{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting happytransformer\n", " Using cached happytransformer-3.0.0-py3-none-any.whl.metadata (4.4 kB)\n", "Requirement already satisfied: matplotlib in c:\\python312\\lib\\site-packages (3.9.2)\n", "Collecting torch>=1.0 (from happytransformer)\n", " Using cached torch-2.5.1-cp312-cp312-win_amd64.whl.metadata (28 kB)\n", "Requirement already satisfied: tqdm>=4.43 in c:\\python312\\lib\\site-packages (from happytransformer) (4.67.1)\n", "Collecting transformers<5.0.0,>=4.30.1 (from happytransformer)\n", " Using cached transformers-4.48.1-py3-none-any.whl.metadata (44 kB)\n", "Collecting datasets<3.0.0,>=2.13.1 (from happytransformer)\n", " Using cached datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n", "Requirement already satisfied: sentencepiece in c:\\python312\\lib\\site-packages (from happytransformer) (0.2.0)\n", "Requirement already 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Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import pickle\n", "import matplotlib.pyplot as plt\n", "from happytransformer import HappyTextToText, TTTrainArgs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "01/25/2025 23:54:32 - INFO - happytransformer.happy_transformer - Using device: cpu\n" ] } ], "source": [ "happy_tt = HappyTextToText(\"T5\", \"t5-base\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "01/26/2025 00:28:57 - INFO - happytransformer.happy_transformer - Preprocessing dataset...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Evaluating the model BEFORE training...\n" ] }, { "data": { "text/html": [ "\n", "
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\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eval_args = TTTrainArgs(\n", " batch_size=1,\n", " max_input_length=512,\n", " max_output_length=150\n", ")\n", "\n", "print(\"Evaluating the model BEFORE training...\")\n", "eval_results_before = happy_tt.eval(\"Dataset/eval.csv\", args=eval_args)\n", "print(\"Evaluation Loss (Before Training):\", eval_results_before.loss)\n", "\n", "with open(\"eval_results_before.txt\", \"w\") as f:\n", " f.write(\"Evaluation Results Before Training:\\n\")\n", " f.write(str(eval_results_before))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Train Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_args = TTTrainArgs(\n", " batch_size=8,\n", " num_train_epochs=5,\n", " max_input_length=512, \n", " max_output_length=150,\n", " eval_ratio=0.1\n", ")\n", "\n", "print(\"Training the model...\")\n", "training_losses = happy_tt.train(\"Dataset/train.csv\", args=train_args)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluate the model on the test set" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Evaluating the model AFTER training...\")\n", "eval_results_after = happy_tt.eval(\"Dataset/eval.csv\", args=eval_args)\n", "eval_loss_after = eval_results_after['eval_loss']\n", "print(\"Evaluation Loss (After Training):\", eval_loss_after)\n", "\n", "with open(\"eval_results_after.txt\", \"w\") as f:\n", " f.write(\"Evaluation Results After Training:\\n\")\n", " f.write(str(eval_results_after))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "epochs = list(range(1, len(training_losses) + 1))\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(epochs, training_losses, label=\"Training Loss\", marker='o')\n", "plt.axhline(y=eval_loss_before, color='orange', linestyle='--', label=f\"Evaluation Loss Before ({eval_loss_before:.4f})\")\n", "plt.axhline(y=eval_loss_after, color='green', linestyle='--', label=f\"Evaluation Loss After ({eval_loss_after:.4f})\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.title(\"Training and Evaluation Losses\")\n", "plt.legend()\n", "plt.grid(True)\n", "\n", "plt.savefig(\"training_eval_comparison.png\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Final Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"\\nFinal Results:\")\n", "print(f\"Evaluation Loss Before Training: {eval_loss_before:.4f}\")\n", "print(f\"Evaluation Loss After Training: {eval_loss_after:.4f}\")" ] } ], "metadata": { "accelerator": "GPU", "colab": { "authorship_tag": "ABX9TyPRpNTtj0LgyUyd+wpJZ+Be", "collapsed_sections": [], "mount_file_id": "1ovKyRAAM9p3HfsAc9wGcZNn8TDhSeUtg", 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