BioLinkBERT-large
This is identical to michiyasunaga/BioLinkBERT-large except the pipeline tag in the model card was changed to feature-extraction.
BioLinkBERT-large model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository.
This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE.
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:
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 or follow any other BERT fine-tuning codebases.
Evaluation results
When fine-tuned on downstream tasks, LinkBERT achieves the following results.
Biomedical benchmarks (BLURB, MedQA, MMLU, 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:
@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)},
}