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
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## BioLinkBERT-large
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BioLinkBERT-large model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT).
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This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as [BLURB](https://microsoft.github.io/BLURB/) and [MedQA-USMLE](https://github.com/jind11/MedQA).
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## BioLinkBERT-large
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**This is identical to `michiyasunaga/BioLinkBERT-large` except the pipeline tag in the model card was changed to feature-extraction.**
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BioLinkBERT-large model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT).
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This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as [BLURB](https://microsoft.github.io/BLURB/) and [MedQA-USMLE](https://github.com/jind11/MedQA).
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