classes = predictions.predictions.argmax(axis=1)\n",
        "evaluate_model(y_dev, predicted_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "accuracy: 0.8825\n",
            "precision: 0.8657407407407407\n",
            "recall: 0.9121951219512195\n",
            "f1 score: 0.8883610451306413\n"
          ]
        }
      ],
      "source": [
        "# test set evaluation\n",
        "predictions = trainer.predict(test_dataset)\n",
        "predicted_classes = predictions.predictions.argmax(axis=1)\n",
        "evaluate_model(y_test, predicted_classes)"
      ]
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