{ "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", "f72d221da4c94bb5acbab5f668c1def9", "2d3c3c7fd5a54e10adad8fc740768c76", "6592984fcb4e4897b264b9750447d89b", "5f8aafce1d524cf6835e8702486705df", "d51a405a37f141a5936b2ecae706f0bb", "7109b208774e4790bc768d44d81dc952", "200a8d340e9147d3a5ec56d7f2279b85", "4e7bfb95159c4594a427bac9f81f813c", "4c26af8820e740728402793fceb96892", "43818e39f86f45aca3dfbcbbf8d2acce", "eb59f4500e5a4ed482d48291d61d6c7b", "ee27bd65565c4ace900909819da98ac5", "467ddd4a13194a658fd89879261e70d9", "668e77f7fa53426eb5cf697258d7d4e9", "c638b22b8a5045e68ebc555ed0485953", "b9491d89851548468a677f3ef066b723", "5c5134c7e921401e82ff7dfe9465eb59", "fb73a0334b3442e4b432c2e4c3ecdc4d", "d065f1a401a34076a84356fdd1c49b62", "3d90645237084c3abc3ee88a84191d80", "013327017e9647feac011d0b6c5918b5", "3cb8b1a5f8494ab3b9d94f3a690dd8f2", "2f20e49834ea482d917408ca723e392b", "773e9a6bc8274550a2875d9d4a1ee923", "a8221960fbdf49f09b158d1cbfa92fee", "7bfc02fd119a4a13ba9111bbc7febaf9", "59b89b854b9141f9b660830d748dd8f6", "dd5fb82a8fb145ebb9b1c3b868fd8233", "2df8e00e0bd741db81ce6e00ac485315", "476de363af084df78bebcc1a243e99f8", "db2bbca4d6f44d07952f0060d209c0b0", "ce1c6cecc02a4fb5ad5a39b7a86fb7e2", "b48b1a14e1dd4b50aaf82b6f1a6d558c", "637e13d37e0f43bfbdbed2b14a64beeb", "b98e3227e2c64b56a397c768c3349676", "f0eb7e129da74570aa3dc6c8717d8d77", "a7a749d8a8944667ab591c57b5bd1e9e", "eb1da161ba4b4acb9923979d985030e9", "cf18046c11b14f009809cf9446bd37eb", "90ba0ad4dfa245d582d3f3c0b3a3865d", "16d7d3ca596b4eb680087aa191c3d5f5", "df0f198900f846f68b6a49d7ce53454b", "5035460a011e4adfa119f7daaff70f9b", "679af32bfed14b2aa9f75de0e125a70f", "b1d6ab19ca5e426482eb07109dac3b7a", "7dc67ce11a104876a4953c35777fc970", "8b3f0740a9ff4a36a7a0f522a9e99c22", "aecec7eed1d74577a8af81b3cbcbacf5", "65b7e6c26327463a840a2015bee75b0c", "e0e769dd0d5947ecb595d33a2ffb42b7", "267ffb08a01b4ac3a33c2be0aa78f310", "632ac505aebe4fd38f21844d51769ff4", "a7b598eaa2af406c81fdd422d9f979e4", "f831a270a3194566b3e87e02fbb97805", "66e5e5db1f0b42dbb164b45362746710", "17368bab90d6483aa8edef180e9bda5c", "d38a516454304dc9a72697aea4384ce1", "6fc322c1c51f4e49baaaed054fb3441c", "56a08cdf540943ec9ba9396aded1ca60", "b9e2a7a35af84766aad07e80217d9fea", "7e57acb8ccbd4a8ea844ee660456dc10", "274295226886447889e7412e8cf6a0e2", "0b6c4d686ef34114be0c67db86fdbbb0", "6fc0f51c09a447e181fe931e669049f5", "cd686a803cbe4668b28a1926b39d02f2", "e8227f90587e4f4fb040ff203076e1ae", "bded9485ff274fd58acf23097ec35ff4", "896faf4f97634ed3854180435696ec83", "172de852d4314ef7b8e28503c14adb7a", "d09b986d19eb47818526d955921d6397", "4bd9c9e2b08647c5ab8dd20d24d4a62e", "fde107bc4d4145bfbc5043fd863aba79", "aa58c2a1ed344de9b03eb80566c7946d", "b76a52ce4a8244518bb679ab0157b0c8", "b479b9eb80074106b3cf8c133b011aa6", "d2bff504b24e4927a5f53dc2850466f8", "2b970e1001b7426bada2d86a3f7dbcbe", "b2167f35001748668cec3dae54c2f9f5", "c352fc0e3bf14ba0b8ede285c8fab6da", "b3eca17a49cf4d2f92591bd199fd31b0", "0e54fd3e0c5341efb6ee0ca18df9b5dd", "aaa1b0f2bde641f6ac8595f2c52b41fb", "0b7e652b133c474bb307fcb0aa979a8b", "58a4ce23825d436c96c8b07e5fe91909", "89a1291e541540278b287aa07836977d", "e08b2053f5834adfa0b61b7e0dce498c", "69a401f64a324b0f966430cd69704aed", "23bedd124d5744478fb0540b21a26cdd", "0a1368cd16db44c78cc2e30f4d7d3e21", "eb42ba79a1da42dbaa146123e2c1e939", "a6b03363441b455cb63bc9e25c3fb1d3", "1903ba6baf9543c59021b29392e0cf5d", "0d5638fcc6714dda828d6a39201a8418", "42ab6de8a8564e09aa34f422a27a9237", "5b161f26700f486bb3e337b26f2da554", "e925956d62be47b1848e9d0643a3fe0a", "e1bdcfaa57ec4755a70f38ee9bdab419", "c3aeb030519c421a9569c70d82bdd5cb", "c538fdd170ab490aaec0493a92a844ae", "b0bff5997437460eb7550d328705da5d", "9c40d185febe4c4bb4818878a84b2ac7", "a89778ae3264444d80f18f33dd57508f", "22ecce50f97d4c5cb8ffaa788c15174c", "e952424e909749f489813aee23f4cea0", "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", "version": "3.12.2" }, "widgets": { "application/vnd.jupyter.widget-state+json": { 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