readme: ---
  license: apache-2.0
  language: en
  datasets:
  - pubmed
  tags:
  - bert
  - exbert
  - linkbert
  - biolinkbert
  - feature-extraction
  - fill-mask
  - question-answering
  - text-classification
  - token-classification
  widget:
  - text: "Sunitinib is a tyrosine kinase inhibitor"
  ---
  
  ## BioLinkBERT-large
  
  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).
  
  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).
  
  
  ## Model description
  
  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.
  
  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).
  
  
  ## Intended uses & limitations
  
  The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
  You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
  
  
  ### How to use
  
  To use the model to get the features of a given text in PyTorch:
  
  ```python
  from transformers import AutoTokenizer, AutoModel
  tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-large')
  model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-large')
  inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt")
  outputs = model(**inputs)
  last_hidden_states = outputs.last_hidden_state
  ```
  
  For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
  
  
  ## Evaluation results
  
  When fine-tuned on downstream tasks, LinkBERT achieves the following results.
  
  **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.
  
  |                         | BLURB score | PubMedQA | BioASQ   | MedQA-USMLE |
  | ----------------------  | --------    | -------- | -------  | --------    |
  | PubmedBERT-base         | 81.10       | 55.8     | 87.5     | 38.1        |
  | **BioLinkBERT-base**    | **83.39**   | **70.2** | **91.4** | **40.0** |
  | **BioLinkBERT-large**   | **84.30**   | **72.2** | **94.8** | **44.6** |
  
  |                         | MMLU-professional medicine     |
  | ----------------------  | --------  |
  | GPT-3 (175 params)      | 38.7      |
  | UnifiedQA (11B params)  | 43.2      |
  | **BioLinkBERT-large (340M params)** | **50.7**  |
  
  
  ## Citation
  
  If you find LinkBERT useful in your project, please cite the following:
  
  ```bibtex
  @InProceedings{yasunaga2022linkbert,
    author =  {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
    title =   {LinkBERT: Pretraining Language Models with Document Links},
    year =    {2022},  
    booktitle = {Association for Computational Linguistics (ACL)},  
  }
  ```
  