Create README.md

README.md

@@ -0,0 +1,63 @@
+model_card_content = """ 
+# **FinBERT Fine-Tuned on Financial Sentiment (Financial PhraseBank + GitHub Dataset)**  
+
+## **📌 Model Description**
+This model is a fine-tuned version of **FinBERT** (`ProsusAI/finbert`) trained for **financial sentiment classification**.  
+It can classify financial text into **three categories**:  
+- **Negative (0)**  
+- **Neutral (1)**  
+- **Positive (2)**  
+
+## **📂 Dataset Used**
+This model was trained on:  
+✅ **Financial PhraseBank (All Agree)** - A widely used financial sentiment dataset.  
+✅ **GitHub Generated Sentiment Dataset** - An additional dataset to improve generalization.  
+
+## **⚙️ Training Parameters**
+| Parameter           | Value  |
+|---------------------|--------|
+| Model Architecture | FinBERT (based on BERT) |
+| Batch Size         | 8      |
+| Learning Rate      | 2e-5   |
+| Epochs            | 3      |
+| Optimizer         | AdamW  |
+| Evaluation Metric  | F1-Score, Accuracy |
+
+## **📊 Model Performance**
+| Dataset          | Accuracy | F1 (Weighted) | Precision | Recall |
+|-----------------|----------|--------------|------------|---------|
+| Financial PhraseBank (Train) | 95.21% | 95.23% | 95.32% | 95.21% |
+| GitHub Test Set | 64.42% | 64.34% | 70.52% | 64.42% |
+
+## **🚀 Intended Use**
+This model is designed for:  
+✅ **Financial Analysts & Investors** to assess sentiment in earnings reports, news, and stock discussions.  
+✅ **Financial Institutions** for NLP-based sentiment analysis in automated trading.  
+✅ **AI Researchers** exploring financial NLP models.
+
+## **⚠️ Limitations**
+⚠️ **May not generalize well to datasets with very different financial language.**  
+⚠️ **Might require fine-tuning for specific financial domains (crypto, banking, startups).**  
+
+## **📥 Usage Example**
+You can use the model via Hugging Face Transformers:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "your-username/finbert-finetuned-github"
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example input
+text = "The company's stock has seen significant growth this quarter."
+
+# Tokenize and predict
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+outputs = model(**inputs)
+
+# Get predicted class
+predicted_class = outputs.logits.argmax().item()
+print(f"Predicted Sentiment: {['Negative', 'Neutral', 'Positive'][predicted_class]}")