Translation
Transformers
PyTorch
Safetensors
IndicTrans
text2text-generation
indictrans2
ai4bharat
multilingual
custom_code
Instructions to use ai4bharat/indictrans2-en-indic-dist-200M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ai4bharat/indictrans2-en-indic-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="ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
superb indic translater
#11
by subbur - opened
thanks for the efforts and licensing liberally. model is working well for telugu and kannada translations from english.
Thank you for appreciating the models. You might like the RoPE variants of IT2 which support a longer context length (~2048 tokens, and occasionally higher) and have a considerable boost in translation capabilities. These models were also ported CT2 for much faster and efficient inference (can easily accomodate beam_size=10 for superior translations).