将tagger添加到空白英文空间管道

Question

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")加载它。处理示例文本的工作方式与此类似

现在我想从blank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，假设我只希望管道中的spacy默认值tagger。这个documentation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.")

应该有用。但是，我得到了与hashembed相关的错误：

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()`?

暗示我应该运行initialize()。好啊如果我然后运行nlp.initialize()，我最终会得到这个错误

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？我从哪里取？为什么默认的模型配置不能解决这个问题？我必须告诉spacy以某种方式使用en_core_web_sm吗？如果是这样的话，我如何在不使用spacy.load("en_core_web_sm")和排除一大堆东西的情况下做到这一点？谢谢你的提示

编辑：理想情况下，我希望能够从修改过的配置文件中只加载管道的一部分，如nlp = English.from_config(config)。我甚至不能使用en_core_web_sm附带的配置文件，因为生成的管道也需要初始化，并且在nlp.initialize()时，我现在收到

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.

暗示它找不到所需的查找表

Answer 1

nlp.add_pipe("tagger")添加新的空白/未初始化标记器，而不是来自en_core_web_sm或任何其他预训练管道的标记器。如果以这种方式添加标记器，则需要在使用它之前对其进行初始化和训练

您可以使用source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，来自spacy.blank("en")的标记化可能与源管道中的标记器所接受的培训不同。通常（尤其是当您离开spacy的预训练管道时），您还应该确保标记器设置相同， 在排除组件的同时加载是一种简单的方法

或者，除了对scispacy的en_core_sci_sm等模型使用nlp.add_pipe(source=)之外，您还可以复制标记器设置，这是一个很好的管道示例。标记化与spacy.blank("en")不同：

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)