Instructions to use anwesham/indicbert_ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anwesham/indicbert_ur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="anwesham/indicbert_ur")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("anwesham/indicbert_ur") model = AutoModelForTokenClassification.from_pretrained("anwesham/indicbert_ur", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anwesham/indicbert_ur: direct link, hf CLI and curl.
- Browser
- Download file 131 MB
-
https://huggingface.co/anwesham/indicbert_ur/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anwesham/indicbert_ur/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anwesham/indicbert_ur/resolve/main/pytorch_model.bin
131 MB
- Xet hash:
- b0757defcefdc86643d708a73edb55a4845988de81ce85328104d60866e9ee0b
- Size of remote file:
- 131 MB
- SHA256:
- b03ad1ff806dfa5fe87c0508c1f5821d06209f12bf992fca2323ce49271ba1a2
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