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 pytorch_model.bin from clagator/biobert_squad2_cased: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/clagator/biobert_squad2_cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://clagator/biobert_squad2_cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/clagator/biobert_squad2_cased/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 1f295f81029d999cf5923ccba98f0be29e4f46b88935479a7062f35c4543fd64
- Size of remote file:
- 433 MB
- SHA256:
- ac0f3630d2725b0e553ced88374ab2cca916700c1e4ee3ed241bec396285a469
路
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