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---
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license: apache-2.0
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language: en
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datasets:
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- pubmed
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tags:
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- bert
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- exbert
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- linkbert
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- biolinkbert
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- feature-extraction
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- fill-mask
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- question-answering
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- text-classification
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- token-classification
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widget:
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- text: "Sunitinib is a tyrosine kinase inhibitor"
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---
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## BioLinkBERT-base
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BioLinkBERT-base 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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## Model description
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LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval).
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## Intended uses & limitations
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The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
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You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
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### How to use
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To use the model to get the features of a given text in PyTorch:
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```python
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from transformers import AutoTokenizer, AutoModel
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tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-base')
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model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-base')
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inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt")
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outputs = model(**inputs)
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last_hidden_states = outputs.last_hidden_state
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```
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For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
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## Evaluation results
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When fine-tuned on downstream tasks, LinkBERT achieves the following results.
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**Biomedical benchmarks ([BLURB](https://microsoft.github.io/BLURB/), [MedQA](https://github.com/jind11/MedQA), [MMLU](https://github.com/hendrycks/test), etc.):** BioLinkBERT attains new state-of-the-art.
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| | BLURB score | PubMedQA | BioASQ | MedQA-USMLE |
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| ---------------------- | -------- | -------- | ------- | -------- |
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| PubmedBERT-base | 81.10 | 55.8 | 87.5 | 38.1 |
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| **BioLinkBERT-base** | **83.39** | **70.2** | **91.4** | **40.0** |
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| **BioLinkBERT-large** | **84.30** | **72.2** | **94.8** | **44.6** |
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| | MMLU-professional medicine |
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| ---------------------- | -------- |
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| GPT-3 (175 params) | 38.7 |
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| UnifiedQA (11B params) | 43.2 |
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| **BioLinkBERT-large (340M params)** | **50.7** |
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## Citation
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If you find LinkBERT useful in your project, please cite the following:
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```bibtex
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@InProceedings{yasunaga2022linkbert,
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author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
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title = {LinkBERT: Pretraining Language Models with Document Links},
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year = {2022},
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booktitle = {Association for Computational Linguistics (ACL)},
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}
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```
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