suha-memon
/

finbert-stock-sentiment

@@ -1,199 +1,141 @@
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 ---
+language: en
+license: mit
+tags:
+- finance
+- sentiment-analysis
+- finbert
+- stock-market
+datasets:
+- financial_phrasebank
+metrics:
+- accuracy
+- f1
+model-index:
+- name: suha-memon/finbert-stock-sentiment
+  results:
+  - task:
+      type: text-classification
+      name: Financial Sentiment Analysis
+    metrics:
+    - type: accuracy
+      value: 0.8188
+      name: Accuracy
+    - type: f1
+      value: 0.8009
+      name: F1 Macro
 ---
+# FinBERT Stock Sentiment Analyzer
+Fine-tuned FinBERT model for financial sentiment analysis of stock-related news headlines.
+## Model Description
+This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on financial sentiment data. It classifies financial text into three categories:
+- **Negative** (0)
+- **Neutral** (1)
+- **Positive** (2)
 ## Training Details
+### Training Approach
+- **Base Model**: ProsusAI/finbert
+- **Fine-tuning Method**: Focal Loss with class weighting
+- **Training Epochs**: 10 (with early stopping)
+- **Learning Rate**: 3e-5
+- **Batch Size**: 16 (effective 32 with gradient accumulation)
+- **Warmup Ratio**: 0.1
+### Performance Metrics
+| Metric | Score |
+|--------|-------|
+| **Accuracy** | 81.88% |
+| **F1 Macro** | 0.8009 |
+| **F1 Weighted** | 0.8259 |
+### Per-Class Performance
+| Class | F1 Score |
+|-------|----------|
+| Negative | 0.6719 |
+| Neutral | 0.8203 |
+| Positive | 0.9104 |
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+# Load model and tokenizer
+model_name = "suha-memon/finbert-stock-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# Example usage
+headline = "Apple announces record-breaking quarterly earnings"
+inputs = tokenizer(headline, return_tensors="pt", truncation=True, max_length=128)
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+# Get probabilities
+negative_prob = predictions[0][0].item()
+neutral_prob = predictions[0][1].item()
+positive_prob = predictions[0][2].item()
+print(f"Negative: {negative_prob:.2%}")
+print(f"Neutral: {neutral_prob:.2%}")
+print(f"Positive: {positive_prob:.2%}")
+```
+## Intended Use
+This model is designed for:
+- Analyzing sentiment of financial news headlines
+- Stock market sentiment analysis
+- Financial document classification
+- Real-time news sentiment tracking
+## Limitations
+- The model performs best on financial news headlines (similar to training data)
+- Negative sentiment detection (F1: 67%) is weaker due to class imbalance in training data
+- May not generalize well to non-financial domains
+- Limited to English language text
+## Training Data
+The model was trained on financial sentiment data from Kaggle, consisting of:
+- Training set: ~4,800 labeled examples
+- Validation set: ~580 examples
+- Test set: ~580 examples
+Class distribution was imbalanced with fewer negative examples, addressed using Focal Loss.
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{finbert-stock-sentiment,
+  author = {Your Name},
+  title = {FinBERT Stock Sentiment Analyzer},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/suha-memon/finbert-stock-sentiment}}
+}
+```
+## Team
+- Suha Memon
+- Nick Cirillo
+- Kalen Truong
+- Bruce Zhang
+## Acknowledgments
+- Base model: [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert)
+- Framework: Hugging Face Transformers