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"description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "First of all we need to install requied **libraries**" + ], + "metadata": { + "id": "k3d4WSh2zKRR" + } + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tIdoqVV1izoz", + "outputId": "392af71f-8313-4be0-c1da-6334550bc69d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "!pip install transformers datasets peft evaluate accelerate -q" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Verifying Installations" + ], + "metadata": { + "id": "Ym_FNQY4zU3y" + } + }, + { + "cell_type": "code", + "source": [ + "import transformers\n", + "import datasets\n", + "import peft\n", + "import evaluate\n", + "print(f\"Tramsformers: {transformers.__version__}\")\n", + "print(f\"Datasets: {datasets.__version__}\")\n", + "print(f\"PEFT: {peft.__version__}\")\n", + "print(f\"Evaluate: {evaluate.__version__}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wOie5cSZxVIZ", + "outputId": "e208377e-dcac-4a59-cc15-6f99bc018284" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tramsformers: 5.0.0\n", + "Datasets: 4.0.0\n", + "PEFT: 0.18.1\n", + "Evaluate: 0.4.6\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Load the datasets**" + ], + "metadata": { + "id": "77A5H5jUzinc" + } + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset" + ], + "metadata": { + "id": "101qqBCBz-JB" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "loading banking77 dataset" + ], + "metadata": { + "id": "bEcOwfZV0IXa" + } + }, + { + "cell_type": "code", + "source": [ + "dataset =load_dataset(\"legacy-datasets/banking77\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "5655c5cca3d547b6a0ac08d1b1fbf008", + "f9ec87bec967441f92f74b6518819709", + "84a24ca325464e1db0cdb56f096c10fc", + "e326b50a6a6f41f79d9af1e544645348", + "cea5de85e6994e4ba40219434102842d", + "c2a9c610fe1b4e93a7aacafeb22069ba", + "aaa2991f6f854d188eb62177c0ccf457", + "d069596b4ad54bb9a785403e0491fbef", + "075bc577ced6404bb1e6a63b47c6139e", + "5549923675df4864a223429518765dd2", + "d0949c0967184dc097899dc1dffc0bd5", + "e062cedda9ff4128b90dc0b76ce7d71c", + "9f20dfd7ca6743338810ee564f1bbf95", + "3589858387154504a9f7663935156d5c", + "6af6400d7394437ea2aaa7816aab963f", + "15b8413c1cb1464d9c5fbe0899d855e6", + "7d04113fcd084ff09ea5fa7c5718680c", + "ac29f3c9178041ed967f11aaa98195e8", + "71b87b8a3c27421b9b3b0fa7f86b44c5", + "8572d5854f2e4cc49ccf7ad17ee7ddd5", + "5d2a532c7f52411a9d1e19fe58b44970", + "fc1caa125cb04ef1a26c6018d2702e0d", + "9ced219b80c74d5392c00c786dc3663a", + "25f95444cec04b4fba706853567f0a31", + "0cb3c9f5192548bcb970b06d71e1fe4b", + "ebb6abfa10e4494480fcef655d7bc280", + "ca9fd314395c4949b090ade258664516", + "040f6956c7cb416dbca66863045d40e7", + "3108dd45cc04481989000aec773bdc8b", + "7803a811de55428abb91932f0a3c325e", + "f34a912cde854e35a345ce5a807f27ea", + "c339895ae7554611ae9679a6e80eaccb", + "db0946daeef94404a0ba4ba58132ccbb", + "5daf5aacab4c4028a127c4128e4a9c97", + "7abc88f1c19b47449b924e183c42e21a", + "d25694f9d9bc4bffb3f8a53ec5a58749", + "ad5bd6f328ec41818e62602dbc53dc77", + "f796fe7a076d409284f65fda06a96633", + "ba6dfbc2bdf249c8b08b45c30495e684", + "d96a1cae1c2846ca8243e4b9cf80f9a2", + "32853e628e794361bee346a975a35534", + "2ec24fdc2a5d47e7bd9224349535fd74", + "bd57051fd3f544d7911b3c43a4b7a63b", + "aa2e7b6a1a794c239e064988383918f6", + "ce41312305a54e0e97b9d113a8245c34", + "82293b8d416847dd96f71c0ed5e805fd", + "57b3d091a24047a8ab97d5d6535a79ef", + "3ba69b950f0848c89aa090f1a69fba57", + "93db226d05cd4e9582cc685ec47dfeb2", + "261c90036bd746ffb020e5f55e27a104", + "0f3994568d514d30ba2e727c5a52c9bd", + "2628185d0ede4f039eb7e38368761d73", + "02771cfe0334486293ed5570c86f9f46", + "fc512a98af3d426ba5be0278c3184398", + "f4faf71ce3e6438b8a788cff559839b5" + ] + }, + "id": "HbYjKkL40NDQ", + "outputId": "eaf2f031-5f62-4187-d930-5c3f3ce112fa" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "5655c5cca3d547b6a0ac08d1b1fbf008" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + 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" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model saved to Google Drive\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Model Evaluation" + ], + "metadata": { + "id": "1xAONxM4H-U7" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Get predictions on test set\n", + "predictions = trainer.predict(tokenized_dataset[\"test\"])\n", + "pred_labels = np.argmax(predictions.predictions, axis=-1)\n", + "true_labels = predictions.label_ids\n", + "\n", + "# Overall accuracy\n", + "accuracy = (pred_labels == true_labels).mean()\n", + "print(f\"Test Accuracy: {accuracy:.4f} ({accuracy*100:.2f}%)\")\n", + "\n", + "# Detailed