README.md

metadata

license: apache-2.0
datasets:
  - harixn/indian_news_sentiment
language:
  - en
base_model:
  - google-bert/bert-base-uncased
pipeline_tag: text-classification
tags:
  - finance

FinBERT Model Card

Model Details

• Model Name: FinBERT
• Model Type: BERT (bert-base-uncased)
• Task: Sentiment Analysis (Stock Market)
• Number of Labels: 3 (positive, negative, neutral)
• Intended Use: Predict sentiment of financial news and social media posts related to the Indian stock market.

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer from Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("harixn/IN-finbert")
model = AutoModelForSequenceClassification.from_pretrained("harixn/IN-finbert")

# Example inference
text = "The stock price of XYZ surged today."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

Training Data

• Fine-tuned on labeled Indian stock market news and social media datasets.
• Labels: positive, negative, neutral.

Limitations and Risks

• Trained specifically for Indian stock market context.
• May not generalize well to other financial markets.
• Predictions should not be used as financial advice.

Evaluation

• Evaluated on held-out validation set of Indian stock market texts.
• Metrics: Accuracy, F1-score per class.

Model Files

• pytorch_model.bin: Trained model weights
• config.json: Model configuration
• vocab.txt, tokenizer_config.json, special_tokens_map.json, tokenizer.json: Tokenizer files

Citation

If you use this model, please cite it as:

FinBERT: Sentiment Analysis Model for Indian Stock Market, harixn, 2025