Instructions to use gdario/biobert_bioasq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gdario/biobert_bioasq with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="gdario/biobert_bioasq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("gdario/biobert_bioasq") model = AutoModelForQuestionAnswering.from_pretrained("gdario/biobert_bioasq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from gdario/biobert_bioasq: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/gdario/biobert_bioasq/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://gdario/biobert_bioasq/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/gdario/biobert_bioasq/resolve/main/flax_model.msgpack
431 MB
- Xet hash:
- 9376a042d59f8e6ab1162eadbaa6c6257efe08633f50bf7c63276b6f00f7cfdc
- Size of remote file:
- 431 MB
- SHA256:
- b265379233f055e36d229fdf5fe62df73dc3a1b07fc0c130dcd67ac691bab9a7
路
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