per class report\n", + "class_names = dataset[\"test\"].features[\"label\"].names\n", + "report = classification_report(\n", + " true_labels,\n", + " pred_labels,\n", + " target_names=class_names,\n", + " output_dict=True\n", + ")\n", + "\n", + "# Find best and worst performing intents\n", + "report_df = pd.DataFrame(report).transpose()\n", + "report_df = report_df.drop(\n", + " [\"accuracy\", \"macro avg\", \"weighted avg\"]\n", + ")\n", + "\n", + "print(\"\\nTop 5 best performing intents:\")\n", + "print(report_df.nlargest(5, \"f1-score\")[[\"f1-score\", \"support\"]])\n", + "\n", + "print(\"\\nTop 5 worst performing intents:\")\n", + "print(report_df.nsmallest(5, \"f1-score\")[[\"f1-score\", \"support\"]])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 312 + }, + "id": "NCqJLNLpIBdd", + "outputId": "b9dd7cb3-7ee5-4c4c-d95f-2b54e2056cbe" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Accuracy: 0.7175 (71.75%)\n", + "\n", + "Top 5 best performing intents:\n", + " f1-score support\n", + "verify_top_up 1.000000 40.0\n", + "age_limit 0.975610 40.0\n", + "change_pin 0.950000 40.0\n", + "passcode_forgotten 0.930233 40.0\n", + "get_physical_card 0.928571 40.0\n", + "\n", + "Top 5 worst performing intents:\n", + " f1-score support\n", + "transfer_not_received_by_recipient 0.310345 40.0\n", + "cash_withdrawal_not_recognised 0.338983 40.0\n", + "supported_cards_and_currencies 0.339623 40.0\n", + "topping_up_by_card 0.339623 40.0\n", + "top_up_by_bank_transfer_charge 0.392857 40.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.save_pretrained(\"banking77-lora-final\")\n", + "tokenizer.save_pretrained(\"banking77-lora-final\")\n", + "print(\"Model saved locally\")" + ], + "metadata": { + "id": "HXa6IDJnCjj3", + "outputId": "0a070d10-f66f-4622-cbbd-aa03fd7a9fe2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model saved locally\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Inference Code" + ], + "metadata": { + "id": "2TwhO54Px12T" + } + }, + { + "cell_type": "code", + "source": [ + "from peft import PeftModel, PeftConfig\n", + "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n", + "import torch\n", + "\n", + "# Load your fine-tuned model\n", + "def load_model(model_path=\"./banking77-lora-final\"):\n", + " tokenizer = AutoTokenizer.from_pretrained(model_path)\n", + " config = PeftConfig.from_pretrained(model_path)\n", + " model = AutoModelForSequenceClassification.from_pretrained(\n", + " config.base_model_name_or_path,\n", + " num_labels=77\n", + " )\n", + " model = PeftModel.from_pretrained(model, model_path)\n", + " model.eval()\n", + " return model, tokenizer\n", + "\n", + "# Predict intent\n", + "def predict_intent(text, model, tokenizer):\n", + " class_names = dataset[\"test\"].features[\"label\"].names\n", + "\n", + " inputs = tokenizer(\n", + " text,\n", + " return_tensors=\"pt\",\n", + " padding=True,\n", + " truncation=True,\n", + " max_length=128\n", + " )\n", + "\n", + " with torch.no_grad():\n", + " outputs = model(**inputs)\n", + " probabilities = torch.softmax(outputs.logits, dim=-1)\n", + " confidence, predicted_class = torch.max(probabilities, dim=-1)\n", + "\n", + " return {\n", + " \"intent\": class_names[predicted_class.item()],\n", + " \"confidence\": f\"{confidence.item()*100:.2f}%\",\n", + " \"text\": text\n", + " }\n", + "\n", + "# Load model\n", + "model, tokenizer = load_model()\n", + "\n", + "# Test with real examples\n", + "test_queries = [\n", + " \"I lost my card and need a replacement\",\n", + " \"Why was my payment declined?\",\n", + " \"How do I add money to my account?\",\n", + " \"I want to change my PIN number\",\n", + " \"What currencies do you support?\"\n", + "]\n", + "\n", + "print(\"Intent Classification Results:\")\n", + "print(\"=\"*50)\n", + "for query in test_queries:\n", + " result = predict_intent(query, model, tokenizer)\n", + " print(f\"\\nText: {result['text']}\")\n", + " print(f\"Intent: {result['intent']}\")\n", + " print(f\"Confidence: {result['confidence']}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 708, + "referenced_widgets": [ + "6d1d9c5c8eef4194987337d6b9460eab", + "c60e097ae7b74ce0b0643759a3d3b7e9", + "76d184cb05634b18ae417b8e55e11969", + "6055c976102140c486f4e7c5d7769d17", + "43ff9fa4a0ab42aebc985ee1db03bd67", + "1c4ed9c9153b4fffb0085f4bb7951c1a", + "cc2a475498f040f4904916a3616fb63e", + "9b9fed4da36f45c0b58bc93a025e4d4f", + "bce6f1ae436f49ff93c9c1c806ac6481", + "ddec6967ce674c7c89d68eaa05ede1c8", + "ccf88702043a455abac5352aca0141a0" + ] + }, + "id": "MSa_7Zpcx4td", + "outputId": "db9a8cee-ab7d-4654-cfbf-e0cad855a3b2" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/100 [00:00