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79 lines
3.2 KiB
Plaintext
79 lines
3.2 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY 101 > PIPELINES
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p
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| When you call #[code nlp] on a text, spaCy first tokenizes the text to
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| produce a #[code Doc] object. The #[code Doc] is then processed in several
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| different steps – this is also referred to as the
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| #[strong processing pipeline]. The pipeline used by the
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| #[+a("/docs/usage/models") default models] consists of a
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| tensorizer, a tagger, a parser and an entity recognizer. Each pipeline
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| component returns the processed #[code Doc], which is then passed on to
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| the next component.
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+image
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include ../../../assets/img/docs/pipeline.svg
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.u-text-right
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+button("/assets/img/docs/pipeline.svg", false, "secondary").u-text-tag View large graphic
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+aside
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| #[strong Name:] ID of the pipeline component.#[br]
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| #[strong Component:] spaCy's implementation of the component.#[br]
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| #[strong Creates:] Objects, attributes and properties modified and set by
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| the component.
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+table(["Name", "Component", "Creates", "Description"])
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+row
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+cell tokenizer
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+cell #[+api("tokenizer") #[code Tokenizer]]
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+cell #[code Doc]
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+cell Segment text into tokens.
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+row("divider")
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+cell tensorizer
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+cell #[code TokenVectorEncoder]
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+cell #[code Doc.tensor]
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+cell Create feature representation tensor for #[code Doc].
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+row
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+cell tagger
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+cell #[+api("tagger") #[code Tagger]]
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+cell #[code Doc[i].tag]
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+cell Assign part-of-speech tags.
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+row
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+cell parser
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+cell #[+api("dependencyparser") #[code DependencyParser]]
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+cell
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| #[code Doc[i].head], #[code Doc[i].dep], #[code Doc.sents],
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| #[code Doc.noun_chunks]
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+cell Assign dependency labels.
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+row
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+cell ner
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+cell #[+api("entityrecognizer") #[code EntityRecognizer]]
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+cell #[code Doc.ents], #[code Doc[i].ent_iob], #[code Doc[i].ent_type]
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+cell Detect and label named entities.
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p
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| The processing pipeline always #[strong depends on the statistical model]
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| and its capabilities. For example, a pipeline can only include an entity
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| recognizer component if the model includes data to make predictions of
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| entity labels. This is why each model will specify the pipeline to use
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| in its meta data, as a simple list containing the component names:
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+code(false, "json").
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"pipeline": ["tensorizer", "tagger", "parser", "ner"]
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p
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| Although you can mix and match pipeline components, their
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| #[strong order and combination] is usually important. Some components may
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| require certain modifications on the #[code Doc] to process it. For
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| example, the default pipeline first applies the tensorizer, which
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| pre-processes the doc and encodes its internal
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| #[strong meaning representations] as an array of floats, also called a
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| #[strong tensor]. This includes the tokens and their context, which is
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| required for the next component, the tagger, to make predictions of the
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| part-of-speech tags. Because spaCy's models are neural network models,
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| they only "speak" tensors and expect the input #[code Doc] to have
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| a #[code tensor].
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