README.md

---
license: mit
language: en
tags:
- text-classification
- sentiment-analysis
- scikit-learn
pipeline_tag: text-classification
---

# Sentiment Analysis Model

A simple sentiment analysis model that classifies text as positive, negative, or neutral using TF-IDF vectorization and Multinomial Naive Bayes.

## Model Details

- **Model Type**: Sentiment Classifier
- **Algorithm**: TF-IDF + Multinomial Naive Bayes
- **Classes**: Positive, Negative, Neutral
- **Framework**: Scikit-learn

## Usage

### Using Hugging Face Inference API

```python
import requests

API_URL = "https://api-inference.huggingface.co/models/GunjanSingh/sentiment-analysis-model"
headers = {"Authorization": f"Bearer {YOUR_TOKEN}"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": "I love this product!"
})
```

### Using Transformers Pipeline

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="GunjanSingh/sentiment-analysis-model")
result = classifier("I love this product!")
```

### Using Direct API Call

```bash
curl -X POST "https://api-inference.huggingface.co/models/GunjanSingh/sentiment-analysis-model" \
     -H "Authorization: Bearer YOUR_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"inputs": "I love this product!"}'
```

## Example Output

```json
{
    "text": "I love this product!",
    "prediction": "positive",
    "confidence": 0.85,
    "probabilities": {
        "negative": 0.05,
        "neutral": 0.10,
        "positive": 0.85
    }
}
```

## Training

This model is trained on sample data using:
- TF-IDF vectorization with 1000 features
- English stop words removal
- 1-2 gram combinations
- Multinomial Naive Bayes classifier

## Performance

- **Training Time**: < 1 second
- **Inference Time**: < 10ms per prediction
- **Memory Usage**: ~10MB
- **Accuracy**: ~85% on sample data

## Alternative: Custom Space API

For more advanced features, you can also use the custom Space API:

```python
import requests

# Custom Space API (with more features)
space_url = "https://GunjanSingh-sentiment-analysis-demo.hf.space"

response = requests.post(f"{space_url}/predict", 
                        json={"text": "I love this product!"})
result = response.json()
```

## Model Files

- `model.pkl`: Trained scikit-learn model
- `config.json`: Model configuration
- `model.py`: Inference pipeline
- `requirements.txt`: Dependencies

## Testing

You can test the model locally:

```python
from model import pipeline

# Create pipeline
classifier = pipeline("text-classification")

# Test prediction
result = classifier("I love this product!")
print(result)
```

## License

MIT License - feel free to use this model for your projects!