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model_cards/bionlp/bluebert_pubmed_mimic_uncased_L-24_H-1024_A-16/README.md
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--- | ||
language: | ||
- en | ||
tags: | ||
- bert | ||
- bluebert | ||
license: | ||
- PUBLIC DOMAIN NOTICE | ||
datasets: | ||
- PubMed | ||
- MIMIC-III | ||
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--- | ||
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# BlueBert-Base, Uncased, PubMed and MIMIC-III | ||
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## Model description | ||
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A BERT model pre-trained on PubMed abstracts and clinical notes ([MIMIC-III](https://mimic.physionet.org/)). | ||
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## Intended uses & limitations | ||
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#### How to use | ||
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Please see https://github.com/ncbi-nlp/bluebert | ||
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## Training data | ||
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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/). | ||
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Pre-trained model: https://huggingface.co/bert-large-uncased | ||
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## Training procedure | ||
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* 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) | ||
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Below is a code snippet for more details. | ||
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```python | ||
value = value.lower() | ||
value = re.sub(r'[\r\n]+', ' ', value) | ||
value = re.sub(r'[^\x00-\x7F]+', ' ', value) | ||
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tokenized = TreebankWordTokenizer().tokenize(value) | ||
sentence = ' '.join(tokenized) | ||
sentence = re.sub(r"\s's\b", "'s", sentence) | ||
``` | ||
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### BibTeX entry and citation info | ||
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```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}, | ||
} | ||
``` | ||
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### Acknowledgments | ||
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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. | ||
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We are also grateful to the authors of BERT and ELMo to make the data and codes publicly available. | ||
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We would like to thank Dr Sun Kim for processing the PubMed texts. | ||
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### Disclaimer | ||
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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. |