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sk-community/
romanized-hindi-transliterator-60M

Name: romanized-hindi-transliterator-60M
Availability: InStock

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Model Card for Romanized Hindi Transliterator

Model Summary

This model is a Marian-based Seq2Seq transliterator trained on the Romanized Hindi dataset (1.8M pairs).
It maps Hindi text written in the Roman alphabet (Hindish) back into Devanagari script.

Architecture: MarianMT (Seq2Seq Transformer)
Parameters: ~60M
Training Data: romanized_hindi dataset (IAST + noisy social media + Dakshina)
Languages: Hindi (Romanized ↔ Devanagari)

Intended Use

Converting Romanized Hindi to Devanagari for:
- NLP preprocessing
- Search & IR
- Chatbots and voice assistants
- Sentiment analysis

Example Usage

python

from transformers import MarianMTModel, MarianTokenizer

model_name = "sk-community/romanized_hindi_transliterator"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

input_text = "tum kaisa ho"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Output: "तुम कैसा हो"

Performance

BLEU (Hindi): 77.57
CER: 0.09
WER: 0.13

Citation

@article{gharami2025indotranslit,
  title={Modeling Romanized Hindi and Bengali: Dataset Creation and Multilingual LLM Integration},
  author={Kanchon Gharami and Quazi Sarwar Muhtaseem and Deepti Gupta and Lavanya Elluri and Shafika Showkat Moni},
  year={2025}
}

sk-community/romanized-hindi-transliterator-60M