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Adriane Boyd 2021-03-17 12:59:05 +01:00 committed by GitHub
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@ -63,7 +63,7 @@ it's easy to swap out or disable the lemmatizer. The pipelines are designed to
be efficient in terms of speed and size and work well when the pipeline is run be efficient in terms of speed and size and work well when the pipeline is run
in full. in full.
When modifying a pretrained v3 pipeline, it's important to understand how the When modifying a pretrained pipeline, it's important to understand how the
components **depend on** each other. Unlike spaCy v2, where the `tagger`, components **depend on** each other. Unlike spaCy v2, where the `tagger`,
`parser` and `ner` components were all independent, some v3 components depend on `parser` and `ner` components were all independent, some v3 components depend on
earlier components in the pipeline. As a result, disabling or reordering earlier components in the pipeline. As a result, disabling or reordering
@ -175,8 +175,8 @@ disable everything else:
nlp = spacy.load("en_core_web_sm", disable=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"]) nlp = spacy.load("en_core_web_sm", disable=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"])
``` ```
In the transformer models, `ner` listens to the `transformer` layer, so you can In the transformer models, `ner` listens to the `transformer` compoinent, so you
disable all components related tagging, parsing, and lemmatization. can disable all components related tagging, parsing, and lemmatization.
```python ```python
nlp = spacy.load("en_core_web_trf", disable=["tagger", "parser", "attribute_ruler", "lemmatizer"]) nlp = spacy.load("en_core_web_trf", disable=["tagger", "parser", "attribute_ruler", "lemmatizer"])