Text Classification
Transformers
PyTorch
English
bert
feature-extraction
exbert
linkbert
biolinkbert
fill-mask
question-answering
token-classification
text-embeddings-inference
Instructions to use michiyasunaga/BioLinkBERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michiyasunaga/BioLinkBERT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="michiyasunaga/BioLinkBERT-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("michiyasunaga/BioLinkBERT-large") model = AutoModel.from_pretrained("michiyasunaga/BioLinkBERT-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from michiyasunaga/BioLinkBERT-large: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/michiyasunaga/BioLinkBERT-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://michiyasunaga/BioLinkBERT-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/michiyasunaga/BioLinkBERT-large/resolve/main/pytorch_model.bin
1.33 GB
- Xet hash:
- d635d116cef87e6cfa05c49915e43708ed63aec77cd290886d9ce9f6270201fe
- Size of remote file:
- 1.33 GB
- SHA256:
- fed75e5716547b54198d4dd123e7a3f3c64a82e1172b3492a11deebd6ab4cd4d
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