diff --git "a/imdb_reviews_bert.ipynb" "b/imdb_reviews_bert.ipynb" new file mode 100644--- /dev/null +++ "b/imdb_reviews_bert.ipynb" @@ -0,0 +1,5437 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "in6jOntrSkcb" + }, + "source": [ + "## Install and Import Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bvs3wUePiAwn", + "outputId": "8df2963d-742f-4581-aeea-5544dbad1c01" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.3)\n", + "Collecting datasets\n", + " Downloading datasets-3.3.0-py3-none-any.whl.metadata (19 kB)\n", + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.18.0)\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n", + 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tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from transformers import TFAutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding\n", + "from datasets import load_dataset\n", + "\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x7jmGz8WSo4v" + }, + "source": [ + "## Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365, + "referenced_widgets": [ + "9b1a7ccfb45249e393ad11a6042d23b5", + "49134400fb514ed48a176b505cb75491", + "a6e6000a4d57492e8cfa8bddcde2b8fc", + "b766878e2745498fb3e2dd2bae890fe8", + "a68fd91a58384e729d95b8b165c6ed82", + "1c08e064916547ec802325ca975555f9", + "f6ea38063e6a458682124bc0c5d562b9", + "90d0dd6cf1034061b5ca2f88c29fc7ea", + "ea0fdbef63424e61a738287542094f86", + "5f072c25118b4b18bf2a2c8563bbfc74", + "50634e280dab40adbd72dfa875cb1856", + "3a05a3a2214a413993f940c456ed0b3c", + "4b86d40e0efa4c63911335b548a41850", + "5cb483386cc24cbab802b520352513a1", + "d06bf7440f3c4227a5198a985d314ab4", + "05a2050841a34f4e8f9c5388077f41c4", + "3897174b7f72448fa89613ad9285e9c0", + "1d3076084c484a8a9a55e80be0448d1a", + "fa8faa5c8f0f4aa8aa4d021680196fa4", + "34e935f5fb6242a48fcc95fe42cced24", + "583c482f09cc4709b18c7b6f3acf5faf", + "6d50676317344131902c91b1087dafa9", + "68957f4ff37d428e91cf1cacdec657e7", + "e68c87d2dd4c4341ae8af110a252311a", + "8dbc4e6544db4fe5a163617b10f81895", + "c83703a8183b4cf18037be81592abd79", + "21081a4e78f646329687a971f8d92ee7", + "be14137203ed4a8aa31a9aee9260c1b9", + "0a1d7298529f46fbabc8a78b828356b0", + "b266d2c503c54562bf3da4a94e2cf693", + "3cb0937e864144ed96b50a4fc2e59729", + "987ffb2189d94c26b92b6a50bd938118", + "4517ad7a7cfe4b09bae288b8689bbf8f", + "819582d519634970b74916886dd340fd", + "59e42281667742afbd18a164b30e3f83", + "7c9e61a3907641b5b3293e3ebf5f1b23", + "9e15d739329d41b9ab3c17d43497a113", + "844410bde9214188abbce34c900d2d5d", + "deafe22df5e947d0a5af1a50bc6e3126", + "b38b4005ecc44506bc79dcb8d99e6836", + "62ef373a334648b59518fb24f0e6816c", + "ed0eb203a8b646f7bd2ebd9feeb3cd1f", + "53c9a84b25214a8996ebe547fef1fedd", + "28c00f4482db41ab9b5620715fcfdd5e", + "670d4591db2e46c2ac2d4a2ae0a059f7", + "2560cbcf73f241e9ae8f0446c87bbddb", + "58a9b307da184547b154b9ec051f9b89", + "e3b4a5812b514e71b53cfb9992a83168", + "f2c82a38fc0a4d36892c410ec0242c8b", + "469da412938341089b699b71f672f6ed", + "9a02b74a0b2b429ba44cb0776ac23446", + "b70511be0a3441d0a794c420a53aabe7", + "c62a54e074a54b1692600291cea0da95", + "fa8e437e08f74c49bcbc2ddfbb32eef5", + "5ca107c329a745a6bd190f90d3096379", + "f8be97680933477e90f48ea489bbeec4", + "059306c9107b454da4e04ed74625fecc", + "cc9bd71bc08849ca80cf28bee14c8fea", + "4501c2409d534d6f9b0df8e2e677f921", + "3aa3127e85c34fdb8a40fdc3946b5f61", + "4ce2d474aa814976a30029636497fa95", + "6e95590cd9e045dbb22f1e05ada6e313", + "f36588f7cfea414c9b5eabfb0b22e538", + "1f3ffffca65f486e81b9a5b99189eb70", + "f6eb81b804934c4ea57cc9539039d3e4", + "27ec599b9c9244d8a358f963e5b5d492", + "78284b79083a43db8c92d78f0379eafb", + "9509decdf0224423a6a44911712aa9e0", + "227af09b705246429b0eced3ef3c97f2", + "94a5e3d77f6c40ab9f810c2c16447cb5", + "250902d2e3724ddb95914cf30448fdc1", + "5e3260139e66472c98d7749c4ef31146", + "f8ce48c8f4d84cf8a3c70eb393d08051", + "f008fac322a94c498c4f4bdc3a3a8105", + "81db6b93c96345c5b5c75f39482a5fcc", + "c5a06e8ab18f436e82b8bc4ffa840c54", + "a391be4fcfb34e3892dc2311da106d39" + ] + }, + "id": "DDAHlrtZ13s6", + "outputId": "ae0961b1-67cc-4d18-8a28-89d87ea70cb2" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: 