diff --git "a/FineTuning.ipynb" "b/FineTuning.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning.ipynb" @@ -0,0 +1,5360 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "PnK_4azs5LBd" + }, + "source": [ + "# **1. Installation of Required Libraries**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dAq-cgrU5QRJ", + "outputId": "58e436b6-734e-4dd5-85d0-71e559ccaedd" + }, + "outputs": [], + "source": [ + "%pip install transformers datasets torch scikit-learn -q\n", + "%pip install transformers[torch] -q\n", + "%pip install accelerate -U -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Gef1it6r5dbW" + }, + "source": [ + "# **2. Implementation Code**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "q7lTlKG55Wmz" + }, + "outputs": [], + "source": [ + "import torch\n", + "from datasets import load_dataset\n", + "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n", + "from sklearn.metrics import accuracy_score, precision_recall_fscore_support" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "f7e274267c304182875fc0dbcc1cfcef", + "01684c31164f4492b071a33a95600d9b", + "e4203bd3c36944f198bc55e0ea24df24", + "46cfc36544844967baae34b374bb1bfd", + "40b56c0545774429a82cfffe5f4feb21", + "3f35a887fb764256ac34b1ecd7fc41ae", + "8172756bc2da40bbb7f81d7a6f36699f", + "a50648375b6343139e5f0532bbebcd85", + "137088c829e646fe98fe513be4e8b840", + "8ef797a2a96f4a63b906439d336d6a13", + 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"e93663416ee24668a870fc5e6100be3e", + "704fe83d3b364de9b2acafab22d905d7", + "c1aca84324e744a1a6d9813c73e316c9", + "88943d24e2444973af7fee4cb83c108d", + "ede9af7988b744afbcde85c9dbcb16b6", + "a9501721caf94fcfa8648d163c6b63d2", + "aacfacc91fc246f59baa8ec965410efd" + ] + }, + "id": "PGoLzOa15u3b", + "outputId": "89654562-f5c9-42f5-86ef-b6db95043edf" + }, + "outputs": [], + "source": [ + "dataset = load_dataset(\"imdb\")\n", + "model_name = \"distilbert-base-uncased\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AahEP4v2HtaW" + }, + "outputs": [], + "source": [ + "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "device = torch.device(\"mps\" if torch.backends.mps.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "device" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 113, + "referenced_widgets": [ + "504c5b0df15e429db4aa4b24502df830", + "92efa50f401b4358aaa7c0123bb3d888", + "b65efca5f73d49c88a27423bfaf620e2", + "34b758995227462d986887694b846c26", + "42de283d319740778d9fb1816c9eb345", + "c5e194fcf6bc4f4cb7ac1f441089ff2e", + "078ec68222354cd3a074b22559b6e629", + "da17f10f53434952b3e641ab05970eff", + "afe594b718bc41d298d94b7d0de662db", + "840f6918211141dbb35c7155a47a2f1e", + "d0df2b4085944b57b31c698fa4551264", + "e569e3698f58422a8d4a6d0f7fbe5709", + "1fc0a3c731e54341a88b93d92f46796f", + "10c9e51cf67541959d81040c01715452", + "dc21bd8c6a23484dba4e039ce48e9ab5", + "e8a5e104151a41fd94b8ea981490154b", + "43f1f28576af458ab2d82176ffbafb86", + "59e7c93af8fc4c01b1aa382a1627fb29", + "be45e8df21384663b7f4abed6df00baa", + "ae1bf853bdd14cb7ae2b5b4a449b1853", + "230becb0d6bf451e8909b3720abfea8a", + "c8e208e13f244e66927507d490dfc90a", + "9884c1ce75944357af8fa26020fcaa79", + "7a3f0ee287c24466864fc03ac646956c", + "fe058673d48b4f388700e71de5b99162", + "8defa4b5de83427ca22b9fb91b84fb14", + "df5b8c9af1cf4f74a43a91016936f80c", + "e6e1b11d89074967bd7d4c0449ab1685", + "3fcf0698c174408fb5dacbf392bbf29b", + "75a1f1fda2b849548a4a01f1d8639564", + "76dedbc86d69405b90cd497cedf0089c", + "09304d2544f146b28a48d14511ca20de", + "7f7cef22cec640db839159b2aa20cb45" + ] + }, + "id": "UZs355r-52fp", + "outputId": "3b88332e-0877-4ea5-f834-bcdcd5b42b00" + }, + "outputs": [], + "source": [ + "def tokenize_function(example):\n", + " return tokenizer(example[\"text\"], padding=\"max_length\", truncation=True)\n", + "\n", + "tokenized_datasets = dataset.map(tokenize_function, batched=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dbWw2dD-58Ky" + }, + "outputs": [], + "source": [ + "train_dataset = tokenized_datasets[\"train\"]\n", + "test_dataset = tokenized_datasets[\"test\"]\n", + "\n", + "small_train_dataset = train_dataset.shuffle(seed=42).select(range(20000))\n", + "small_eval_dataset = test_dataset.shuffle(seed=42).select(range(5000))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N7caBVWU59i0", + "outputId": "e958a3e8-1c8c-45f5-c24e-0808e5a6e9a8" + }, + "outputs": [], + "source": [ + "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)\n", + "model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u6FezLZY6IZz", + "outputId": "70232730-d898-4d4b-c0b5-0742441541f1" + }, + "outputs": [], + "source": [ + "training_args = TrainingArguments(\n", + " output_dir=\"./results\",\n", + " evaluation_strategy=\"epoch\",\n", + " save_strategy=\"epoch\",\n", + " learning_rate=2e-5,\n", + " per_device_train_batch_size=16,\n", + " per_device_eval_batch_size=16,\n", + " num_train_epochs=1,\n", + " weight_decay=0.01,\n", + " logging_dir='./logs',\n", + " logging_steps=10,\n", + " load_best_model_at_end=True,\n", + " save_total_limit=2,\n", + " save_steps=500,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ACVm1fs46JTj" + }, + "outputs": [], + "source": [ + "def compute_metrics(pred):\n", + " labels = pred.label_ids\n", + " preds = pred.predictions.argmax(-1)\n", + " precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average=\"binary\")\n", + " acc = accuracy_score(labels, preds)\n", + " return {\"accuracy\": acc, \"precision\": precision, \"recall\": recall, \"f1\": f1}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yT6a4s8f6L5s" + }, + "outputs": [], + "source": [ + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=small_train_dataset,\n", + " eval_dataset=small_eval_dataset,\n", + " compute_metrics=compute_metrics,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141 + }, + "id": "WQTtO0906Otz", + "outputId": "e4e38b57-40e2-47b2-fd96-7577f1c98644" + }, + "outputs": [], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 37 + }, + "id": "VTB9fOXK6Rq7", + "outputId": "43f751f4-2a9b-41d6-dd9e-23087ef2d317" + }, + "outputs": [], + "source": [ + "eval_results = trainer.evaluate(eval_dataset=test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jwYpSh2W6UE5", + "outputId": "f0349b19-5ddf-433a-97b0-b3e11270d45b" + }, + "outputs": [], + "source": [ + "print(f\"Test Accuracy: {eval_results['eval_accuracy']:.4f}\")\n", + "print(f\"Test Precision: {eval_results['eval_precision']:.4f}\")\n", + "print(f\"Test Recall: {eval_results['eval_recall']:.4f}\")\n", + "print(f\"Test F1-score: {eval_results['eval_f1']:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YruBBViO6W4V", + "outputId": "67d496ad-ad28-4589-ea26-d274a9f512a3" + }, + "outputs": [], + "source": [ + "model.save_pretrained(\"./fine-tuned-model\")\n", + "tokenizer.save_pretrained(\"./fine-tuned-model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8KqgPo3Dhta" + }, + "source": [ + "# **3. Using the Model for Prediction**\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xPJuvRerDyq3", + "outputId": "87f5572c-97e4-4b6a-f07d-f43342f90ba7" + }, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", + "\n", + "model_name = \"./fine-tuned-model\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PvzjdLk0Dm1W" + }, + "outputs": [], + "source": [ + "def predict_sentiment(review_text):\n", + " inputs = tokenizer(review_text, padding=True, truncation=True, return_tensors=\"pt\").to(device)\n", + "\n", + " with torch.no_grad():\n", + " outputs = model(**inputs)\n", + "\n", + " logits = outputs.logits\n", + " predictions = torch.softmax(logits, dim=-1)\n", + "\n", + " predicted_label = torch.argmax(predictions, dim=-1).item()\n", + " sentiment = \"Positive\" if predicted_label == 1 else \"Negative\"\n", + "\n", + " return sentiment, predictions[0].cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BjgV2dU1DsMk", + "outputId": "5651b853-9c6a-4ca9-a289-8cc154416d00" + }, + "outputs": [], + "source": [ + "review_text = \"This movie was absolutely wonderful, with brilliant acting and a captivating story!\"\n", + "sentiment, probs = predict_sentiment(review_text)\n", + "\n", + "print(f\"Review: {review_text}\")\n", + "print(f\"Predicted Sentiment: {sentiment}\")\n", + "print(f\"Probabilities: {probs}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g0yneon8DtcV", + "outputId": "dfd80d51-6667-478d-a8ca-42adfb3e9ab7" + }, + "outputs": [], + "source": [ + "test_reviews = [\n", + " \"I loved this movie! The plot was amazing and the characters were so well developed.\",\n", + " \"This was the worst movie I have ever seen. It was a complete waste of time.\",\n", + " \"An average film with decent acting but nothing special.\",\n", + " \"Absolutely fantastic! A must-watch for everyone.\",\n", + " \"The story was boring and the acting was mediocre at best.\"\n", + "]\n", + "\n", + "for review in test_reviews:\n", + " sentiment, probs = predict_sentiment(review)\n", + " print(\"----\"*20)\n", + " print(f\"Review: {review}\")\n", + " print(f\"Predicted Sentiment: {sentiment}\")\n", + " print(f\"Probabilities: {probs}\\n\")\n", + " print(\"----\"*20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0vTikcxxEBAt" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "PnK_4azs5LBd" + ], + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "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", + 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