---
language: "en"
tags:
  - deep-learning
  - transformers
  - huggingface
license: "mit"
datasets:
  - my_dataset
model-name: "my_model"
---

# **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:

```python
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]}")