# BERT-Base-Uncased Fine-Tuned Model for Intent Classification on CLINC150 Dataset

This repository hosts a fine-tuned BERT model for multi-class intent classification using the CLINC150 (plus) dataset. The model is trained to classify user queries into 150 in-scope intents and handle out-of-scope (OOS) queries.

## Model Details

- **Model Architecture:** BERT Base Uncased  
- **Task:** Multi-class Intent Classification  
- **Dataset:** CLINC150 (plus variant)  
- **Quantization:** Float16  
- **Fine-tuning Framework:** Hugging Face Transformers  

---

## Installation

```bash
pip install transformers datasets scikit-learn evaluate

```

---

## Loading the Model

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load tokenizer and model
model_path = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
# Define test sentences
test_sentences = [
    "Can you tell me the weather in New York?",
    "I want to transfer money to my friend",
    "Play some relaxing jazz music",
]


# Tokenize and predict
def predict_intent(sentences, model, tokenizer, id2label_fn, device="cpu"):
    if isinstance(sentences, str):
        sentences = [sentences]

    model.eval()
    model.to(device)

    inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predictions = torch.argmax(logits, dim=-1)

    return [id2label_fn(label.item()) for label in predictions]

```

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## Performance Metrics

- **Accuracy:** 0.947097  
- **Precision:** 0.949821  
- **Recall:** 0.947097  
- **F1 Score:** 0.945876
  

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## Fine-Tuning Details

### Dataset

The CLINC150 (plus) dataset contains 151 intent classes (150 in-scope + 1 out-of-scope) for intent classification in English utterances. It includes 15k training, 3k validation, and 4.5k test examples with diverse user queries.


### Training

- **Epochs:** 5 
- **Batch size:** 16  
- **Learning rate:** 2e-5  
- **Evaluation strategy:** `epoch`  

---

## Quantization

Post-training quantization was applied using PyTorch’s `half()` precision (FP16) to reduce model size and inference time.

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## Repository Structure

```python
.
├── quantized-model/               # Contains the quantized model files
│   ├── config.json
│   ├── model.safetensors
│   ├── tokenizer_config.json
│   ├── vocab.txt
│   └── special_tokens_map.json
├── README.md                      # Model documentation
```

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## Limitations

- The model is trained specifically for multi classification on CLINIC150 Dataset.
- FP16 quantization may result in slight numerical instability in edge cases.


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## Contributing

Feel free to open issues or submit pull requests to improve the model or documentation.