Instructions to use dmis-lab/biobert-large-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-large-cased-v1.1-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="dmis-lab/biobert-large-cased-v1.1-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-large-cased-v1.1-squad") model = AutoModelForQuestionAnswering.from_pretrained("dmis-lab/biobert-large-cased-v1.1-squad", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dmis-lab/biobert-large-cased-v1.1-squad: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/dmis-lab/biobert-large-cased-v1.1-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dmis-lab/biobert-large-cased-v1.1-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dmis-lab/biobert-large-cased-v1.1-squad/resolve/main/pytorch_model.bin
1.45 GB
- Xet hash:
- 9c4dff064715461605926a2587aa4820f87c7631ced62869f12ba17fa532a942
- Size of remote file:
- 1.45 GB
- SHA256:
- 13932348759395e3532826862cc87f4bc4b09f37f34e138231fc0328a25bf979
路
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