finbert-sentiment-tr

A fine-tuned version of distilbert/distilbert-base-uncased for financial text sentiment classification (negative / neutral / positive).

Usage

```python from transformers import pipeline

classifier = pipeline("text-classification", model="leplebi/finbert-sentiment-tr", top_k=None) classifier("The company reported strong quarterly earnings.") ```

Training Details

• Base model: distilbert/distilbert-base-uncased
• Dataset: Financial PhraseBank (Malo et al., 2014), "50Agree" configuration (~4846 sentences)
• Epochs: 4 (best checkpoint selected at epoch 3, based on f1_macro)
• Learning rate: 2e-5
• Batch size: 16

Results (test set, 727 examples)

Metric	Value
Accuracy	0.8638
F1 Macro	0.8497
F1 Weighted	0.8636
Precision Macro	0.8372
Recall Macro	0.8675

Limitations

• Trained on short, single-sentence financial statements; performance on longer reports or paragraphs may differ.
• Not intended for use on non-English text.

License / Data Source Note

This model was trained using the Financial PhraseBank dataset (Malo et al., 2014), which is distributed under an academic/research license. Please review the original license terms before any commercial use.