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 - A widely used financial sentiment dataset.
✅ GitHub Generated Sentiment Dataset - An additional dataset to test the model.
⚙️ 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 of financial sentences in ex. 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:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "Driisa/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]}")
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