---
language: en
license: apache-2.0
tags:
- finance
- sentiment-analysis
- modernbert
- financial-nlp
datasets:
- neoyipeng/financial_reasoning_aggregated
metrics:
- accuracy
widget:
- text: "The company reported strong quarterly earnings with revenue beating expectations."
  example_title: "Positive Example"
- text: "Stock prices fell sharply following disappointing guidance from management."
  example_title: "Negative Example"
- text: "The merger is expected to close in Q3 pending regulatory approval."
  example_title: "Neutral Example"
---

# ModernFinBERT: Financial Sentiment Analysis

ModernFinBERT is a financial sentiment analysis model based on the ModernBERT architecture, fine-tuned specifically for financial text classification.

## Model Details

- **Base Model**: answerdotai/ModernBERT-base
- **Task**: 3-class sentiment classification (Negative, Neutral, Positive)
- **Training Data**: Vanilla sentiment tasks from multiple sources (including FinancialPhraseBank)
- **Parameters**: 149,607,171

## Performance

### Overall Accuracy

| Split | Accuracy |
|-------|----------|
| Validation | 85.3% |
| Test | 83.1% |

### Test Accuracy by Source

| Source | Accuracy | Correct/Total |
|--------|----------|---------------|
| 4.0 | 89.5% | 77/86 |
| 9.0 | 88.0% | 205/233 |
| 5.0 | 84.4% | 205/243 |
| 3.0 | 80.0% | 20/25 |
| 8.0 | 69.1% | 94/136 |

## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("neoyipeng/ModernFinBERT-base")
model = AutoModelForSequenceClassification.from_pretrained("neoyipeng/ModernFinBERT-base")

text = "The company's quarterly results exceeded analyst expectations."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

labels = ["NEGATIVE", "NEUTRAL", "POSITIVE"]
predicted_class = labels[predictions.argmax().item()]
confidence = predictions.max().item()

print(f"Sentiment: {predicted_class} ({confidence:.2f})")
```

## Training Details

- **Epochs**: 3
- **Batch Size**: 32
- **Learning Rate**: 5e-05
- **Optimizer**: AdamW
- **Scheduler**: Cosine
- **Framework**: Unsloth + Transformers

## Citation

If you use this model, please cite:
```bibtex
@misc{modernfinbert2025,
  title={ModernFinBERT: A Modern Approach to Financial Sentiment Analysis},
  author={Neo Yi Peng},
  year={2025},
  howpublished={HuggingFace Model Hub},
  url={https://huggingface.co/neoyipeng/ModernFinBERT-base}
}
```