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2020.11-BERT预训练模型下载地址记录_训练好的bert参数哪里下载

训练好的bert参数哪里下载

Reference:https://github.com/google-research/bert

目录

模型结构组成(Pytorch)

pytorch_model.bin

bert_config.json

vocab.txt

下载地址汇总

补充内容


模型结构组成(Pytorch

pytorch_model.bin

bert_config.json

vocab.txt

 

下载地址汇总

  1. ​PRETRAINED_MODEL_ARCHIVE_MAP = {
  2. 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
  3. 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
  4. 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
  5. 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
  6. 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
  7. 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
  8. 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
  9. }
  1. PRETRAINED_VOCAB_ARCHIVE_MAP = {
  2. 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
  3. 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
  4. 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
  5. 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
  6. 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
  7. 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
  8. 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
  9. }

 

补充内容

https://www.jianshu.com/p/3d0bb34c488a 中提到:

1)base和large的区别是在参数规模上,如果显卡显存有限只能选择base版本;

2)cased和uncased分别代表大小写敏感和大小写不敏感,在不同任务中个人感觉都可以试一试。

 

 

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