AutoTokenizer.from_pretrained 无法加载本地保存的预训练分词器 (PyTorch)

我是 PyTorch 的新手，最近我一直在尝试使用 Transformers。我正在使用 HuggingFace 提供的预训练分词器。
我成功下载并运行它们。但如果我尝试保存它们并再次加载，则会发生一些错误。
如果我使用AutoTokenizer.from_pretrained下载分词器，然后它就可以工作了。

[1]:    tokenizer = AutoTokenizer.from_pretrained('distilroberta-base')
        text = "Hello there"
        enc = tokenizer.encode_plus(text)
        enc.keys()

Out[1]: dict_keys(['input_ids', 'attention_mask'])

但如果我使用保存它tokenizer.save_pretrained("distilroberta-tokenizer")并尝试在本地加载它，然后失败。

[2]:    tmp = AutoTokenizer.from_pretrained('distilroberta-tokenizer')


---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/transformers/configuration_utils.py in get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    238                 resume_download=resume_download,
--> 239                 local_files_only=local_files_only,
    240             )

/opt/conda/lib/python3.7/site-packages/transformers/file_utils.py in cached_path(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, local_files_only)
    266         # File, but it doesn't exist.
--> 267         raise EnvironmentError("file {} not found".format(url_or_filename))
    268     else:

OSError: file distilroberta-tokenizer/config.json not found

During handling of the above exception, another exception occurred:

OSError                                   Traceback (most recent call last)
<ipython-input-25-3bd2f7a79271> in <module>
----> 1 tmp = AutoTokenizer.from_pretrained("distilroberta-tokenizer")

/opt/conda/lib/python3.7/site-packages/transformers/tokenization_auto.py in from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs)
    193         config = kwargs.pop("config", None)
    194         if not isinstance(config, PretrainedConfig):
--> 195             config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
    196 
    197         if "bert-base-japanese" in pretrained_model_name_or_path:

/opt/conda/lib/python3.7/site-packages/transformers/configuration_auto.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    194 
    195         """
--> 196         config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
    197 
    198         if "model_type" in config_dict:

/opt/conda/lib/python3.7/site-packages/transformers/configuration_utils.py in get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    250                 f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n"
    251             )
--> 252             raise EnvironmentError(msg)
    253 
    254         except json.JSONDecodeError:

OSError: Can't load config for 'distilroberta-tokenizer'. Make sure that:

- 'distilroberta-tokenizer' is a correct model identifier listed on 'https://huggingface.co/models'

- or 'distilroberta-tokenizer' is the correct path to a directory containing a config.json file

目录中缺少“config.json”。在检查目录时，我得到这些文件的列表：

[3]:    !ls distilroberta-tokenizer

Out[3]: merges.txt  special_tokens_map.json  tokenizer_config.json  vocab.json

我知道这个问题之前已经发布过，但似乎都不起作用。我也尝试遵循docs https://huggingface.co/transformers/model_doc/auto.html#autotokenizer但仍然无法使其发挥作用。
任何帮助，将不胜感激。

目前有一个issue https://github.com/huggingface/transformers/issues/4197正在调查中，它仅影响 AutoTokenizers，但不会影响底层标记器，例如（RobertaTokenizer）。例如，以下内容应该有效：

from transformers import RobertaTokenizer

tokenizer = RobertaTokenizer.from_pretrained('YOURPATH')

要使用 AutoTokenizer，您还需要保存配置以离线加载它：

from transformers import AutoTokenizer, AutoConfig

tokenizer = AutoTokenizer.from_pretrained('distilroberta-base')
config = AutoConfig.from_pretrained('distilroberta-base')

tokenizer.save_pretrained('YOURPATH')
config.save_pretrained('YOURPATH')

tokenizer = AutoTokenizer.from_pretrained('YOURPATH')

我建议either对分词器和模型使用不同的路径or保留模型的 config.json，因为您应用于模型的一些修改将存储在 config.json 中，该修改是在model.save_pretrained()当您按照上述方式在模型后保存标记生成器时，该文件将被覆盖（即您将无法使用标记生成器 config.json 加载修改后的模型）。