huggingface-transformers - 使用 Huggingface Transformers 从磁盘加载预训练模型

Question

来自文档 for from_pretrained ，我知道我不必每次都下载预训练的向量，我可以使用以下语法保存它们并从磁盘加载:

  - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
  - (not applicable to all derived classes, deprecated) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.

所以，我去了模型中心:

https://huggingface.co/models

我找到了我想要的模型:

https://huggingface.co/bert-base-cased

我从他们提供给这个存储库的链接下载了它:

Pretrained model on English language using a masked language modeling(MLM) objective. It was introduced in this paper and first released inthis repository. This model is case-sensitive: it makes a differencebetween english and English.

存储在:

  /my/local/models/cased_L-12_H-768_A-12/

其中包含:

 ./
 ../
 bert_config.json
 bert_model.ckpt.data-00000-of-00001
 bert_model.ckpt.index
 bert_model.ckpt.meta
 vocab.txt

所以，现在我有以下几点:

  PATH = '/my/local/models/cased_L-12_H-768_A-12/'
  tokenizer = BertTokenizer.from_pretrained(PATH, local_files_only=True)

我得到这个错误:

>           raise EnvironmentError(msg)
E           OSError: Can't load config for '/my/local/models/cased_L-12_H-768_A-12/'. Make sure that:
E           
E           - '/my/local/models/cased_L-12_H-768_A-12/' is a correct model identifier listed on 'https://huggingface.co/models'
E           
E           - or '/my/local/models/cased_L-12_H-768_A-12/' is the correct path to a directory containing a config.json file

同样，当我直接链接到 config.json 时:

  PATH = '/my/local/models/cased_L-12_H-768_A-12/bert_config.json'
  tokenizer = BertTokenizer.from_pretrained(PATH, local_files_only=True)

        if state_dict is None and not from_tf:
            try:
                state_dict = torch.load(resolved_archive_file, map_location="cpu")
            except Exception:
                raise OSError(
>                   "Unable to load weights from pytorch checkpoint file. "
                    "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. "
                )
E               OSError: Unable to load weights from pytorch checkpoint file. If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True.

我应该怎么做才能让 Huggingface 使用我的本地预训练模型？
更新以解决评论

YOURPATH = '/somewhere/on/disk/'

name = 'transfo-xl-wt103'
tokenizer = TransfoXLTokenizerFast(name)
model = TransfoXLModel.from_pretrained(name)
tokenizer.save_pretrained(YOURPATH)
model.save_pretrained(YOURPATH)

>>> Please note you will not be able to load the save vocabulary in Rust-based TransfoXLTokenizerFast as they don't share the same structure.
('/somewhere/on/disk/vocab.bin', '/somewhere/on/disk/special_tokens_map.json', '/somewhere/on/disk/added_tokens.json')

所以一切都得救了，但是……

YOURPATH = '/somewhere/on/disk/'
TransfoXLTokenizerFast.from_pretrained('transfo-xl-wt103', cache_dir=YOURPATH, local_files_only=True)

    "Cannot find the requested files in the cached path and outgoing traffic has been"
ValueError: Cannot find the requested files in the cached path and outgoing traffic has been disabled. To enable model look-ups and downloads online, set 'local_files_only' to False.

Answer 1

该文件相对于您的模型文件夹位于何处？我相信它必须是一个相对路径而不是绝对路径。因此，如果您编写代码的文件位于 'my/local/' ，那么你的代码应该是这样的:

PATH = 'models/cased_L-12_H-768_A-12/'
tokenizer = BertTokenizer.from_pretrained(PATH, local_files_only=True)

您只需要指定所有文件所在的文件夹，而不是直接指定文件。我认为这绝对是 PATH 的问题。 .尝试改变“斜线”的样式:“/”与“\”，这些在不同的操作系统中是不同的。也可以尝试使用“.”，就像这样 ./models/cased_L-12_H-768_A-12/等等。