Instructions to use rohitbc/lenu_IN-indic-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rohitbc/lenu_IN-indic-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rohitbc/lenu_IN-indic-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rohitbc/lenu_IN-indic-bert") model = AutoModelForSequenceClassification.from_pretrained("rohitbc/lenu_IN-indic-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rohitbc/lenu_IN-indic-bert: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/rohitbc/lenu_IN-indic-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rohitbc/lenu_IN-indic-bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rohitbc/lenu_IN-indic-bert/resolve/main/pytorch_model.bin
134 MB
- Xet hash:
- fd8109a32731e8cb04d5940d617e867a09b1e621743822edcdfab70758616594
- Size of remote file:
- 134 MB
- SHA256:
- a72d185701e8239ad111362c04c533161f231dc44b1db87c41bcb8f865eb7d1a
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