Instructions to use clagator/biobert_squad2_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clagator/biobert_squad2_cased 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="clagator/biobert_squad2_cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("clagator/biobert_squad2_cased") model = AutoModelForQuestionAnswering.from_pretrained("clagator/biobert_squad2_cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from clagator/biobert_squad2_cased: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/clagator/biobert_squad2_cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://clagator/biobert_squad2_cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/clagator/biobert_squad2_cased/resolve/main/flax_model.msgpack
431 MB
- Xet hash:
- cd4b4f5c24bf93fb9a3ab2124cb9c127ba648153fc75031a52fb2ae06e64f46f
- Size of remote file:
- 431 MB
- SHA256:
- 0f7f63d4d7a1c558d4194e79c814dfacf9095994a882b1c90efc424d054dec67
路
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