{ "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", "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.4)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.17.0)\n", 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xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n", "Successfully installed datasets-3.3.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n" ] } ], "source": [ "pip install transformers datasets tensorflow scikit-learn numpy" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "xaGG6MUR1pUq" }, "outputs": [], "source": [ "import 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. 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