hindi-roberta Toxicity Classifier

hindi-roberta-toxicity-classifier is an HindRoBERTa based model that can be used for judging the toxicity level of a given Hindi text string. This model was trained on the Polygl0t/hindi-toxicity-qwen-annotations dataset.

Details

For training, we added a classification head with a single regression output to l3cube-pune/hindi-roberta. Only the classification head was trained, i.e., the rest of the model was frozen.

Dataset: hindi-toxicity-qwen-annotations
Language: Hindi
Number of Training Epochs: 20
Batch size: 256
Optimizer: torch.optim.AdamW
Learning Rate: 3e-4
Eval Metric: f1-score

This repository has the source code used to train this model.

Evaluation Results

Confusion Matrix

	1	2	3	4	5
1	11526	2601	134	7	0
2	722	1713	281	10	0
3	240	1092	590	7	2
4	21	242	308	104	13
5	5	46	78	68	123

Precision: 0.58656
Recall: 0.45341
F1 Macro: 0.47433
Accuracy: 0.7028

Usage

Here's an example of how to use the Toxicity Classifier:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("Polygl0t/hindi-roberta-toxicity-classifier")
model = AutoModelForSequenceClassification.from_pretrained("Polygl0t/hindi-roberta-toxicity-classifier")
model.to(device)

text = "यह एक उदाहरण है।"
encoded_input  =  tokenizer(text, return_tensors="pt", padding="longest", truncation=True).to(device)

with  torch.no_grad():
    model_output  =  model(**encoded_input)
    logits  =  model_output.logits.squeeze(-1).float().cpu().numpy()

# scores are produced in the range [0, 4]. To convert to the range [1, 5], we can simply add 1 to the score.
score = [x + 1 for x in logits.tolist()][0]

print({
 "text": text,
 "score": score,
 "int_score": [int(round(max(0, min(score, 4)))) + 1 for score in logits][0],
})

Cite as 🤗

@misc{shiza2026lilmoo,
      title={{Raising Bars, Not Parameters: LilMoo Compact Language Model for Hindi}}, 
      author={Shiza Fatimah and Aniket Sen and Sophia Falk and Florian Mai and Lucie Flek and Nicholas Kluge Corr{\^e}a},
      year={2026},
      eprint={2603.03508},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.03508}, 
}

Aknowlegments

Polyglot is a project funded by the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the State of North Rhine-Westphalia (MWK) as part of TRA Sustainable Futures (University of Bonn) and the Excellence Strategy of the federal and state governments.

We also gratefully acknowledge the granted access to the Marvin cluster hosted by University of Bonn along with the support provided by its High Performance Computing & Analytics Lab.

License

According to l3cube-pune/hindi-roberta, the model is released under cc-by-4.0. For any queries, please get in touch with the authors of the original paper tied to hindi-roberta.