Driisa/finbert-finetuned-github

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

📊 Model Performance

🚀 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]}")