metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- text-classification
- bert
- finbert
- finance
- sentiment
- sentiment-analysis
- financial-sentiment
datasets:
- FinanceInc/auditor_sentiment
- nickmuchi/financial-classification
- warwickai/financial_phrasebank_mirror
pipeline_tag: text-classification
🎯 FinBERT-Pro
An improved financial sentiment model built on ProsusAI/finbert. Fine-tuned on 3 expert-annotated financial datasets for more robust sentiment classification.
The model provides softmax outputs for three sentiment classes: Positive, Negative, Neutral.
🚀 Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="ENTUM-AI/FinBERT-Pro")
classifier("Stock price soars on record-breaking earnings report")
# [{'label': 'Positive', 'score': 0.99}]
classifier("Company announces quarterly earnings results")
# [{'label': 'Neutral', 'score': 0.98}]
classifier("Revenue decline signals weakening market position")
# [{'label': 'Negative', 'score': 0.98}]
📊 Training Data
Fine-tuned on 3 expert-annotated public datasets:
| Dataset | Samples |
|---|---|
| FinanceInc/auditor_sentiment | ~4.8K |
| nickmuchi/financial-classification | ~5K |
| warwickai/financial_phrasebank_mirror | ~4.8K |
Unlike the original FinBERT (trained on a single dataset), FinBERT-Pro combines multiple expert-annotated sources for better generalization across different financial text styles.
🔍 What's Different from FinBERT?
- Multiple data sources — trained on 3 expert-annotated datasets instead of 1
- Class-weighted training — handles imbalanced label distributions
- Better generalization — diverse training data improves robustness on unseen financial texts
⚠️ Limitations
- English only
- Designed for short financial texts (headlines, news, reports)