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README.md

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+# DistilBERT-Based Intent Detection Model for Banking Customer Queries
+
+This repository contains a fine-tuned **DistilBERT** model for **intent detection** in banking customer support scenarios. It is trained on the **BANKING77 dataset** and designed to accurately classify user queries into 77 distinct banking-related intents.
+
+## Model Details
+
+- **Model Architecture:** DistilBERT Base Uncased  
+- **Task:** Intent Detection for Banking Queries  
+- **Dataset:** [BANKING77](https://huggingface.co/datasets/banking77)  
+- **Fine-tuning Framework:** Hugging Face Transformers  
+- **Language:** English  
+- **Number of Labels:** 77  
+- **Quantization:** *Not applied (full precision)*  
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch datasets
+```
+
+### Loading the Model
+
+```python
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
+
+# Load fine-tuned model
+model_path = "./banking77-distilbert"  # Adjust path if different
+model = DistilBertForSequenceClassification.from_pretrained(model_path)
+tokenizer = DistilBertTokenizer.from_pretrained(model_path)
+model.eval()
+
+# Sample query
+text = "I need to reset my online banking password."
+
+# Tokenize and predict
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+with torch.no_grad():
+    outputs = model(**inputs)
+predicted_class = torch.argmax(outputs.logits, dim=1).item()
+
+# Example label map (replace with the full BANKING77 map)
+label_map = {0: "activate_my_card", 1: "balance", 2: "card_arrival", ..., 76: "why_was_my_card_declined"}
+print(f"Predicted Intent: {label_map[predicted_class]}")
+```
+
+## Performance Metrics
+
+- **Accuracy:** ~95% (on the BANKING77 test split)  
+- **Loss:** ~0.13 (after fine-tuning for 4 epochs)  
+
+## Fine-Tuning Details
+
+### Dataset
+
+- **Name:** BANKING77  
+- **Size:** ~13,000 customer support queries  
+- **Intents:** 77 unique labeled banking intents
+
+### Training
+
+- **Epochs:** 4  
+- **Batch Size:** 16  
+- **Learning Rate:** 2e-5  
+- **Optimizer:** AdamW  
+- **Evaluation Strategy:** per epoch  
+- **Loss Function:** CrossEntropyLoss  
+
+### Hardware
+
+- **GPU Used:** NVIDIA Tesla T4 (via Google Colab)  
+- **Training Time:** ~15 minutes  
+
+## Repository Structure
+
+```
+.
+├── banking77-distilbert/     # Fine-tuned model directory (saved via trainer.save_model)
+│   ├── config.json
+│   ├── pytorch_model.bin
+│   ├── tokenizer_config.json
+│   ├── vocab.txt
+├── intent_predictor.py       # Script for predicting intents (with preprocessing)
+├── README.md                 # Model documentation
+```
+
+## Limitations
+
+- The model is trained only on banking-related intents; it may misclassify out-of-domain queries.
+- Multilingual support is not included — limited to English.
+- Model does not handle multiple intents per query.
+
+## Contributing
+
+Contributions and suggestions are welcome. Please open an issue or pull request for improvements or additional features.

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
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+}

model.safetensors

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special_tokens_map.json

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tokenizer_config.json

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+}