Instructions to use surajp/sanbert-from-indicbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surajp/sanbert-from-indicbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="surajp/sanbert-from-indicbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("surajp/sanbert-from-indicbert") model = AutoModelForMaskedLM.from_pretrained("surajp/sanbert-from-indicbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from surajp/sanbert-from-indicbert: direct link, hf CLI and curl.
- Browser
- Download file 15.3 MB
-
https://huggingface.co/surajp/sanbert-from-indicbert/resolve/main/tokenizer.json
- Command line
-
hf download hf://surajp/sanbert-from-indicbert/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/surajp/sanbert-from-indicbert/resolve/main/tokenizer.json
15.3 MB
- Xet hash:
- 0ad6822529f758ccfc03bbacee8ba5068acdc8be56e592b4e7cf0e3562fab0c6
- Size of remote file:
- 15.3 MB
- SHA256:
- d55509a424968c0e43385b7bf796b3446de492397bc0e80b861c0e968acf661f
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