financial-sentiment-improved

This model is a fine-tuned version of DistilBERT for financial sentiment analysis. It has been trained on financial news and social media data to classify text into three sentiment categories: Bearish (negative), Neutral, and Bullish (positive).

Model Performance

• Accuracy: 0.847
• F1 Score: 0.845

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("codealchemist01/financial-sentiment-improved")
model = AutoModelForSequenceClassification.from_pretrained("codealchemist01/financial-sentiment-improved")

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)

Training Details

This model was trained using advanced techniques including:

• Balanced dataset sampling
• Custom loss functions
• Learning rate scheduling
• Early stopping

Intended Use

This model is designed for financial sentiment analysis tasks, including:

• Social media sentiment monitoring
• News sentiment analysis
• Market sentiment tracking
• Financial document analysis