Instructions to use surajp/SanBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surajp/SanBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="surajp/SanBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("surajp/SanBERTa") model = AutoModelForMaskedLM.from_pretrained("surajp/SanBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from surajp/SanBERTa: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/surajp/SanBERTa/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://surajp/SanBERTa/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/surajp/SanBERTa/resolve/main/flax_model.msgpack
265 MB
- Xet hash:
- a2a1916db956655b7cf41aa564484587df02e11371e1d148f216dee5664b88f5
- Size of remote file:
- 265 MB
- SHA256:
- 172d685114761ec2b7bc9d939c8702b0601359022144ace81f877bc854b22080
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