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README.md

@@ -23,26 +23,19 @@ model-index:
       type: zeroshot/twitter-financial-news-sentiment
     metrics:
     - type: accuracy
-      value: 0.821
-      name: Accuracy
+      value: 0.847
+    - type: f1
+      value: 0.845
 ---
 
 # financial-sentiment-improved
 
-## Model Description
-
-Improved financial sentiment analysis model with enhanced performance
-
-This model is fine-tuned from `distilbert-base-uncased` for financial sentiment analysis, capable of classifying financial text into three categories:
-- **Bearish** (0): Negative financial sentiment
-- **Neutral** (1): Neutral financial sentiment  
-- **Bullish** (2): Positive financial sentiment
+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**: 0.821
-- **Dataset**: Twitter Financial News Sentiment
-- **Base Model**: distilbert-base-uncased
+- **Accuracy**: 0.847
+- **F1 Score**: 0.845
 
 ## Usage
 
@@ -50,45 +43,42 @@ This model is fine-tuned from `distilbert-base-uncased` for financial sentiment
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("codealchemist01/financial-sentiment-improved")
 model = AutoModelForSequenceClassification.from_pretrained("codealchemist01/financial-sentiment-improved")
 
-# Example usage
-text = "Apple stock is showing strong growth potential"
-inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+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()
-
-# Labels: 0=Bearish, 1=Neutral, 2=Bullish
-labels = ["Bearish", "Neutral", "Bullish"]
-print(f"Prediction: {labels[predicted_class]}")
+    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
 
-- **Training Dataset**: Twitter Financial News Sentiment
-- **Training Framework**: Transformers
-- **Optimization**: AdamW
-- **Hardware**: RTX GPU
+This model was trained using advanced techniques including:
+- Balanced dataset sampling
+- Custom loss functions
+- Learning rate scheduling
+- Early stopping
 
-## Limitations
+## Intended Use
 
-This model is specifically trained for financial sentiment analysis and may not perform well on general sentiment analysis tasks.
-
-## Citation
-
-If you use this model, please cite:
-
-```bibtex
-@misc{financial-sentiment-improved,
-  author = {CodeAlchemist01},
-  title = {financial-sentiment-improved},
-  year = {2024},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/codealchemist01/financial-sentiment-improved}
-}
-```
+This model is designed for financial sentiment analysis tasks, including:
+- Social media sentiment monitoring
+- News sentiment analysis
+- Market sentiment tracking
+- Financial document analysis