README.md

---
language: en
license: apache-2.0
tags:
- text-classification
- branch-switching
- hospital-chatbot
- distilbert
datasets:
- branch_switch_classification
widget:
- text: "I want to switch to Mumbai branch"
- text: "What are your hospital timings?"
- text: "Can I change to the branch near my home?"
---

# Branch Switch Classification Model

This model classifies whether a user wants to switch hospital branches or is asking for general information.

## Model Description

- **Model**: DistilBERT for Sequence Classification
- **Task**: Binary Classification
- **Domain**: Hospital/Healthcare Chatbot
- **Classes**: 
  - `True`: User wants to switch branches
  - `False`: General query/information seeking

## Usage

```python
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = DistilBertTokenizer.from_pretrained("hitty28/branch-switch-classifier")
model = DistilBertForSequenceClassification.from_pretrained("hitty28/branch-switch-classifier")

# Predict
def predict(text):
    inputs = tokenizer(text, truncation=True, padding='max_length', max_length=128, return_tensors='pt')
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        predicted_class = torch.argmax(predictions, dim=-1).item()
    return bool(predicted_class)

# Example
result = predict("I want to switch to Delhi branch")
print(result)  # True
```

## Training Data

The model was trained on a comprehensive dataset including:
- Direct branch switch requests
- Location-specific switches  
- Facility-based switches
- Information queries about branches
- Medical service inquiries
- Edge cases and ambiguous statements

## Performance

The model achieves high accuracy in distinguishing between branch switching intents and general information queries.