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我很难弄清楚如何从 spacy V3 中的内置模型一点一点地组装 spacy 管道。我已经下载了en_core_web_sm模型，可以用nlp = spacy.load("en_core_web_sm"). 示例文本的处理就像这样工作得很好。

现在我想要的是从空白构建一个英语管道并一点一点地添加组件。我不想加载整个en_core_web_sm管道并排除组件。为了具体起见，假设我只想要tagger管道中的 spacy 默认值。文档向我建议

import spacy

from spacy.pipeline.tagger import DEFAULT_TAGGER_MODEL
config = {"model": DEFAULT_TAGGER_MODEL}

nlp = spacy.blank("en")
nlp.add_pipe("tagger", config=config)
nlp("This is some sample text.")

应该管用。但是我收到与以下相关的错误hashembed：

Traceback (most recent call last):
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/spacy/language.py", line 1000, in __call__
    doc = proc(doc, **component_cfg.get(name, {}))
  File "spacy/pipeline/trainable_pipe.pyx", line 56, in spacy.pipeline.trainable_pipe.TrainablePipe.__call__
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/spacy/util.py", line 1507, in raise_error
    raise e
  File "spacy/pipeline/trainable_pipe.pyx", line 52, in spacy.pipeline.trainable_pipe.TrainablePipe.__call__
  File "spacy/pipeline/tagger.pyx", line 111, in spacy.pipeline.tagger.Tagger.predict
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/model.py", line 315, in predict
    return self._func(self, X, is_train=False)[0]
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/chain.py", line 54, in forward
    Y, inc_layer_grad = layer(X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/model.py", line 291, in __call__
    return self._func(self, X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/chain.py", line 54, in forward
    Y, inc_layer_grad = layer(X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/model.py", line 291, in __call__
    return self._func(self, X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/chain.py", line 54, in forward
    Y, inc_layer_grad = layer(X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/model.py", line 291, in __call__
    return self._func(self, X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/with_array.py", line 30, in forward
    return _ragged_forward(
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/with_array.py", line 90, in _ragged_forward
    Y, get_dX = layer(Xr.dataXd, is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/model.py", line 291, in __call__
    return self._func(self, X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/concatenate.py", line 44, in forward
    Ys, callbacks = zip(*[layer(X, is_train=is_train) for layer in model.layers])
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/concatenate.py", line 44, in <listcomp>
    Ys, callbacks = zip(*[layer(X, is_train=is_train) for layer in model.layers])
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/model.py", line 291, in __call__
    return self._func(self, X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/chain.py", line 54, in forward
    Y, inc_layer_grad = layer(X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/model.py", line 291, in __call__
    return self._func(self, X, is_train=is_train)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/layers/hashembed.py", line 61, in forward
    vectors = cast(Floats2d, model.get_param("E"))
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/model.py", line 216, in get_param
    raise KeyError(
KeyError: "Parameter 'E' for model 'hashembed' has not been allocated yet."


The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3437, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-2-8e2b4cf9fd33>", line 8, in <module>
    nlp("This is some sample text.")
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/spacy/language.py", line 1003, in __call__
    raise ValueError(Errors.E109.format(name=name)) from e
ValueError: [E109] Component 'tagger' could not be run. Did you forget to call `initialize()`?

暗示我应该跑initialize()。好的。如果我然后运行，nlp.initialize()我最终会收到此错误

Traceback (most recent call last):
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3437, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-3-eeec225a68df>", line 1, in <module>
    nlp.initialize()
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/spacy/language.py", line 1273, in initialize
    proc.initialize(get_examples, nlp=self, **p_settings)
  File "spacy/pipeline/tagger.pyx", line 271, in spacy.pipeline.tagger.Tagger.initialize
  File "spacy/pipeline/pipe.pyx", line 104, in spacy.pipeline.pipe.Pipe._require_labels
ValueError: [E143] Labels for component 'tagger' not initialized. This can be fixed by calling add_label, or by providing a representative batch of examples to the component's `initialize` method.

现在我有点不知所措。哪些标签示例？我从哪里拿走它们？为什么默认模型配置不解决这个问题？我必须告诉 spacy 以en_core_web_sm某种方式使用吗？如果是这样，我怎么能这样做而不使用spacy.load("en_core_web_sm")和排除一大堆东西？感谢您的提示！

编辑：理想情况下，我希望能够从修改后的配置文件中仅加载管道的一部分，例如nlp = English.from_config(config). 我什至不能使用附带的配置文件，en_core_web_sm因为生成的管道也需要初始化，nlp.initialize()现在我收到了

Traceback (most recent call last):
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3437, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-67-eeec225a68df>", line 1, in <module>
    nlp.initialize()
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/spacy/language.py", line 1246, in initialize
    I = registry.resolve(config["initialize"], schema=ConfigSchemaInit)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/config.py", line 727, in resolve
    resolved, _ = cls._make(
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/config.py", line 776, in _make
    filled, _, resolved = cls._fill(
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/thinc/config.py", line 848, in _fill
    getter_result = getter(*args, **kwargs)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/spacy/language.py", line 98, in load_lookups_data
    lookups = load_lookups(lang=lang, tables=tables)
  File "/home/valentin/miniconda3/envs/eval/lib/python3.8/site-packages/spacy/lookups.py", line 30, in load_lookups
    raise ValueError(Errors.E955.format(table=", ".join(tables), lang=lang))
ValueError: [E955] Can't find table(s) lexeme_norm for language 'en' in spacy-lookups-data. Make sure you have the package installed or provide your own lookup tables if no default lookups are available for your language.

暗示它没有找到所需的查找表。

4

1 回答 1

2

nlp.add_pipe("tagger")添加一个新的空白/未初始化的标注器，而不是来自en_core_web_sm或任何其他预训练管道的标注器。如果以这种方式添加标记器，则需要先对其进行初始化和训练，然后才能使用它。

source您可以使用以下选项从现有管道添加组件：

nlp = spacy.add_pipe("tagger", source=spacy.load("en_core_web_sm"))

也就是说，标记化可能spacy.blank("en")与源管道中的标记器所训练的不同。通常（尤其是当您离开 spacy 的预训练管道后），您还应该确保标记器设置相同，并且在排除组件的同时加载是一种简单的方法。

或者，除了nlp.add_pipe(source=)用于 scispacy's 之类的模型之外，您还可以复制标记器设置en_core_sci_sm，这是标记化与以下不同的管道的一个很好的示例spacy.blank("en")：

nlp = spacy.blank("en")
source_nlp = spacy.load("en_core_sci_sm")
nlp.tokenizer.from_bytes(source_nlp.tokenizer.to_bytes())
nlp.add_pipe("tagger", source=source_nlp)
于 2021-08-02T12:04:35.957 回答