Instructions to use bisectgroup/biobigbird-base-stage3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bisectgroup/biobigbird-base-stage3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bisectgroup/biobigbird-base-stage3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bisectgroup/biobigbird-base-stage3") model = AutoModelForMaskedLM.from_pretrained("bisectgroup/biobigbird-base-stage3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from bisectgroup/biobigbird-base-stage3: direct link, hf CLI and curl.
- Browser
- Download file 454 MB
-
https://huggingface.co/bisectgroup/biobigbird-base-stage3/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://bisectgroup/biobigbird-base-stage3/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/bisectgroup/biobigbird-base-stage3/resolve/main/flax_model.msgpack
454 MB
- Xet hash:
- 8aaf364e8650b9257b4d0153c4ef347e2aeece96ce522fd34fa8eef5b02409ef
- Size of remote file:
- 454 MB
- SHA256:
- 9c50493a2a7448117d4f9b7bbb404a3c5fe92d712f5459921e1ff2448c48283c
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