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	Token.tag	Assign part-of-speech tags.
parser	DependencyParser	Token.head, Token.dep, Doc.sents, Doc.noun_chunks	Assign dependency labels.
ner	EntityRecognizer	Doc.ents, Token.ent_iob, Token.ent_type	Detect and label named entities.
lemmatizer	Lemmatizer	Token.lemma	Assign base forms.
textcat	TextCategorizer	Doc.cats	Assign document labels.
custom	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 and config, as a simple list containing the component names:

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

import Accordion from 'components/accordion.js'

The statistical components like the tagger or parser are typically independent and don't share any data between each other. For example, the named entity recognizer doesn't use any features set by the tagger and parser, and so on. This means that you can swap them, or remove single components from the pipeline without affecting the others. However, components may share a "token-to-vector" component like Tok2Vec or Transformer.

Custom components may also depend on annotations set by other components. For example, a custom lemmatizer may need the part-of-speech tags assigned, so it'll only work if it's added after the tagger. The parser will respect pre-defined sentence boundaries, so if a previous component in the pipeline sets them, its dependency predictions may be different. Similarly, it matters if you add the EntityRuler before or after the statistical entity recognizer: if it's added before, the entity recognizer will take the existing entities into account when making predictions. The EntityLinker, which resolves named entities to knowledge base IDs, should be preceded by a pipeline component that recognizes entities such as the EntityRecognizer.

The tokenizer is a "special" component and isn't part of the regular pipeline. It also doesn't show up in nlp.pipe_names. The reason is that there can only really be one tokenizer, and while all other pipeline components take a Doc and return it, the tokenizer takes a string of text and turns it into a Doc. You can still customize the tokenizer, though. nlp.tokenizer is writable, so you can either create your own Tokenizer class from scratch, or even replace it with an entirely custom function.

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