diff --git "a/notebook.ipynb" "b/notebook.ipynb"
--- "a/notebook.ipynb"
+++ "b/notebook.ipynb"
@@ -1,6607 +1,1473 @@
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- "\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",
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- "source": [
- "!pip install transformers datasets peft evaluate accelerate -q"
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- "Verifying Installations"
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- "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__}\")"
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- "id": "wOie5cSZxVIZ",
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- "Tramsformers: 5.0.0\n",
- "Datasets: 4.0.0\n",
- "PEFT: 0.18.1\n",
- "Evaluate: 0.4.6\n"
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- "execution_count": 18,
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- "model_id": "5655c5cca3d547b6a0ac08d1b1fbf008"
- }
- },
- "metadata": {}
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "data/train-00000-of-00001.parquet: 0%| | 0.00/298k [00:00"
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- "text/html": [
- "\n",
- " \n",
- " \n",
- "
\n",
- " [1410/1410 01:45, Epoch 5/5]\n",
- "
\n",
- " \n",
- " \n",
- " | Epoch | \n",
- " Training Loss | \n",
- " Validation Loss | \n",
- " Accuracy | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 1 | \n",
- " 3.995700 | \n",
- " 3.609172 | \n",
- " 0.293706 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " 2.572789 | \n",
- " 2.307555 | \n",
- " 0.604396 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " 2.008083 | \n",
- " 1.736333 | \n",
- " 0.693307 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " 1.693004 | \n",
- " 1.496839 | \n",
- " 0.712288 | \n",
- "
\n",
- " \n",
- " | 5 | \n",
- " 1.582025 | \n",
- " 1.425961 | \n",
- " 0.735265 | \n",
- "
\n",
- " \n",
- "
"
- ]
- },
- "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",
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- ]
- },
- "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"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [1410/1410 01:45, Epoch 5/5]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Accuracy | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 3.995700 | \n",
+ " 3.609172 | \n",
+ " 0.293706 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2.572789 | \n",
+ " 2.307555 | \n",
+ " 0.604396 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 2.008083 | \n",
+ " 1.736333 | \n",
+ " 0.693307 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1.693004 | \n",
+ " 1.496839 | \n",
+ " 0.712288 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 1.582025 | \n",
+ " 1.425961 | \n",
+ " 0.735265 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ]
+ },
+ "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",
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+ "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