README.md

metadata

title: Financial Sentiment Analysis Ensemble
emoji: 📈
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - financial-sentiment
  - sentiment-analysis
  - finance
  - nlp
  - ensemble
  - transformers
  - gradio

🚀 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:

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.

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