Instructions to use exafluence/BERT-ClinicalQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use exafluence/BERT-ClinicalQA 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="exafluence/BERT-ClinicalQA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("exafluence/BERT-ClinicalQA") model = AutoModelForQuestionAnswering.from_pretrained("exafluence/BERT-ClinicalQA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from exafluence/BERT-ClinicalQA: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/exafluence/BERT-ClinicalQA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://exafluence/BERT-ClinicalQA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/exafluence/BERT-ClinicalQA/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 235a08306541980cb00d7c55864cb10202b4f3d0b0bd0240302b41441521f74f
- Size of remote file:
- 265 MB
- SHA256:
- 8d82812b5bd2fc50ba3d4e53551603b6881ffa05ba061e19bc16aa02a03b52f4
路
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