harixn/IN-finbert

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

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("harixn/IN-finbert")
model = AutoModelForSequenceClassification.from_pretrained("harixn/IN-finbert")

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

# Get probabilities
probs = torch.softmax(outputs.logits, dim=1)
print("Probabilities:", probs)

# Get predicted class
pred_class = torch.argmax(probs, dim=1).item()
classes = ["negative", "neutral", "positive"]
print("Predicted class:", classes[pred_class])

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