Fine-tuned from ProsusAI/finbert on the SEntFiN 1.0 dataset of human-annotated Indian financial news headlines sourced from the Economic Times and Moneycontrol, covering NSE500-listed companies.

Why This Model

ProsusAI/finbert was trained primarily on US/EU financial text (SEC filings, financial news wire). On Indian financial headlines it scores approximately F1 = 0.76. This model addresses that domain gap by fine-tuning on 9,514 expert-annotated Indian financial headlines, achieving F1 = 0.873 on a held-out validation set — a +11% improvement on Indian financial text.

Model Details

Property	Value
Base model	`ProsusAI/finbert`
Architecture	BERT-base (110M parameters)
Task	3-class sentiment classification
Labels	`positive` (0), `negative` (1), `neutral` (2)
Training data	SEntFiN 1.0 — 8,086 samples
Validation data	SEntFiN 1.0 — 1,428 samples
Best epoch	3 / 4
F1 weighted	0.873
Accuracy	0.873
Training hardware	Kaggle T4 GPU (fp16)

Training Configuration

python

TrainingArguments(
    num_train_epochs=4,
    per_device_train_batch_size=32,
    learning_rate=2e-5,
    warmup_steps=100,
    weight_decay=0.01,
    fp16=True,
    load_best_model_at_end=True,
    metric_for_best_model="f1_weighted",
)

Per-Class Results (Validation Set)

Class	Precision	Recall	F1	Support
positive	0.91	0.86	0.89	504
negative	0.87	0.89	0.88	407
neutral	0.84	0.87	0.86	517
weighted avg	0.87	0.87	0.87	1428

Dataset

SEntFiN 1.0 (paper, GitHub) is a human-annotated dataset of 10,753 financial news headlines with entity-sentiment annotations, covering 920 companies in the NSE500 index (2002–2017). Headlines were sourced from the Economic Times and Moneycontrol.

Preprocessing applied:

Headlines with multiple entities having conflicting sentiments were dropped (1,239 rows)
Remaining 9,514 rows used for training
Stratified 85/15 train/validation split

Usage

python

from transformers import pipeline

pipe = pipeline(
    "text-classification",
    model="tahp0604/finbert-sentfin",
)

headlines = [
    "Reliance Industries posts record quarterly profit",
    "HDFC Bank shares fall 4% after RBI penalty",
    "TCS Q4 results in line with analyst estimates",
]

for h in headlines:
    result = pipe(h)[0]
    print(f"[{result['label'].upper():8s} {result['score']:.2f}]  {h}")

Output:

[POSITIVE 0.94]  Reliance Industries posts record quarterly profit
[NEGATIVE 0.91]  HDFC Bank shares fall 4% after RBI penalty
[NEUTRAL  0.83]  TCS Q4 results in line with analyst estimates

Direct inference

python

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("tahp0604/finbert-sentfin")
model     = AutoModelForSequenceClassification.from_pretrained("tahp0604/finbert-sentfin")
model.eval()

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
    with torch.no_grad():
        probs = torch.softmax(model(**inputs).logits, dim=-1)[0]
    idx   = int(probs.argmax())
    label = model.config.id2label[idx]
    return label, float(probs[idx])

label, confidence = predict("Infosys raises revenue guidance for FY26")
print(f"{label} ({confidence:.2f})")  # positive (0.89)

Label Mapping

python

id2label = {0: "positive", 1: "negative", 2: "neutral"}
label2id = {"positive": 0, "negative": 1, "neutral": 2}

This is identical to ProsusAI/finbert — the model is a drop-in replacement.

Intended Use

Sentiment scoring of Indian financial news headlines
NSE/BSE stock news sentiment analysis
Component in financial signal fusion pipelines

Limitations

Trained on headlines from 2002–2017 — may not capture terminology from newer financial instruments or recent regulatory changes
Optimised for short headlines (≤ 128 tokens) — performance may degrade on full article text
Covers Indian equities (NSE500) — may underperform on commodities, currencies, or global markets
Not intended for investment decisions

Citation

If you use this model, please cite the SEntFiN dataset:

bibtex

@article{sinha2022sentfin,
  title={SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News},
  author={Sinha, Ankur and Kedas, Satishwar and Kumar, Rishu and Malo, Pekka},
  journal={Journal of the Association for Information Science and Technology},
  volume={73},
  number={9},
  pages={1314--1335},
  year={2022}
}

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