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 tokenizer.json from rohitbc/lenu_IN-indic-bert: direct link, hf CLI and curl.
- Browser
- Download file 15.3 MB
-
https://huggingface.co/rohitbc/lenu_IN-indic-bert/resolve/main/tokenizer.json
- Command line
-
hf download hf://rohitbc/lenu_IN-indic-bert/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rohitbc/lenu_IN-indic-bert/resolve/main/tokenizer.json
15.3 MB
- Xet hash:
- 3f8371019b4842545ead8e60dd592bd47e0d7cd13ec5a2b00b32a727422e7f1c
- Size of remote file:
- 15.3 MB
- SHA256:
- d3c1ccf220a9bc498c235bce88506e5d55b292d26187f1df8708eedb0642943c
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