Translation
Transformers
PyTorch
IndicTrans
text2text-generation
indictrans2
ai4bharat
multilingual
custom_code
Instructions to use Raghavan/indictrans2-indic-en-dist-200M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Raghavan/indictrans2-indic-en-dist-200M with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Raghavan/indictrans2-indic-en-dist-200M", trust_remote_code=True)# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("Raghavan/indictrans2-indic-en-dist-200M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
IndicTrans2
This is the model card of IndicTrans2 Indic-En 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}
}
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