mirror of https://github.com/explosion/spaCy.git
533 lines
23 KiB
Plaintext
533 lines
23 KiB
Plaintext
//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0
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include ../_includes/_mixins
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p
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| We're very excited to finally introduce spaCy v2.0! On this page, you'll
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| find a summary of the new features, information on the backwards
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| incompatibilities, including a handy overview of what's been renamed or
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| deprecated. To help you make the most of v2.0, we also
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| #[strong re-wrote almost all of the usage guides and API docs], and added
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| more real-world examples. If you're new to spaCy, or just want to brush
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| up on some NLP basics and the details of the library, check out
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| the #[+a("/usage/spacy-101") spaCy 101 guide] that explains the most
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| important concepts with examples and illustrations.
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+h(2, "summary") Summary
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+grid.o-no-block
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+grid-col("half")
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p This release features
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| entirely new #[strong deep learning-powered models] for spaCy's tagger,
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| parser and entity recognizer. The new models are #[strong 20x smaller]
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| than the linear models that have powered spaCy until now: from 300 MB to
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| only 15 MB.
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p
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| We've also made several usability improvements that are
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| particularly helpful for #[strong production deployments]. spaCy
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| v2 now fully supports the Pickle protocol, making it easy to use
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| spaCy with #[+a("https://spark.apache.org/") Apache Spark]. The
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| string-to-integer mapping is #[strong no longer stateful], making
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| it easy to reconcile annotations made in different processes.
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| Models are smaller and use less memory, and the APIs for serialization
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| are now much more consistent.
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+table-of-contents
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+item #[+a("#summary") Summary]
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+item #[+a("#features") New features]
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+item #[+a("#features-models") Neural network models]
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+item #[+a("#features-pipelines") Improved processing pipelines]
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+item #[+a("#features-text-classification") Text classification]
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+item #[+a("#features-hash-ids") Hash values instead of integer IDs]
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+item #[+a("#features-serializer") Saving, loading and serialization]
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+item #[+a("#features-displacy") displaCy visualizer]
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+item #[+a("#features-language") Language data and lazy loading]
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+item #[+a("#features-matcher") Revised matcher API and phrase matcher]
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+item #[+a("#incompat") Backwards incompatibilities]
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+item #[+a("#migrating") Migrating from spaCy v1.x]
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+item #[+a("#benchmarks") Benchmarks]
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p
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| The main usability improvements you'll notice in spaCy v2.0 are around
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| #[strong defining, training and loading your own models] and components.
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| The new neural network models make it much easier to train a model from
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| scratch, or update an existing model with a few examples. In v1.x, the
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| statistical models depended on the state of the #[code Vocab]. If you
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| taught the model a new word, you would have to save and load a lot of
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| data — otherwise the model wouldn't correctly recall the features of your
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| new example. That's no longer the case.
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p
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| Due to some clever use of hashing, the statistical models
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| #[strong never change size], even as they learn new vocabulary items.
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| The whole pipeline is also now fully differentiable. Even if you don't
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| have explicitly annotated data, you can update spaCy using all the
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| #[strong latest deep learning tricks] like adversarial training, noise
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| contrastive estimation or reinforcement learning.
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+section("features")
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+h(2, "features") New features
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p
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| This section contains an overview of the most important
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| #[strong new features and improvements]. The #[+a("/api") API docs]
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| include additional deprecation notes. New methods and functions that
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| were introduced in this version are marked with a #[+tag-new(2)] tag.
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+h(3, "features-models") Convolutional neural network models
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+aside-code("Example", "bash").
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spacy download en # default English model
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spacy download de # default German model
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spacy download fr # default French model
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spacy download es # default Spanish model
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spacy download xx_ent_wiki_sm # multi-language NER
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p
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| spaCy v2.0 features new neural models for tagging,
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| parsing and entity recognition. The models have
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| been designed and implemented from scratch specifically for spaCy, to
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| give you an unmatched balance of speed, size and accuracy. The new
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| models are #[strong 10× smaller], #[strong 20% more accurate],
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| and #[strong just as fast] as the previous generation.
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| #[strong GPU usage] is now supported via
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| #[+a("http://chainer.org") Chainer]'s CuPy module.
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+infobox
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| #[+label-inline Usage:] #[+a("/models") Models directory],
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| #[+a("/usage/#gpu") Using spaCy with GPU]
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+h(3, "features-pipelines") Improved processing pipelines
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+aside-code("Example").
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# Set custom attributes
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Doc.set_extension('my_attr', default=False)
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Token.set_extension('my_attr', getter=my_token_getter)
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assert doc._.my_attr, token._.my_attr
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# Add components to the pipeline
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my_component = lambda doc: doc
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nlp.add_pipe(my_component)
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p
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| It's now much easier to #[strong customise the pipeline] with your own
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| components: functions that receive a #[code Doc] object, modify and
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| return it. Extensions let you write any
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| #[strong attributes, properties and methods] to the #[code Doc],
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| #[code Token] and #[code Span]. You can add data, implement new
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| features, integrate other libraries with spaCy or plug in your own
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| machine learning models.
