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")# 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 pytorch_model.bin from surajp/SanBERTa: direct link, hf CLI and curl.
- Browser
- Download file 357 MB
-
https://huggingface.co/surajp/SanBERTa/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://surajp/SanBERTa@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/surajp/SanBERTa/resolve/refs%2Fpr%2F1/pytorch_model.bin
357 MB
- Xet hash:
- 7b4280addf1cd424c0e8277037bab0f3bc7b172ec50523a63a77ddbb2908ad96
- Size of remote file:
- 357 MB
- SHA256:
- a8abe29e49664867731239900f507c57028377364e9129e46e12555569dc30d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.