如何使用 ktrain for NER Offline？

Question

我已经按照这个笔记本 (https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/tutorials/tutorial-06-sequence-tagging.ipynb) 训练了我的英语模型。我可以毫无问题地保存我的预训练模型和运行。

但是，我需要再次运行它，但是脱机并且它不起作用，我知道我需要下载文件并执行类似于此处执行的操作。

https://github.com/huggingface/transformers/issues/136

但是，我无法理解我需要在哪里更改 ktrain 的设置。

我运行这个：

ktrain.load_predictor('Functions/my_english_nermodel')

这是我得到的错误：

Traceback (most recent call last):
  File "Z:\Functions\NER.py", line 155, in load_bert
    reloaded_predictor= ktrain.load_predictor('Z:/Functions/my_english_nermodel')
  File "C:\Program Files\Python37\lib\site-packages\ktrain\core.py", line 1316, in load_predictor
    preproc = pickle.load(f)
  File "C:\Program Files\Python37\lib\site-packages\ktrain\text\ner\anago\preprocessing.py", line 76, in __setstate__
    if self.te_model is not None: self.activate_transformer(self.te_model, layers=self.te_layers)
  File "C:\Program Files\Python37\lib\site-packages\ktrain\text\ner\anago\preprocessing.py", line 100, in activate_transformer
    self.te = TransformerEmbedding(model_name, layers=layers)
  File "C:\Program Files\Python37\lib\site-packages\ktrain\text\preprocessor.py", line 1095, in __init__
    self.tokenizer = self.tokenizer_type.from_pretrained(model_name)
  File "C:\Program Files\Python37\lib\site-packages\transformers\tokenization_utils.py", line 903, in from_pretrained
    return cls._from_pretrained(*inputs, **kwargs)
  File "C:\Program Files\Python37\lib\site-packages\transformers\tokenization_utils.py", line 1008, in _from_pretrained
    list(cls.vocab_files_names.values()),
OSError: Model name 'bert-base-uncased' was not found in tokenizers model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased, bert-base-finnish-cased-v1, bert-base-finnish-uncased-v1, bert-base-dutch-cased). We assumed 'bert-base-dutch-cased' was a path, a model identifier, or url to a directory containing vocabulary files named ['vocab.txt'] but couldn't find such vocabulary files at this path or url.

Process finished with exit code 1

Answer 1

我找到了一个解决方案，当 ktrain 运行连接到互联网时，它会创建一个文件夹： ''' C:\Users\lemolina.cache\torch\transformers ''' 我需要在无法访问互联网的机器中复制相同的文件夹

Answer 2

更一般地说，基于 transformers 的预训练模型被下载到 <home_directory>/.cache/torch/transformers。例如，在 Linux 上，这将是 /home/<user_name>/.cache/torch/transformers。

如上面的答案所示，在无法访问互联网的机器上重新加载 ktrain predictor（对于使用来自模型的 ktrain 模型transformers 库），您需要将该文件夹中的模型文件复制到新机器上的相同位置。

如何使用 ktrain for NER Offline？

how to use ktrain for NER Offline?

python

tensorflow

offline

named-entity-recognition

bert-language-model