spaCy/website/docs/usage/101/_pipelines.md

When you call nlp on a text, spaCy first tokenizes the text to produce a Doc object. The Doc is then processed in several different steps – this is also referred to as the processing pipeline. The pipeline used by the default models consists of a tagger, a parser and an entity recognizer. Each pipeline component returns the processed Doc, which is then passed on to the next component.

• Name: ID of the pipeline component.
• Component: spaCy's implementation of the component.
• Creates: Objects, attributes and properties modified and set by the component.

Name	Component	Creates	Description
tokenizer	Tokenizer	Doc	Segment text into tokens.
tagger	Tagger	Doc[i].tag	Assign part-of-speech tags.
parser	DependencyParser	Doc[i].head, Doc[i].dep, Doc.sents, Doc.noun_chunks	Assign dependency labels.
ner	EntityRecognizer	Doc.ents, Doc[i].ent_iob, Doc[i].ent_type	Detect and label named entities.
textcat	TextCategorizer	Doc.cats	Assign document labels.
...	custom components	Doc._.xxx, Token._.xxx, Span._.xxx	Assign custom attributes, methods or properties.

The processing pipeline always depends on the statistical model and its capabilities. For example, a pipeline can only include an entity recognizer component if the model includes data to make predictions of entity labels. This is why each model will specify the pipeline to use in its meta data, as a simple list containing the component names:

"pipeline": ["tagger", "parser", "ner"]

import Accordion from 'components/accordion.js'

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