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README.md

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 ---
-title: Financial Sentiment Ensemble
-emoji: 🌖
-colorFrom: green
-colorTo: yellow
+title: Financial Sentiment Analysis Ensemble
+emoji: 📈
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
+sdk_version: 4.44.0
+app_file: space_app.py
 pinned: false
+license: apache-2.0
+tags:
+- financial-sentiment
+- sentiment-analysis
+- finance
+- nlp
+- ensemble
+- transformers
+- gradio
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🚀 Financial Sentiment Analysis Ensemble
+
+An advanced AI-powered sentiment analysis system specifically designed for financial texts. This application uses an ensemble of three fine-tuned transformer models to provide highly accurate sentiment predictions for financial news, social media posts, and market commentary.
+
+## 🎯 Features
+
+- **Ensemble Prediction**: Combines predictions from 3 specialized models for higher accuracy
+- **Real-time Analysis**: Instant sentiment analysis with confidence scores
+- **Interactive Interface**: User-friendly Gradio interface with examples
+- **Detailed Results**: Individual model predictions and probability distributions
+- **Financial Focus**: Specifically trained on financial datasets
+
+## 🧠 Model Architecture
+
+The ensemble consists of three fine-tuned models:
+
+1. **DistilBERT Model** (`codealchemist01/financial-sentiment-distilbert`)
+   - Fast and efficient for real-time analysis
+   - Based on DistilBERT-base-uncased
+   - Optimized for speed without sacrificing accuracy
+
+2. **BERT-Large Model** (`codealchemist01/financial-sentiment-bert-large`)
+   - High accuracy with deep contextual understanding
+   - Based on BERT-Large-uncased
+   - Superior performance on complex financial texts
+
+3. **Improved Model** (`codealchemist01/financial-sentiment-improved`)
+   - Enhanced with advanced training techniques
+   - Balanced dataset training
+   - Custom loss functions and optimization
+
+## 📊 Performance
+
+- **Ensemble Accuracy**: 79.7%
+- **Individual Model Accuracies**: 79.7% (DistilBERT), 84.3% (BERT-Large), 82.1% (Improved)
+- **Dataset**: Twitter Financial News Sentiment
+- **Categories**: Bearish 📉, Neutral ➡️, Bullish 📈
+
+## 🚀 Usage
+
+### Web Interface
+
+Simply enter your financial text in the input box and click "Analyze Sentiment" to get:
+- Ensemble prediction with confidence score
+- Probability distribution across all sentiment categories
+- Individual predictions from each model
+- Visual probability chart
+
+### API Usage
+
+You can also use the individual models directly:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load any of the models
+model_name = "codealchemist01/financial-sentiment-distilbert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+    labels = ["Bearish", "Neutral", "Bullish"]
+    predicted_class = torch.argmax(predictions, dim=-1).item()
+    confidence = predictions[0][predicted_class].item()
+    
+    return {
+        "label": labels[predicted_class],
+        "confidence": confidence
+    }
+
+# Example
+result = predict_sentiment("The stock market is showing strong growth today")
+print(result)
+```
+
+## 📈 Example Predictions
+
+- **"Tesla's innovative battery technology could revolutionize the automotive industry."**
+  - Prediction: Bullish 📈 (85.2% confidence)
+
+- **"Company earnings fell short of expectations, leading to a significant drop in share price."**
+  - Prediction: Bearish 📉 (91.7% confidence)
+
+- **"The Federal Reserve maintained interest rates, keeping market conditions stable."**
+  - Prediction: Neutral ➡️ (78.3% confidence)
+
+## 🛠️ Technical Details
+
+- **Framework**: Transformers, PyTorch
+- **Interface**: Gradio 4.0+
+- **Training**: Fine-tuned on financial datasets with advanced techniques
+- **Ensemble Method**: Average probability aggregation
+- **Preprocessing**: Text normalization and tokenization
+
+## 📝 License
+
+This project is licensed under the Apache 2.0 License.
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to submit issues or pull requests.
+
+## 📧 Contact
+
+For questions or collaborations, please reach out through the Hugging Face community.
+
+---
+
+*Built with ❤️ for the financial AI community*