Instructions to use dmis-lab/biobert-base-cased-v1.1-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmis-lab/biobert-base-cased-v1.1-squad 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="dmis-lab/biobert-base-cased-v1.1-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-base-cased-v1.1-squad") model = AutoModelForQuestionAnswering.from_pretrained("dmis-lab/biobert-base-cased-v1.1-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dmis-lab/biobert-base-cased-v1.1-squad: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/dmis-lab/biobert-base-cased-v1.1-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dmis-lab/biobert-base-cased-v1.1-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dmis-lab/biobert-base-cased-v1.1-squad/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 317ffe0ec1066169523b3e70d6a133e8731b65e2da26dcc0f119a0737e4e1759
- Size of remote file:
- 433 MB
- SHA256:
- 3f68c261c995fbc3acf35caf38b66466e74919af6230d39590a63d92a26b2125
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.