Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/EmilyAlsentzer/clinicalBERT
repository for Publicly Available Clinical BERT Embeddings
https://github.com/EmilyAlsentzer/clinicalBERT
Last synced: about 1 month ago
JSON representation
repository for Publicly Available Clinical BERT Embeddings
- Host: GitHub
- URL: https://github.com/EmilyAlsentzer/clinicalBERT
- Owner: EmilyAlsentzer
- License: mit
- Created: 2019-04-05T20:50:16.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-08-25T14:11:44.000Z (over 4 years ago)
- Last Synced: 2024-04-28T05:28:10.705Z (8 months ago)
- Language: Python
- Size: 95.7 KB
- Stars: 630
- Watchers: 25
- Forks: 126
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-bert - EmilyAlsentzer/clinicalBERT
README
# clinicalBERT
Repository for [Publicly Available Clinical BERT Embeddings](https://www.aclweb.org/anthology/W19-1909/) (NAACL Clinical NLP Workshop 2019)
## Using Clinical BERT
UPDATE: You can now use ClinicalBERT directly through the [transformers](https://github.com/huggingface/transformers) library. Check out the [Bio+Clinical BERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) and [Bio+Discharge Summary BERT](https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT) model pages for instructions on how to use the models within the Transformers library.
## Download Clinical BERT
The Clinical BERT models can also be downloaded [here](https://www.dropbox.com/s/8armk04fu16algz/pretrained_bert_tf.tar.gz?dl=0), or via
```
wget -O pretrained_bert_tf.tar.gz https://www.dropbox.com/s/8armk04fu16algz/pretrained_bert_tf.tar.gz?dl=1
```
`biobert_pretrain_output_all_notes_150000` corresponds to Bio+Clinical BERT, and `biobert_pretrain_output_disch_100000` corresponds to Bio+Discharge Summary BERT. Both models are finetuned from [BioBERT](https://arxiv.org/abs/1901.08746). We specifically use the [BioBERT-Base v1.0 (+ PubMed 200K + PMC 270K)](https://github.com/naver/biobert-pretrained) version of BioBERT.
`bert_pretrain_output_all_notes_150000` corresponds to Clinical BERT, and `bert_pretrain_output_disch_100000` corresponds to Discharge Summary BERT. Both models are finetuned from the cased version of BERT, specifically cased_L-12_H-768_A-12.
## Reproduce Clinical BERT
#### Pretraining
To reproduce the steps necessary to finetune BERT or BioBERT on MIMIC data, follow the following steps:
1. Run `format_mimic_for_BERT.py` - Note you'll need to change the file paths at the top of the file.
2. Run `create_pretrain_data.sh`
3. Run `finetune_lm_tf.sh`
Note: See issue [#4](https://github.com/EmilyAlsentzer/clinicalBERT/issues/4) for ways to improve section splitting code.
#### Downstream Tasks
To see an example of how to use clinical BERT for the Med NLI tasks, go to the `run_classifier.sh` script in the downstream_tasks folder. To see an example for NER tasks, go to the `run_i2b2.sh` script.
## Contact
Please post a Github issue or contact [email protected] if you have any questions.
## Citation
Please acknowledge the following work in papers or derivative software:
Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott. 2019. Publicly available clinical BERT embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 72-78, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
```
@inproceedings{alsentzer-etal-2019-publicly,
title = "Publicly Available Clinical {BERT} Embeddings",
author = "Alsentzer, Emily and
Murphy, John and
Boag, William and
Weng, Wei-Hung and
Jin, Di and
Naumann, Tristan and
McDermott, Matthew",
booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W19-1909",
doi = "10.18653/v1/W19-1909",
pages = "72--78"
}
```