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biobert-pretrained

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

ModelsDomain-specific models
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创建于 2019-01-28 · 更新于 2026-09-16 · 今日第 14709 名
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jhyukleeDSKSD

naver/biobert-pretrained 排名第 14,709，有 710 个星标。

同类别比较

naver/biobert-pretrained 排名第 14,709，此处有 8 个同类条目；microsoft/BiomedParse 排名第 14,720。

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关键参数与规格
关键参数与规格
参数值
类别Models
子类别Domain-specific models
关键词Biomedical
星标数710
分叉数92
贡献者2
创建日期2019-01-28
更新日期2026-09-16
1 天星标数0
7 天星标数0
排名14,709
上次排名14,554
排名变化-155
7 天星标排名14,614
上次 7 天星标排名14,624
7 天星标排名变化10
总星标排名14,736
上次总星标排名14,711
总星标排名变化-25
国家South Korea
README 摘录

BioBERT Pre-trained Weights This repository provides pre-trained weights of BioBERT, a language representation model for biomedical domain, especially designed for biomedical text mining tasks such as biomedical named entity recognition, relation extraction, question answering, etc. Please refer to our paper BioBERT: a pre-trained biomedical language representation model for biomedical text mining for more details. Downloading pre-trained weights Go to releases section of this repository or click links below to download pre-trained weights of BioBERT. We

provide three combinations of pre-trained weights: BioBERT (+ PubMed), BioBERT (+ PMC), and BioBERT (+ PubMed + PMC). Pre-training was based on the original BERT code provided by Google, and training details are described in our paper. Currently available versions of pre-trained weights are as follows: **BioBERT-Base v1. 1 (+ PubMed 1M)** - based on BERT-base-Cased (same vocabulary) **BioBERT-Large v1. 1 (+ PubMed 1M)** - based on BERT-large-Cased (custom 30k vocabulary), NER/QA Results **BioBERT-Base v1. 0 (+ PubMed

200K)** - based on BERT-base-Cased (same vocabulary) **BioBERT-Base v1. 0 (+ PMC 270K)** - based on BERT-base-Cased (same vocabulary) **BioBERT-Base v1. 0 (+ PubMed 200K + PMC 270K)** - based on BERT-base-Cased (same vocabulary) Make sure to specify the versions of pre-trained weights used in your works. If you have difficulty choosing which one to use, we recommend using **BioBERT-Base v1. 1 (+ PubMed 1M)** or **BioBERT-Large v1. 1 (+ PubMed 1M)** depending on your GPU resources

. Note that for BioBERT-Base, we are using WordPiece vocabulary ( ) provided by Google as any new words in biomedical corpus can be represented with subwords (for instance, Leukemia => Leu + ##ke + ##mia). More details are in the closed issue #1. Pre-training corpus We do not provide pre-processed version of each corpus. However, each pre-training corpus could be found in the following links: ** **: ftp://ftp. gov/pubmed/updatefiles/ ** **: ftp://ftp. gov/pub/pmc/oa_bulk/ Estimated size of each

corpus is 4. 5 billion words for ** ** + ** **, and 13. Fine-tuning BioBERT To fine-tunine BioBERT on biomedical text mining tasks using provided pre-trained weights, refer to the DMIS GitHub repository for BioBERT. Citation Contact information For help or issues using pre-trained weights of BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee ( ), or Sungdong Kim ( ) for communication related to pre-trained weights of BioBERT.

常见问题

记录了多少星标和分叉？

naver/biobert-pretrained 有 710 个星标和 92 个分叉。

仓库排名是多少？

naver/biobert-pretrained 排名第 14,709。

哪个仓库排名最接近？

naver/biobert-pretrained 与 microsoft/BiomedParse 比较，后者排名第 14,720，有 709 个星标。

README

BioBERT Pre-trained Weights

This repository provides pre-trained weights of BioBERT, a language representation model for biomedical domain, especially designed for biomedical text mining tasks such as biomedical named entity recognition, relation extraction, question answering, etc. Please refer to our paper BioBERT: a pre-trained biomedical language representation model for biomedical text mining for more details.

Downloading pre-trained weights

Go to releases section of this repository or click links below to download pre-trained weights of BioBERT. We provide three combinations of pre-trained weights: BioBERT (+ PubMed), BioBERT (+ PMC), and BioBERT (+ PubMed + PMC). Pre-training was based on the original BERT code provided by Google, and training details are described in our paper. Currently available versions of pre-trained weights are as follows:

Make sure to specify the versions of pre-trained weights used in your works. If you have difficulty choosing which one to use, we recommend using BioBERT-Base v1.1 (+ PubMed 1M) or BioBERT-Large v1.1 (+ PubMed 1M) depending on your GPU resources. Note that for BioBERT-Base, we are using WordPiece vocabulary (vocab.txt) provided by Google as any new words in biomedical corpus can be represented with subwords (for instance, Leukemia => Leu + ##ke + ##mia). More details are in the closed issue #1.

Pre-training corpus

We do not provide pre-processed version of each corpus. However, each pre-training corpus could be found in the following links:

Estimated size of each corpus is 4.5 billion words for PubMed Abstracts1 + PubMed Abstracts2, and 13.5 billion words for PubMed Central Full Texts.

Fine-tuning BioBERT

To fine-tunine BioBERT on biomedical text mining tasks using provided pre-trained weights, refer to the DMIS GitHub repository for BioBERT.

Citation

@article{10.1093/bioinformatics/btz682,
    author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
    title = "{BioBERT: a pre-trained biomedical language representation model for biomedical text mining}",
    journal = {Bioinformatics},
    year = {2019},
    month = {09},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btz682},
    url = {https://doi.org/10.1093/bioinformatics/btz682},
}

Contact information

For help or issues using pre-trained weights of BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee (lee.jnhk@gmail.com), or Sungdong Kim (sungdong.kim@navercorp.com) for communication related to pre-trained weights of BioBERT.

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