Update section on new v2.0 features

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ines 2017-05-24 20:54:37 +02:00
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@ -8,6 +8,65 @@ p
+h(2, "features") New features
+h(3, "features-pipelines") Improved processing pipelines
+aside-code("Example").
# Modify an existing pipeline
nlp = spacy.load('en')
nlp.pipeline.append(my_component)
# Register a factory to create a component
spacy.set_factory('my_factory', my_factory)
nlp = Language(pipeline=['my_factory', mycomponent])
p
| It's now much easier to customise the pipeline with your own components.
| Components are functions that receive a #[code Doc] object, modify and
| return it. If your component is stateful, you'll want to create a new one
| for each pipeline. You can do that by defining and registering a factory
| which receives the shared #[code Vocab] object and returns a component.
p
| spaCy's default components the vectorizer, tagger, parser and entity
| recognizer, can be added to your pipeline by using their string IDs.
| This way, you won't have to worry about finding and implementing them
| to use the default tagger, simply add #[code "tagger"] to the pipeline,
| and spaCy will know what to do.
+infobox
| #[strong API:] #[+api("language") #[code Language]]
| #[strong Usage:] #[+a("/docs/usage/language-processing-pipeline") Processing text]
+h(3, "features-serializer") Saving, loading and serialization
+aside-code("Example").
nlp = spacy.load('en') # shortcut link
nlp = spacy.load('en_core_web_sm') # package
nlp = spacy.load('/path/to/en') # unicode path
nlp = spacy.load(Path('/path/to/en')) # pathlib Path
nlp.to_disk('/path/to/nlp')
nlp = English().from_disk('/path/to/nlp')
p
| spay's serialization API has been made consistent across classes and
| objects. All container classes and pipeline components now have a
| #[code to_bytes()], #[code from_bytes()], #[code to_disk()] and
| #[code from_disk()] method that supports the Pickle protocol.
p
| The improved #[code spacy.load] makes loading models easier and more
| transparent. You can load a model by supplying its
| #[+a("/docs/usage/models#usage") shortcut link], the name of an installed
| #[+a("/docs/usage/saving-loading#generating") model package] or a path.
| The #[code Language] class to initialise will be determined based on the
| model's settings. For a blank language, you can import the class directly,
| e.g. #[code from spacy.lang.en import English].
+infobox
| #[strong API:] #[+api("spacy#load") #[code spacy.load]], #[+api("binder") #[code Binder]]
| #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
+h(3, "features-displacy") displaCy visualizer with Jupyter support
+aside-code("Example").
@ -28,33 +87,6 @@ p
| #[strong API:] #[+api("displacy") #[code displacy]]
| #[strong Usage:] #[+a("/docs/usage/visualizers") Visualizing spaCy]
+h(3, "features-loading") Loading
+aside-code("Example").
nlp = spacy.load('en') # shortcut link
nlp = spacy.load('en_core_web_sm') # package
nlp = spacy.load('/path/to/en') # unicode path
nlp = spacy.load(Path('/path/to/en')) # pathlib Path
p
| The improved #[code spacy.load] makes loading models easier and more
| transparent. You can load a model by supplying its
| #[+a("/docs/usage/models#usage") shortcut link], the name of an installed
| #[+a("/docs/usage/saving-loading#generating") model package], a unicode
| path or a #[code Path]-like object. spaCy will try resolving the load
| argument in this order. The #[code path] keyword argument is now deprecated.
p
| The #[code Language] class to initialise will be determined based on the
| model's settings. If no model is found, spaCy will let you know and won't
| just return an empty #[code Language] object anymore. If you want a blank
| language, you can always import the class directly, e.g.
| #[code from spacy.lang.en import English].
+infobox
| #[strong API:] #[+api("spacy#load") #[code spacy.load]]
| #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
+h(3, "features-language") Improved language data and lazy loading
p
@ -65,46 +97,15 @@ p
| complex regular expressions. The language data has also been tidied up
| and simplified. It's now also possible to overwrite the functions that
| compute lexical attributes like #[code like_num], and supply
| language-specific syntax iterators, e.g. to determine noun chunks.
| language-specific syntax iterators, e.g. to determine noun chunks. spaCy
| now also supports simple lookup-based lemmatization. The data is stored
| in a dictionary mapping a string to its lemma.
+infobox
| #[strong API:] #[+api("language") #[code Language]]
| #[strong Code:] #[+src(gh("spaCy", "spacy/lang")) spacy/lang]
| #[strong Usage:] #[+a("/docs/usage/adding-languages") Adding languages]
+h(3, "features-pipelines") Improved processing pipelines
+aside-code("Example").
from spacy.language import Language
nlp = Language(pipeline=['token_vectors', 'tags',
'dependencies'])
+infobox
| #[strong API:] #[+api("language") #[code Language]]
| #[strong Usage:] #[+a("/docs/usage/processing-text") Processing text]
+h(3, "features-lemmatizer") Simple lookup-based lemmatization
+aside-code("Example").
LOOKUP = {
"aba": "abar",
"ababa": "abar",
"ababais": "abar",
"ababan": "abar",
"ababanes": "ababán"
}
p
| spaCy now supports simple lookup-based lemmatization. The data is stored
| in a dictionary mapping a string to its lemma. To determine a token's
| lemma, spaCy simply looks it up in the table. The lookup lemmatizer can
| be imported from #[code spacy.lemmatizerlookup]. It's initialised with
| the lookup table, and should be returned by the #[code create_lemmatizer]
| classmethod of the language's defaults.
+infobox
| #[strong API:] #[+api("language") #[code Language]]
| #[strong Usage:] #[+a("/docs/usage/adding-languages") Adding languages]
+h(3, "features-matcher") Revised matcher API
+aside-code("Example").
@ -129,12 +130,6 @@ p
| #[strong API:] #[+api("matcher") #[code Matcher]]
| #[strong Usage:] #[+a("/docs/usage/rule-based-matching") Rule-based matching]
+h(3, "features-serializer") Serialization
+infobox
| #[strong API:] #[+api("serializer") #[code Serializer]]
| #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
+h(3, "features-models") Neural network models for English, German, French and Spanish
+infobox