[('tagger', <spacy.pipeline.pipes.Tagger at 0x7fbd766f84c0>),
('parser', <spacy.pipeline.pipes.DependencyParser at 0x7fbd813184c0>),
('ner', <spacy.pipeline.pipes.EntityRecognizer at 0x7fbd81318400>)]
默认的流水线由词性标注器(
tagger
)、解析器(
parser
)和命名实体识别器(
ner
)
组成,所有这些都需要根据特定的语言选择。这里没有明确列出分词器,是因为分
词器在任何情况下都是必需的。
spaCy
的分词器非常快,但是所有其他步骤都基于神经模型,并且需要花费大量时间。
但是与其他库相比,
spaCy
的模型是最快的。处理整个流水线所需的时间仅是分词
的
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