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Update section on new v2.0 features
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@ -8,6 +8,65 @@ p
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+h(2, "features") New features
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+h(3, "features-pipelines") Improved processing pipelines
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+aside-code("Example").
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# Modify an existing pipeline
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nlp = spacy.load('en')
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nlp.pipeline.append(my_component)
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# Register a factory to create a component
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spacy.set_factory('my_factory', my_factory)
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nlp = Language(pipeline=['my_factory', mycomponent])
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p
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| It's now much easier to customise the pipeline with your own components.
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| Components are functions that receive a #[code Doc] object, modify and
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| return it. If your component is stateful, you'll want to create a new one
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| for each pipeline. You can do that by defining and registering a factory
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| which receives the shared #[code Vocab] object and returns a component.
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p
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| spaCy's default components – the vectorizer, tagger, parser and entity
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| recognizer, can be added to your pipeline by using their string IDs.
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| This way, you won't have to worry about finding and implementing them –
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| to use the default tagger, simply add #[code "tagger"] to the pipeline,
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| and spaCy will know what to do.
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+infobox
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| #[strong API:] #[+api("language") #[code Language]]
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| #[strong Usage:] #[+a("/docs/usage/language-processing-pipeline") Processing text]
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+h(3, "features-serializer") Saving, loading and serialization
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+aside-code("Example").
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nlp = spacy.load('en') # shortcut link
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nlp = spacy.load('en_core_web_sm') # package
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nlp = spacy.load('/path/to/en') # unicode path
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nlp = spacy.load(Path('/path/to/en')) # pathlib Path
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nlp.to_disk('/path/to/nlp')
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nlp = English().from_disk('/path/to/nlp')
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p
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| spay's serialization API has been made consistent across classes and
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| objects. All container classes and pipeline components now have a
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| #[code to_bytes()], #[code from_bytes()], #[code to_disk()] and
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| #[code from_disk()] method that supports the Pickle protocol.
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p
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| The improved #[code spacy.load] makes loading models easier and more
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| transparent. You can load a model by supplying its
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| #[+a("/docs/usage/models#usage") shortcut link], the name of an installed
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| #[+a("/docs/usage/saving-loading#generating") model package] or a path.
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| The #[code Language] class to initialise will be determined based on the
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| model's settings. For a blank language, you can import the class directly,
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| e.g. #[code from spacy.lang.en import English].
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+infobox
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| #[strong API:] #[+api("spacy#load") #[code spacy.load]], #[+api("binder") #[code Binder]]
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| #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
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+h(3, "features-displacy") displaCy visualizer with Jupyter support
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+aside-code("Example").
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@ -28,33 +87,6 @@ p
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| #[strong API:] #[+api("displacy") #[code displacy]]
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| #[strong Usage:] #[+a("/docs/usage/visualizers") Visualizing spaCy]
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+h(3, "features-loading") Loading
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+aside-code("Example").
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nlp = spacy.load('en') # shortcut link
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nlp = spacy.load('en_core_web_sm') # package
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nlp = spacy.load('/path/to/en') # unicode path
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nlp = spacy.load(Path('/path/to/en')) # pathlib Path
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p
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| The improved #[code spacy.load] makes loading models easier and more
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| transparent. You can load a model by supplying its
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| #[+a("/docs/usage/models#usage") shortcut link], the name of an installed
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| #[+a("/docs/usage/saving-loading#generating") model package], a unicode
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| path or a #[code Path]-like object. spaCy will try resolving the load
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| argument in this order. The #[code path] keyword argument is now deprecated.
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p
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| The #[code Language] class to initialise will be determined based on the
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| model's settings. If no model is found, spaCy will let you know and won't
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| just return an empty #[code Language] object anymore. If you want a blank
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| language, you can always import the class directly, e.g.
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| #[code from spacy.lang.en import English].
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+infobox
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| #[strong API:] #[+api("spacy#load") #[code spacy.load]]
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| #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
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+h(3, "features-language") Improved language data and lazy loading
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p
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@ -65,46 +97,15 @@ p
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| complex regular expressions. The language data has also been tidied up
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| and simplified. It's now also possible to overwrite the functions that
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| compute lexical attributes like #[code like_num], and supply
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| language-specific syntax iterators, e.g. to determine noun chunks.
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| language-specific syntax iterators, e.g. to determine noun chunks. spaCy
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| now also supports simple lookup-based lemmatization. The data is stored
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| in a dictionary mapping a string to its lemma.
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+infobox
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| #[strong API:] #[+api("language") #[code Language]]
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| #[strong Code:] #[+src(gh("spaCy", "spacy/lang")) spacy/lang]
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| #[strong Usage:] #[+a("/docs/usage/adding-languages") Adding languages]
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+h(3, "features-pipelines") Improved processing pipelines
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+aside-code("Example").
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from spacy.language import Language
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nlp = Language(pipeline=['token_vectors', 'tags',
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'dependencies'])
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+infobox
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| #[strong API:] #[+api("language") #[code Language]]
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| #[strong Usage:] #[+a("/docs/usage/processing-text") Processing text]
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+h(3, "features-lemmatizer") Simple lookup-based lemmatization
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+aside-code("Example").
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LOOKUP = {
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"aba": "abar",
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"ababa": "abar",
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"ababais": "abar",
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"ababan": "abar",
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"ababanes": "ababán"
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}
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p
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| spaCy now supports simple lookup-based lemmatization. The data is stored
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| in a dictionary mapping a string to its lemma. To determine a token's
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| lemma, spaCy simply looks it up in the table. The lookup lemmatizer can
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| be imported from #[code spacy.lemmatizerlookup]. It's initialised with
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| the lookup table, and should be returned by the #[code create_lemmatizer]
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| classmethod of the language's defaults.
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+infobox
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| #[strong API:] #[+api("language") #[code Language]]
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| #[strong Usage:] #[+a("/docs/usage/adding-languages") Adding languages]
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+h(3, "features-matcher") Revised matcher API
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+aside-code("Example").
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@ -129,12 +130,6 @@ p
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| #[strong API:] #[+api("matcher") #[code Matcher]]
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| #[strong Usage:] #[+a("/docs/usage/rule-based-matching") Rule-based matching]
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+h(3, "features-serializer") Serialization
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+infobox
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| #[strong API:] #[+api("serializer") #[code Serializer]]
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| #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
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+h(3, "features-models") Neural network models for English, German, French and Spanish
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+infobox
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