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Karthik1610/
finbert-financial-sentiment

Name: finbert-financial-sentiment
Availability: InStock

financial-sentiment-analysis

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FinBERT fine-tuned on Financial PhraseBank

A ProsusAI/finbert checkpoint fine-tuned for 3-class sentiment classification (negative / neutral / positive) on the Financial PhraseBank dataset (Malo et al., 2014).

This model classifies the sentiment of text. It does not predict prices, returns, or any tradeable signal. Sentiment on financial news headlines is not, by itself, a trading strategy.

Full source code, training notebook, benchmark harness, and deployment instructions: karthik-1604/financial-sentiment-finbert-api.

How to use

python

from transformers import BertTokenizer, BertForSequenceClassification
import torch

tokenizer = BertTokenizer.from_pretrained("Karthik1610/finbert-financial-sentiment")
model = BertForSequenceClassification.from_pretrained("Karthik1610/finbert-financial-sentiment")

text = "The company reported record profits and raised its annual dividend by 25%."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=64, padding="max_length")
with torch.no_grad():
    logits = model(**inputs).logits
probs = torch.softmax(logits, dim=1).squeeze()

id2label = model.config.id2label
pred_id = int(probs.argmax())
print(id2label[pred_id], float(probs[pred_id]))
# positive 0.99...

Label order: {0: "negative", 1: "neutral", 2: "positive"} (model.config.id2label) - note this is not the same order as the base ProsusAI/finbert checkpoint's native config ({0: positive, 1: negative, 2: neutral}). Always read the id-to-label mapping from model.config rather than hardcoding it; the repo's eval/label_mapping.py does this defensively for exactly that reason.

Training data

Financial PhraseBank (ankurzing/sentiment-analysis-for-financial-news on Kaggle) - 4,838 English-language financial news sentences, annotated by finance professionals with negative / neutral / positive sentiment. Class distribution: 59.4% neutral, 28.2% positive, 12.5% negative.

Training procedure

Base model: ProsusAI/finbert
Max sequence length: 64
Batch size: 32
Epochs: 4
Learning rate: 2e-5, linear warmup (10% of steps) + linear decay
Loss: weighted cross-entropy (inverse-frequency class weights: negative=1.814, neutral=0.382, positive=0.804) to counter class imbalance
Train/test split: stratified 85/15, seed 42
Also validated via stratified 5-fold cross-validation (mean accuracy 88.30% +/- 0.85%, mean weighted-F1 88.39% +/- 0.79%)

Full training code: notebooks/finbert_training.ipynb in the GitHub repo.

Evaluation results

Scored through a shared, model-agnostic harness (eval/harness.py in the repo) against four baselines - majority-class, TF-IDF+LogisticRegression, ProsusAI/finbert zero-shot, and bert-base-uncased fine-tuned the same way - on both the in-domain PhraseBank test split and an out-of-distribution set (FiQA Task-1 sentiment, thresholded from its native [-1, 1] score).

PhraseBank test (in-domain)

Model	Macro-F1	Accuracy
This model (fine-tuned FinBERT)	0.883	0.886
FinBERT, zero-shot	0.865	0.872
bert-base-uncased, fine-tuned	0.845	0.850
TF-IDF + Logistic Regression	0.716	0.752
Majority-class (floor)	0.248	0.594

FiQA OOD (out-of-distribution)

Model	Macro-F1	Accuracy
FinBERT, zero-shot	0.482	0.498
This model (fine-tuned FinBERT)	0.414	0.403
bert-base-uncased, fine-tuned	0.428	0.417
TF-IDF + Logistic Regression	0.289	0.299
Majority-class (floor)	0.049	0.079

Notable finding: zero-shot FinBERT (no fine-tuning at all) generalizes better to the out-of-distribution FiQA set than this fine-tuned model does, despite scoring lower in-domain. Fine-tuning on PhraseBank's narrow style (short, 2014-era financial headlines) buys in-domain accuracy at some cost to robustness on different financial text. See the GitHub repo's benchmark section for the full table, confusion matrices, and cost comparison.

Calibration note: the original FinBERT paper (Araci, 2019) reports ~0.84 F1 across all PhraseBank agreement levels and ~0.95 F1 on the 100%-agreement subset. Our splits differ (own seeded 85/15 split, no agreement filtering), so these numbers are not directly comparable - listed for context only.

Limitations

Small dataset (4,838 sentences; ~726 in the test split) - metrics have real variance.
English-language, 2014-era financial news headlines only. Performance on other languages, document types, or more recent terminology is untested (the FiQA OOD result above is the closest measurement of that kind of gap).
Sentiment classification, not a trading signal - see the notice at the top.
Fine-tuned on data licensed CC-BY-NC-SA-4.0 (Financial PhraseBank via Kaggle); this checkpoint is released under the same license and terms (non-commercial, share-alike).

Citation

If referencing the base model or dataset:

@article{araci2019finbert,
  title={FinBERT: Financial Sentiment Analysis with Pre-trained Language Models},
  author={Araci, Dogu},
  journal={arXiv preprint arXiv:1908.10063},
  year={2019}
}

@article{malo2014good,
  title={Good debt or bad debt: Detecting semantic orientations in economic texts},
  author={Malo, Pekka and Sinha, Ankur and Korhonen, Pekka and Wallenius, Jyrki and Takala, Pyry},
  journal={Journal of the Association for Information Science and Technology},
  year={2014}
}