Item	Value
Base model	`bert-base-uncased`
Dataset	Financial PhraseBank
Labels	positive (0) · negative (1) · neutral (2)
Epochs	4
Hardware	CPU-only training

Evaluation Results (Validation + Test)

Validation Accuracy (best): 81.32%

Test Performance:

              precision    recall  f1-score   support

    positive       0.71      0.75      0.73       204
    negative       0.67      0.81      0.74        91
     neutral       0.88      0.82      0.85       432

    accuracy                           0.80       727
   macro avg       0.75      0.79      0.77       727
weighted avg       0.81      0.80      0.80       727

Training completed in 17m 9s. Logs are available in training_logs.csv and training curve in training_metrics.png.

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tok   = AutoTokenizer.from_pretrained("Kroalist/financial-sentiment-bert-base")
model = AutoModelForSequenceClassification.from_pretrained("Kroalist/financial-sentiment-bert-base")

Last updated: 2025-04-23