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tsmaitry/indic-toxicity-detector
indic-toxicity-detector is a text classification model from tsmaitry. Use it when you need a label for a piece of text. The card lists the license as apache-2.0.
Fine-tuned version of ai4bharat/IndicBERTv2-MLM-only for toxicity detection in multilingual text (English, Hinglish, Hindi, Tamil).
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From the Hugging Face model README
Fine-tuned version of ai4bharat/IndicBERTv2-MLM-only for toxicity detection in multilingual text (English, Hinglish, Hindi, Tamil).
This model classifies text as either toxic or non-toxic. It was trained on a balanced dataset with class weights to handle imbalanced data.
Languages Supported:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("indic-toxicity-detector")
tokenizer = AutoTokenizer.from_pretrained("indic-toxicity-detector")
# Predict
def predict_toxicity(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
outputs = model(**inputs)
probabilities = torch.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(probabilities, dim=-1).item()
confidence = probabilities[0][predicted_class].item()
label = model.config.id2label[predicted_class]
return {"label": label, "confidence": confidence}
# Example
result = predict_toxicity("You are amazing!")
print(result) # {'label': 'non-toxic', 'confidence': 0.95}
@misc{indic-toxicity-detector,
author = {Your Name},
title = {IndicBERT Multilingual Toxicity Detector},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/indic-toxicity-detector}
}
Apache 2.0