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+image
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include ../assets/img/pipeline.svg
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+infobox
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| #[+label-inline API:] #[+api("language") #[code Language]],
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| #[+api("doc#set_extension") #[code Doc.set_extension]],
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| #[+api("span#set_extension") #[code Span.set_extension]],
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| #[+api("token#set_extension") #[code Token.set_extension]]
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| #[+label-inline Usage:]
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| #[+a("/usage/processing-pipelines") Processing pipelines]
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| #[+label-inline Code:]
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| #[+src("/usage/examples#section-pipeline") Pipeline examples]
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+h(3, "features-text-classification") Text classification
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+aside-code("Example").
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from spacy.lang.en import English
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nlp = English(pipeline=['tensorizer', 'tagger', 'textcat'])
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p
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| spaCy v2.0 lets you add text categorization models to spaCy pipelines.
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| The model supports classification with multiple, non-mutually exclusive
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| labels – so multiple labels can apply at once. You can change the model
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| architecture rather easily, but by default, the #[code TextCategorizer]
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| class uses a convolutional neural network to assign position-sensitive
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| vectors to each word in the document.
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+infobox
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| #[+label-inline API:] #[+api("textcategorizer") #[code TextCategorizer]],
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| #[+api("doc#attributes") #[code Doc.cats]],
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| #[+api("goldparse#attributes") #[code GoldParse.cats]]#[br]
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| #[+label-inline Usage:] #[+a("/usage/text-classification") Text classification]
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+h(3, "features-hash-ids") Hash values instead of integer IDs
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+aside-code("Example").
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doc = nlp(u'I love coffee')
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assert doc.vocab.strings[u'coffee'] == 3197928453018144401
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assert doc.vocab.strings[3197928453018144401] == u'coffee'
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beer_hash = doc.vocab.strings.add(u'beer')
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assert doc.vocab.strings[u'beer'] == beer_hash
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assert doc.vocab.strings[beer_hash] == u'beer'
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p
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| The #[+api("stringstore") #[code StringStore]] now resolves all strings
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| to hash values instead of integer IDs. This means that the string-to-int
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| mapping #[strong no longer depends on the vocabulary state], making a lot
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| of workflows much simpler, especially during training. Unlike integer IDs
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| in spaCy v1.x, hash values will #[strong always match] – even across
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| models. Strings can now be added explicitly using the new
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| #[+api("stringstore#add") #[code Stringstore.add]] method. A token's hash
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| is available via #[code token.orth].
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+infobox
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| #[+label-inline API:] #[+api("stringstore") #[code StringStore]]
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| #[+label-inline Usage:] #[+a("/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]
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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, i.e. #[code Language], #[code Doc],
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| #[code Vocab] and #[code StringStore] now have a #[code to_bytes()],
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| #[code from_bytes()], #[code to_disk()] and #[code from_disk()] method
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| 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("/usage/models#usage") shortcut link], the name of an installed
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| #[+a("/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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| #[+label-inline API:] #[+api("spacy#load") #[code spacy.load]]
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| #[+label-inline Usage:] #[+a("/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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from spacy import displacy
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doc = nlp(u'This is a sentence about Facebook.')
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displacy.serve(doc, style='dep') # run the web server
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html = displacy.render(doc, style='ent') # generate HTML
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p
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| Our popular dependency and named entity visualizers are now an official
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| part of the spaCy library. displaCy can run a simple web server, or
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| generate raw HTML markup or SVG files to be exported. You can pass in one
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| or more docs, and customise the style. displaCy also auto-detects whether
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| you're running #[+a("https://jupyter.org") Jupyter] and will render the
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| visualizations in your notebook.
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+infobox
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| #[+label-inline API:] #[+api("displacy") #[code displacy]]
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| #[+label-inline Usage:] #[+a("/usage/visualizers") Visualizing spaCy]
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+h(3, "features-language") Improved language data and lazy loading
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p
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| Language-specfic data now lives in its own submodule, #[code spacy.lang].
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| Languages are lazy-loaded, i.e. only loaded when you import a
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| #[code Language] class, or load a model that initialises one. This allows
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| languages to contain more custom data, e.g. lemmatizer lookup tables, or
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| complex regular expressions. The language data has also been tidied up
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| and simplified. spaCy now also supports simple lookup-based lemmatization.
