BlueBert-Base, Uncased, PubMed and MIMIC-III

Model description

A BERT model pre-trained on PubMed abstracts and clinical notes (MIMIC-III).

Intended uses & limitations

How to use

Please see https://github.com/ncbi-nlp/bluebert

Training data

We provide preprocessed PubMed texts that were used to pre-train the BlueBERT models. The corpus contains ~4000M words extracted from the PubMed ASCII code version.

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

Below is a code snippet for more details.

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

@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

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