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我想在互联网访问受限的企业环境中使用 spacy-transformers，所以我必须手动从 huggingfaces 中心下载变压器模型，并让它们在 spacy 中工作。

在此示例中，我尝试使用 en_core_web_trf 预训练模型中的转换器管道组件：

import spacy
import spacy_transformers

nlp_trf = spacy.load("en_core_web_trf") # load roberta pretrained model
transformer= nlp_trf.get_pipe("transformer") # get transformer pipeline component
transformer.to_disk("transfomer_pretrained") # save pipeline component to disk

nlp = spacy.blank("en") 
trf = nlp.add_pipe("transformer")
trf.from_disk("transformer_pretrained", exclude=["vocab"]) # load transformer pipeline component from disk

我收到以下错误消息：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-23-c66c45181d83> in <module>
      1 #trf.model.initialize([nlp.make_doc("hello world")])
----> 2 trf.from_disk("models/transformer_pretrained", exclude=["vocab"])
      3 nlp.pipe_names

C:\_Development\Python37\site-packages\spacy_transformers\pipeline_component.py in from_disk(self, path, exclude)
    400             "model": load_model,
    401         }
--> 402         util.from_disk(path, deserialize, exclude)
    403         return self

C:\_Development\Python37\site-packages\spacy\util.py in from_disk(path, readers, exclude)
   1172         # Split to support file names like meta.json
   1173         if key.split(".")[0] not in exclude:
-> 1174             reader(path / key)
   1175     return path
   1176 

C:\_Development\Python37\site-packages\spacy_transformers\pipeline_component.py in load_model(p)
    390             p = Path(p).absolute()
    391             tokenizer, transformer = huggingface_from_pretrained(
--> 392                 p, self.model.attrs["tokenizer_config"]
    393             )
    394             self.model.attrs["tokenizer"] = tokenizer

C:\_Development\Python37\site-packages\spacy_transformers\util.py in huggingface_from_pretrained(source, config)
     29     else:
     30         str_path = source
---> 31     tokenizer = AutoTokenizer.from_pretrained(str_path, **config)
     32     transformer = AutoModel.from_pretrained(str_path)
     33     ops = get_current_ops()

C:\_Development\Python37\site-packages\transformers\models\auto\tokenization_auto.py in from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs)
    388         kwargs["_from_auto"] = True
    389         if not isinstance(config, PretrainedConfig):
--> 390             config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
    391 
    392         use_fast = kwargs.pop("use_fast", True)

C:\_Development\Python37\site-packages\transformers\models\auto\configuration_auto.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    396         """
    397         kwargs["_from_auto"] = True
--> 398         config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
    399         if "model_type" in config_dict:
    400             config_class = CONFIG_MAPPING[config_dict["model_type"]]

C:\_Development\Python37\site-packages\transformers\configuration_utils.py in get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    464                 local_files_only=local_files_only,
    465                 use_auth_token=use_auth_token,
--> 466                 user_agent=user_agent,
    467             )
    468             # Load config dict

C:\_Development\Python37\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, use_auth_token, local_files_only)
   1171             user_agent=user_agent,
   1172             use_auth_token=use_auth_token,
-> 1173             local_files_only=local_files_only,
   1174         )
   1175     elif os.path.exists(url_or_filename):

C:\_Development\Python37\site-packages\transformers\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)
   1387                 else:
   1388                     raise ValueError(
-> 1389                         "Connection error, and we cannot find the requested files in the cached path."
   1390                         " Please try again or make sure your Internet connection is on."
   1391                     )

ValueError: Connection error, and we cannot find the requested files in the cached path. Please try again or make sure your Internet connection is on.

如错误消息所述，在缓存路径中找不到请求的文件。有人可以向我解释我必须将哪些文件放在 chache 路径中吗？或者另一种方式来预下载模型并在 spacy 中使用它们。

版本：

空间 3.0.5

spacy-transformers 1.0.2

变压器 4.5.1

4

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