Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,128 @@
 ---
 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!