
## 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)},  
}
```

{"language": "en", "license": "apache-2.0", "tags": ["bert", "exbert", "linkbert", "biolinkbert", "feature-extraction", "fill-mask", "question-answering", "text-classification", "token-classification"], "datasets": ["pubmed"], "widget": [{"text": "Sunitinib is a tyrosine kinase inhibitor"}]}
transformers pytorch bert feature-extraction exbert linkbert biolinkbert fill-mask question-answering text-classification token-classification en dataset:pubmed arxiv:2203.15827 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #feature-extraction #exbert #linkbert #biolinkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-pubmed #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us 

BioLinkBERT-large
-----------------


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:


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.




If you find LinkBERT useful in your project, please cite the following:



### How to use


To use the model to get the features of a given text in PyTorch:


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.




If you find LinkBERT useful in your project, please cite the following:
TAGS
#transformers #pytorch #bert #feature-extraction #exbert #linkbert #biolinkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-pubmed #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us 
 ### How to use


To use the model to get the features of a given text in PyTorch:


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.




If you find LinkBERT useful in your project, please cite the following:
passage: TAGS
#transformers #pytorch #bert #feature-extraction #exbert #linkbert #biolinkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-pubmed #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us 
### How to use


To use the model to get the features of a given text in PyTorch:


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.




If you find LinkBERT useful in your project, please cite the following: