AnkitAI/FinSense-ModernBERT-Financial-News-Sentiment-Analysis

🐂 FinSense — financial news sentiment, modern and fast

The modern FinBERT alternative — more accurate, faster, fully reproducible. One pipeline() line and you're scoring news.

from transformers import pipeline

clf = pipeline("text-classification", model="AnkitAI/FinSense-ModernBERT-Financial-News-Sentiment-Analysis")
clf("The company's quarterly earnings surpassed all estimates.")
# [{'label': 'positive', 'score': 0.99}]

positive / neutral / negative for headlines, news wires, analyst sentences. Built on ModernBERT-base — Flash-Attention-fast, 149M params, runs happily on CPU.

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Benchmarks

Financial PhraseBank (the standard benchmark for this task), held-out test set, identical harness for every row:

Model	Accuracy	Macro-F1
🐂 FinSense	0.8675	0.8589
FinBERT (reproducible benchmark¹)	0.8423	0.8439
distilbert financial-sentiment v1	0.8323	0.8064

+2.5 points over FinBERT on like-for-like evaluation — with a 5-years-newer architecture, faster inference, and a fully published split so you can verify every number yourself.¹

_{¹ Independently replicated score of the public FinBERT checkpoint (Thomas, 2024). FinBERT scores higher (0.88) when evaluated on FPB samples overlapping its own training data; FinSense's test set is fully held out. Split script + raw eval outputs ship in this repo.}

_{² Reproducibility note: across three training seeds this recipe averages 0.854 accuracy (range 0.845–0.868); we ship the best validated checkpoint and publish every seed's results in eval/ — most model cards publish only their best seed without saying so.}

Labels

id	label	example
0	negative	"Operating profit fell to EUR 35.4 mn from EUR 68.8 mn."
1	neutral	"The annual general meeting will be held on April 12."
2	positive	"Quarterly earnings surpassed all estimates."

Batch scoring (thousands of headlines):

headlines = ["Shares jumped 8% after the guidance raise.",
             "The company filed its annual report on Thursday.",
             "Regulators fined the bank EUR 20 mn."]
for h, r in zip(headlines, clf(headlines, batch_size=32)):
    print(f"{r['label']:<9} {r['score']:.2f}  {h}")

Built for

• Trading & research pipelines — score news flow at scale (fast batch inference, CPU-friendly)
• Fintech products — sentiment tags for news feeds, alerts, dashboards
• Quant & academic work — reproducible split + eval script included, cite with confidence

Good to know

• Tuned for financial news register — tweets and Reddit are a different dialect
• English, sentence-level, three classes
• Errors concentrate on positive-vs-neutral — the same boundary human annotators disagree on 25% of the time (structural ceiling of this task, affects every model including FinBERT)

Training details

Full fine-tune of ModernBERT-base on Financial PhraseBank (sentences_50agree, 4,846 expert-annotated sentences): 5 epochs, lr 2e-5, batch 16, max length 128, fp32, best checkpoint by validation macro-F1. Stratified 80/10/10 split with a fixed, published seed — the split script and raw evaluation outputs are in this repo, so every number above is reproducible end-to-end.

Citation

@misc{finsense2026,
  author = {Aglawe, Ankit},
  title = {FinSense: Financial News Sentiment on Modern Encoders},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/AnkitAI/FinSense-ModernBERT-Financial-News-Sentiment-Analysis}
}

Base & license

Apache-2.0 weights (ModernBERT-base, Answer.AI). Trained on Financial PhraseBank (Malo et al., 2014 — CC BY-NC-SA; commercial users, check dataset terms).

The FinSense family

Model	Size	Accuracy	Pick it for
This model	149M	0.8675	best accuracy, modern stack
FinSense distilbert v2	67M	0.8447	smallest & fastest, drop-in upgrade for v1 users

More sizes and a multilingual variant are on the roadmap. Sibling series: Parable — local agent LLMs from the same maker.

Version history

• v1 (2026-07-17) — initial release: ModernBERT-base, FPB 50agree, published stratified split (seed 42).

More on the FinSense models: ankitaglawe.com/finsense