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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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duplicated_from: michiyasunaga/BioLinkBERT-large |
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--- |
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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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## 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-large') |
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model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-large') |
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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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