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-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michiyasunaga/BioLinkBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="michiyasunaga/BioLinkBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("michiyasunaga/BioLinkBERT-base") model = AutoModel.from_pretrained("michiyasunaga/BioLinkBERT-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71e61a543551f8136c392b0d08c6600fc5fe79a24caf400131dd9b50c1951206
- Size of remote file:
- 433 MB
- SHA256:
- acc5ae5f16206b893adfcf10772ee4472e24d7847145ac961097bd06129e2ece
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