metadata

language:
  - as
  - bn
  - brx
  - doi
  - en
  - gom
  - gu
  - hi
  - kn
  - ks
  - kas
  - mai
  - ml
  - mr
  - mni
  - mnb
  - ne
  - or
  - pa
  - sa
  - sat
  - sd
  - snd
  - ta
  - te
  - ur
language_details: >-
  asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr,
  hin_Deva, kan_Knda, kas_Arab, kas_Deva, mai_Deva, mal_Mlym, mar_Deva,
  mni_Beng, mni_Mtei, npi_Deva, ory_Orya, pan_Guru, san_Deva, sat_Olck,
  snd_Arab, snd_Deva, tam_Taml, tel_Telu, urd_Arab
tags:
  - indictrans2
  - translation
  - ai4bharat
  - multilingual
license: mit
datasets:
  - flores-200
  - IN22-Gen
  - IN22-Conv
metrics:
  - bleu
  - chrf
  - chrf++
  - comet
inference: false

IndicTrans2

This is the model card of IndicTrans2 En-Indic Distilled 200M variant.

Please refer to section 7.6: Distilled Models in the TMLR submission for further details on model training, data and metrics.

Usage Instructions

Please refer to the github repository for a detail description on how to use HF compatible IndicTrans2 models for inference.

Citation

If you consider using our work then please cite using:

@article{ai4bharat2023indictrans2,
  title   = {IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
  author  = {AI4Bharat and Jay Gala and Pranjal A. Chitale and Raghavan AK and Sumanth Doddapaneni and Varun Gumma and Aswanth Kumar and Janki Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M. Khapra and Raj Dabre and Anoop Kunchukuttan},
  year    = {2023},
  journal = {arXiv preprint arXiv: 2305.16307}
}