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

python

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)