# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Kroalist/financial-sentiment-bert-base")
model = AutoModelForSequenceClassification.from_pretrained("Kroalist/financial-sentiment-bert-base")Quick Links
Financial Sentiment BERT-Base (BERT-base-uncased fine-tune)
Fine-tuned on Financial PhraseBank for three-way sentiment.
| 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
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
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google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kroalist/financial-sentiment-bert-base")