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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_pr

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