finbert-finance-sentiment

A 3-class financial sentiment model that labels English financial text as positive, negative, or neutral. It is a fine-tuned version of ProsusAI/finbert, further trained on the Financial PhraseBank dataset.

Model description

The model takes a sentence of financial text — such as a line from a news headline, earnings report, or market commentary — and predicts its sentiment. It builds on FinBERT, a BERT model pretrained on a large financial corpus, and sharpens it on expert-annotated financial sentences.

• Base model: ProsusAI/finbert
• Task: 3-class sentiment classification (text-classification)
• Language: English (financial domain)
• Labels: negative (0), neutral (1), positive (2)

Intended uses & limitations

Intended uses: Sentiment scoring of financial text — news headlines, earnings commentary, analyst notes, and similar short financial statements.

Limitations:

• English and financial domain only; performance on general or non-financial text will be weaker.
• Trained on relatively formal financial news sentences, so very informal text (e.g. social media slang, tickers, memes) may be less reliable.
• Sentiment here means market/financial tone, not personal opinion — "shares fell" is negative regardless of phrasing.

How to use

from transformers import pipeline

clf = pipeline("text-classification", model="rajaadil/finbert-finance-sentiment")
print(clf("The company reported record profits and raised its dividend."))
# [{'label': 'positive', 'score': 0.97}]

Training data

Trained on the sentences_allagree subset of the Financial PhraseBank dataset — the highest-quality subset, containing only sentences where all human annotators agreed on the label.

• Split: 80% train / 20% test
• Classes: negative, neutral, positive (neutral is the majority class)

Training procedure

Hyperparameter	Value
Base model	ProsusAI/finbert
Epochs	4 (best checkpoint kept)
Train batch size	16
Learning rate	2e-5
Max sequence length	128
Best model selected on	macro-F1

Evaluation results

Best checkpoint (epoch 3), evaluated on the held-out test set:

Metric	Value
Accuracy	0.820
F1 (macro)	0.811

Macro-F1 is reported alongside accuracy because the dataset is class-imbalanced (neutral is the majority class); macro-F1 reflects performance across all three classes rather than favouring the majority.

Framework versions

• Transformers
• PyTorch
• Datasets

Author

Built by rajaadil.