From 638c0b7c5085b162b656d4b8289c6c32a9949a51 Mon Sep 17 00:00:00 2001
From: Yifan Peng <pengyifan.mail@gmail.com>
Date: Thu, 5 Nov 2020 03:03:19 -0500
Subject: [PATCH] Create README.md (#8223)

* Create README.md

* Update README.md

* Apply suggestions from code review

Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
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 create mode 100644 model_cards/bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12/README.md

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+---
+language: 
+- en
+tags:
+- bluebert
+license: 
+- PUBLIC DOMAIN NOTICE
+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-base-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},
+}
+```