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--- |
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language: |
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- en |
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tags: |
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- bluebert |
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license: cc0-1.0 |
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datasets: |
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- pubmed |
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--- |
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# BlueBert-Base, Uncased, PubMed |
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## Model description |
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A BERT model pre-trained on PubMed abstracts |
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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. |
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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-base-uncased |
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## Training procedure |
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* lowercasing the text |
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* removing speical chars `\x00`-`\x7F` |
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* 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 |
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value = value.lower() |
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value = re.sub(r'[\r\n]+', ' ', value) |
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value = re.sub(r'[^\x00-\x7F]+', ' ', value) |
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tokenized = TreebankWordTokenizer().tokenize(value) |
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sentence = ' '.join(tokenized) |
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sentence = re.sub(r"\s's\b", "'s", sentence) |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@InProceedings{peng2019transfer, |
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author = {Yifan Peng and Shankai Yan and Zhiyong Lu}, |
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title = {Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets}, |
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booktitle = {Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019)}, |
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year = {2019}, |
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pages = {58--65}, |
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} |
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``` |
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