neoyipeng/ModernFinBERT-base

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

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:

@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}
}