Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
exbert
linkbert
biolinkbert
fill-mask
question-answering
text-classification
token-classification
text-embeddings-inference
Instructions to use dimfeld/BioLinkBERT-large-feat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dimfeld/BioLinkBERT-large-feat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dimfeld/BioLinkBERT-large-feat")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dimfeld/BioLinkBERT-large-feat") model = AutoModel.from_pretrained("dimfeld/BioLinkBERT-large-feat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d635d116cef87e6cfa05c49915e43708ed63aec77cd290886d9ce9f6270201fe
- Size of remote file:
- 1.33 GB
- SHA256:
- fed75e5716547b54198d4dd123e7a3f3c64a82e1172b3492a11deebd6ab4cd4d
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