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
- Xet hash:
- d6232d3e271a1c45a67e73ad569dfef92894aedac68045694d96ff83cbcf98c7
- Size of remote file:
- 914 MB
- SHA256:
- 3a1ffde8f165ef552456958dcb162905f6b380c7873e5c05226106a5c26145f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.