iitb-en-indic-without-punct

iitb-en-indic-without-punct

This model is a fine-tuned version of ai4bharat/indictrans2-en-indic-dist-200M designed to improve punctuation robustness in English-to-Marathi machine translation.

It was introduced in the paper Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation.

Model Description

Traditional machine translation systems often struggle when punctuation is missing or ambiguous in the source text. This checkpoint represents Approach 2 from the associated research, where the IndicTrans2 model was directly fine-tuned on the IITB-ENG-MAR dataset with all English source punctuations removed. This allows the model to implicitly learn the context required to resolve semantic and structural ambiguities when punctuation is absent.

• Paper: Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation
• Repository: Viram_Marathi (GitHub)
• Demo: Punctuation Robust Translation Demo
• Base Model: ai4bharat/indictrans2-en-indic-dist-200M

Intended Uses & Limitations

This model is intended for translating English text into Marathi, particularly in scenarios where the source English text might lack proper punctuation or contain punctuation-induced ambiguities.

Training and Evaluation Data

The model was fine-tuned on a modified version of the English-Marathi parallel corpus from IIT Bombay.

It was evaluated on the Virām (formerly PEM) diagnostic benchmark, which consists of 54 manually curated, punctuation-ambiguous instances designed to assess how well MT systems preserve meaning when punctuation is varied or missing.

Training Procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8

Evaluation Results

Framework versions

• Transformers 4.53.2
• Pytorch 2.4.0a0+f70bd71a48.nv24.06
• Datasets 2.21.0
• Tokenizers 0.21.4

Citation

If you use this model or the Virām benchmark, please cite:

@article{shejole2025assessing,
  title={Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation},
  author={Shejole, Kaustubh and others},
  journal={arXiv preprint arXiv:2601.09725},
  year={2025}
}