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83 lines
3.4 KiB
Plaintext
83 lines
3.4 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("/models") default models] consists of a tagger, a parser and an
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| entity recognizer. Each pipeline component returns the processed
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| #[code Doc], which is then passed on to the next component.
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+graphic("/assets/img/pipeline.svg")
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include ../../assets/img/pipeline.svg
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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 #[strong 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 #[strong 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 #[strong parser]
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+cell #[+api("dependencyparser") #[code DependencyParser]]
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+cell
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| #[code Doc[i].head],
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| #[code Doc[i].dep],
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| #[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 #[strong 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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+row
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+cell #[strong textcat]
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+cell #[+api("textcategorizer") #[code TextCategorizer]]
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+cell #[code Doc.cats]
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+cell Assign document labels.
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+row("divider")
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+cell #[strong ...]
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+cell #[+a("/usage/processing-pipelines#custom-components") custom components]
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+cell #[code Doc._.xxx], #[code Token._.xxx], #[code Span._.xxx]
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+cell Assign custom attributes, methods or properties.
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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": ["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. As the
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| processing pipeline is applied, spaCy encodes the document's 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 first 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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