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" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9b1a7ccfb45249e393ad11a6042d23b5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "README.md: 0%| | 0.00/7.81k [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12, 5))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "sns.lineplot(x = [epoch + 1 for epoch in history.epoch], y = history.history['accuracy'], label = 'Training Accuracy')\n", + "sns.lineplot(x = [epoch + 1 for epoch in history.epoch], y = history.history['val_accuracy'], label = 'Validation Accuracy')\n", + "plt.title(\"Accuracy\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "sns.lineplot(x = [epoch + 1 for epoch in history.epoch], y = history.history['loss'], label = 'Training Loss')\n", + "sns.lineplot(x = [epoch + 1 for epoch in history.epoch], y = history.history['val_loss'], label = 'Validation Loss')\n", + "plt.title(\"Loss\")\n", + "\n", + "plt.suptitle(\"Fine Tuned BERT Performance\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HOAWxJlLUSSS" + }, + "source": [ + "## Comparative Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Dx5SWyUcUR-b", + "outputId": "ea4d55cb-965d-4443-9330-e86ea0d53b54" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BERT Accuracy without Tuning : 0.52144\n", + "BERT Accuracy After Fine Tuning : 0.9262\n" + ] + } + ], + "source": [ + "print(\"BERT Accuracy without Tuning :\", pretrained_accuracy)\n", + "print(\"BERT Accuracy After Fine Tuning :\", tuned_accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IOXNFnnrtLeM" + }, + "source": [ + "## Example Uses" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "5zAEvJ1SsLDW" + }, + "outputs": [], + "source": [ + "model_path = 'Fine_Tuned_BERT'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s_uSUJ-5tNv7" + }, + "source": [ + "#### Loading Fine Tuned Model" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QZ8sGMPdsNJu", + "outputId": "f0747688-ed97-423e-d0af-f8018a2fadf4" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some layers from the model checkpoint at Fine_Tuned_BERT were not used when initializing TFBertForSequenceClassification: ['dropout_113']\n", + "- This IS expected if you are initializing TFBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing TFBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", + "All the layers of TFBertForSequenceClassification were initialized from the model checkpoint at Fine_Tuned_BERT.\n", + "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n" + ] + } + ], + "source": [ + "tuned_bert_user = TFAutoModelForSequenceClassification.from_pretrained(model_path)\n", + "tokenizer_user = AutoTokenizer.from_pretrained(model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "83DJnW5qtUEL" + }, + "source": [ + "#### Sentiment Prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "PaW2oYApsW1-" + }, + "outputs": [], + "source": [ + "def predict_sentiment(text):\n", + " tokens = tokenizer_user(text, padding=True, truncation=True, return_tensors=\"tf\")\n", + " preds = tuned_bert_user.predict(tokens).logits\n", + " pred_class = np.argmax(preds, axis=1)[0]\n", + " return \"Positive\" if pred_class == 1 else \"Negative\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "beugsaWLtW2l" + }, + "source": [ + "#### Example" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lFamGh8ssitl", + "outputId": "ace41d99-2db4-43c9-df85-b6f58b2160a8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 63ms/step\n", + "Sentiment of ' This is a fantastic movie! ' : Positive\n", + "1/1 [==============================] - 0s 70ms/step\n", + "Sentiment of ' This movie was terrible. ' : Negative\n" + ] + } + ], + "source": [ + "text = \"This is a fantastic movie!\"\n", + "sentiment = predict_sentiment(text)\n", + "print(\"Sentiment of '\", text, \"' :\", sentiment)\n", + "\n", + "text = \"This movie was terrible.\"\n", + "sentiment = predict_sentiment(text)\n", + "print(\"Sentiment of '\", text, \"' :\", sentiment)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "K_YJZIhbs_F4" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + }, + "widgets": { + 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