Text Classification
Transformers
PyTorch
English
bert
sentiment-analysis
finance
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Kroalist/financial-sentiment-bert")
model = AutoModelForSequenceClassification.from_pretrained("Kroalist/financial-sentiment-bert")Quick Links
Financial Sentiment BERT (FinBERT fine-tune)
Sentence-level classifier for English financial news.
| Item | Value |
|---|---|
| Base model | ProsusAI/finbert |
| Dataset | Financial PhraseBank |
| Labels | positive (0) · negative (1) · neutral (2) |
| Epochs | 3 (early-stopped) |
| Best val acc | 0.8356 |
| Test acc | 0.81 |
| Hardware | CPU-only training |
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tok = AutoTokenizer.from_pretrained("Kroalist/financial-sentiment-bert")
model = AutoModelForSequenceClassification.from_pretrained("Kroalist/financial-sentiment-bert")
Last updated: 2025-04-23
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Base model
ProsusAI/finbertEvaluation results
- accuracy on Financial PhraseBanktest set self-reported0.810
- macro F1 on Financial PhraseBanktest set self-reported0.790
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kroalist/financial-sentiment-bert")