|
--- |
|
language: |
|
- en |
|
tags: |
|
- bert |
|
- bluebert |
|
license: cc0-1.0 |
|
datasets: |
|
- PubMed |
|
|
|
--- |
|
|
|
# BlueBert-Base, Uncased, PubMed |
|
|
|
## Model description |
|
|
|
A BERT model pre-trained on PubMed abstracts. |
|
|
|
## Intended uses & limitations |
|
|
|
#### How to use |
|
|
|
Please see https://github.com/ncbi-nlp/bluebert |
|
|
|
## Training data |
|
|
|
We provide [preprocessed PubMed texts](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/pubmed_uncased_sentence_nltk.txt.tar.gz) that were used to pre-train the BlueBERT models. |
|
The corpus contains ~4000M words extracted from the [PubMed ASCII code version](https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PubMed/). |
|
|
|
Pre-trained model: https://huggingface.co/bert-large-uncased |
|
|
|
## Training procedure |
|
|
|
* lowercasing the text |
|
* removing speical chars `\x00`-`\x7F` |
|
* tokenizing the text using the [NLTK Treebank tokenizer](https://www.nltk.org/_modules/nltk/tokenize/treebank.html) |
|
|
|
Below is a code snippet for more details. |
|
|
|
```python |
|
value = value.lower() |
|
value = re.sub(r'[\r\n]+', ' ', value) |
|
value = re.sub(r'[^\x00-\x7F]+', ' ', value) |
|
|
|
tokenized = TreebankWordTokenizer().tokenize(value) |
|
sentence = ' '.join(tokenized) |
|
sentence = re.sub(r"\s's\b", "'s", sentence) |
|
``` |
|
|
|
### BibTeX entry and citation info |
|
|
|
```bibtex |
|
@InProceedings{peng2019transfer, |
|
author = {Yifan Peng and Shankai Yan and Zhiyong Lu}, |
|
title = {Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets}, |
|
booktitle = {Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019)}, |
|
year = {2019}, |
|
pages = {58--65}, |
|
} |
|
``` |
|
|
|
### Acknowledgments |
|
|
|
This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of |
|
Medicine and Clinical Center. This work was supported by the National Library of Medicine of the National Institutes of Health under award number 4R00LM013001-01. |
|
|
|
We are also grateful to the authors of BERT and ELMo to make the data and codes publicly available. |
|
|
|
We would like to thank Dr Sun Kim for processing the PubMed texts. |
|
|
|
### Disclaimer |
|
|
|
This tool shows the results of research conducted in the Computational Biology Branch, NCBI. The information produced |
|
on this website is not intended for direct diagnostic use or medical decision-making without review and oversight |
|
by a clinical professional. Individuals should not change their health behavior solely on the basis of information |
|
produced on this website. NIH does not independently verify the validity or utility of the information produced |
|
by this tool. If you have questions about the information produced on this website, please see a health care |
|
professional. More information about NCBI's disclaimer policy is available. |
|
|
