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我目前正在从fr_core_news_lg管道更新 NER 模型。当我最后一次使用它时，该代码大约在 1 或 2 个月前工作。但是现在，发生了一些事情，我不能再运行它了。我没有对代码进行任何更改，只是想再次运行它。但我收到以下错误：

Traceback (most recent call last):
File "../nermodel.py", line 174, in <module>
ner_model.train(med_label)
File "../nermodel.py", line 102, in train
optimizer = self.nlp.entity.create_optimizer()
AttributeError: 'French' object has no attribute 'entity'

错误指向我用新示例更新我的 NER 模型的代码部分：

def train(self, label, n_iter=10, batch_size=50):
    # creating an optimizer and selecting a list of pipes NOT to train
    optimizer = self.nlp.entity.create_optimizer()
    other_pipes = [pipe for pipe in self.nlp.pipe_names if pipe != 'ner']

    # adding a named entity label
    ner = self.nlp.get_pipe('ner')
    ner.add_label(label)

    with self.nlp.disable_pipes(*other_pipes):
        for itn in range(n_iter):
            random.shuffle(self.train_data)
            losses = {}

            # batch the examples and iterate over them
            for batch in spacy.util.minibatch(self.train_data, size=batch_size):
                texts = [text for text, entities in batch]
                annotations = [entities for text, entities in batch]

                # update the model
                self.nlp.update(texts, annotations, sgd=optimizer, losses=losses)
                print(losses)
    print("Final loss: ", losses)

单个训练示例，以便 NER 了解“咨询”是一个实体，如下所示：

('et la consultation post-réanimation', {'entities': [(6, 18, 'MEDICAL_TERM')]})

我已将 SpaCy 更新到最新版本，并再次下载了fr_core_news_lg模型，甚至在新的 python 环境中尝试过，但无济于事。这让我觉得管道或 SpaCy 库发生了变化。谷歌搜索，我无法找到确切的答案。有人可以解决这个问题吗？

编辑：提供了更多细节。

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1 回答 1

1

我认为这段代码应该适合你：

def train(self, label, n_iter=10, batch_size=50):
    # creating an optimizer and selecting a list of pipes NOT to train
    optimizer = self.nlp.create_optimizer()
    other_pipes = [pipe for pipe in self.nlp.pipe_names if pipe != 'ner']

    # adding a named entity label
    ner = self.nlp.get_pipe('ner')
    ner.add_label(label)

    with self.nlp.disable_pipes(*other_pipes):
        for itn in range(n_iter):
            random.shuffle(self.train_data)
            losses = {}

            # batch the examples and iterate over them
            for batch in spacy.util.minibatch(self.train_data, size=batch_size):
                for text, annotations in batch:
                    doc = nlp.make_doc(text)
                    example = Example.from_dict(doc, annotations)
                    nlp.update([example], drop=0.35, sgd=optimizer, losses=losses)
                print(losses)
    print("Final loss: ", losses)

为了进一步分解它，在 spacy 3 中有两个变化：

  1. 他们摆脱了实体nlp.entity.create_optimizer()
  2. 我们不直接将文本和注释传递给nlp.update()但与Example
于 2021-05-16T03:08:45.967 回答