Instructions to use gsarti/biobert-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/biobert-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gsarti/biobert-nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gsarti/biobert-nli") model = AutoModel.from_pretrained("gsarti/biobert-nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from gsarti/biobert-nli: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/gsarti/biobert-nli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://gsarti/biobert-nli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/gsarti/biobert-nli/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- 1da63b7c033f56f099ed2f322435c7d4203330d6867d103aa9b8c9d42182235a
- Size of remote file:
- 433 MB
- SHA256:
- 09619528aad469ce2ae3489f074533a47e547b1d50a6e24940b68d911e5f172b
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