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+infobox
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| #[+label-inline API:] #[+api("language") #[code Language]]
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| #[+label-inline Code:] #[+src(gh("spaCy", "spacy/lang")) #[code spacy/lang]]
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| #[+label-inline Usage:] #[+a("/usage/adding-languages") Adding languages]
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+h(3, "features-matcher") Revised matcher API and phrase matcher
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+aside-code("Example").
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from spacy.matcher import Matcher, PhraseMatcher
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matcher = Matcher(nlp.vocab)
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matcher.add('HEARTS', None, [{'ORTH': '❤️', 'OP': '+'}])
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phrasematcher = PhraseMatcher(nlp.vocab)
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phrasematcher.add('OBAMA', None, nlp(u"Barack Obama"))
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p
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| Patterns can now be added to the matcher by calling
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| #[+api("matcher-add") #[code matcher.add()]] with a match ID, an optional
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| callback function to be invoked on each match, and one or more patterns.
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| This allows you to write powerful, pattern-specific logic using only one
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| matcher. For example, you might only want to merge some entity types,
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| and set custom flags for other matched patterns. The new
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| #[+api("phrasematcher") #[code PhraseMatcher]] lets you efficiently
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| match very large terminology lists using #[code Doc] objects as match
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| patterns.
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+infobox
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| #[+label-inline API:] #[+api("matcher") #[code Matcher]],
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| #[+api("phrasematcher") #[code PhraseMatcher]]
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| #[+label-inline Usage:] #[+a("/usage/rule-based-matching") Rule-based matching]
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+section("incompat")
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+h(2, "incompat") Backwards incompatibilities
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+table(["Old", "New"])
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+row
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+cell
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| #[code spacy.en]
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| #[code spacy.xx]
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+cell
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| #[code spacy.lang.en]
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| #[code spacy.lang.xx]
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+row
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+cell #[code orth]
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+cell #[code lang.xx.lex_attrs]
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+row
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+cell #[code syntax.iterators]
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+cell #[code lang.xx.syntax_iterators]
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+row
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+cell #[code Language.save_to_directory]
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+cell #[+api("language#to_disk") #[code Language.to_disk]]
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+row
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+cell #[code Language.create_make_doc]
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+cell #[+api("language#attributes") #[code Language.tokenizer]]
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+row
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+cell
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| #[code Vocab.load]
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| #[code Vocab.load_lexemes]
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+cell
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| #[+api("vocab#from_disk") #[code Vocab.from_disk]]
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| #[+api("vocab#from_bytes") #[code Vocab.from_bytes]]
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+row
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+cell
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| #[code Vocab.dump]
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+cell
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| #[+api("vocab#to_disk") #[code Vocab.to_disk]]#[br]
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| #[+api("vocab#to_bytes") #[code Vocab.to_bytes]]
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+row
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+cell
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| #[code Vocab.load_vectors]
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| #[code Vocab.load_vectors_from_bin_loc]
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+cell
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| #[+api("vectors#from_disk") #[code Vectors.from_disk]]
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| #[+api("vectors#from_bytes") #[code Vectors.from_bytes]]
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+row
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+cell
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| #[code Vocab.dump_vectors]
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+cell
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| #[+api("vectors#to_disk") #[code Vectors.to_disk]]
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| #[+api("vectors#to_bytes") #[code Vectors.to_bytes]]
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+row
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+cell
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| #[code StringStore.load]
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+cell
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| #[+api("stringstore#from_disk") #[code StringStore.from_disk]]
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| #[+api("stringstore#from_bytes") #[code StringStore.from_bytes]]
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+row
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+cell
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| #[code StringStore.dump]
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+cell
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| #[+api("stringstore#to_disk") #[code StringStore.to_disk]]
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| #[+api("stringstore#to_bytes") #[code StringStore.to_bytes]]
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+row
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+cell #[code Tokenizer.load]
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+cell
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| #[+api("tokenizer#from_disk") #[code Tokenizer.from_disk]]
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| #[+api("tokenizer#from_bytes") #[code Tokenizer.from_bytes]]
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+row
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+cell #[code Tagger.load]
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+cell
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| #[+api("tagger#from_disk") #[code Tagger.from_disk]]
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| #[+api("tagger#from_bytes") #[code Tagger.from_bytes]]
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+row
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+cell #[code DependencyParser.load]
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+cell
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| #[+api("dependencyparser#from_disk") #[code DependencyParser.from_disk]]
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| #[+api("dependencyparser#from_bytes") #[code DependencyParser.from_bytes]]
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+row
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+cell #[code EntityRecognizer.load]
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+cell
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| #[+api("entityrecognizer#from_disk") #[code EntityRecognizer.from_disk]]
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| #[+api("entityrecognizer#from_bytes") #[code EntityRecognizer.from_bytes]]
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+row
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+cell #[code Matcher.load]
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+cell -
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+row
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+cell
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| #[code Matcher.add_pattern]
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| #[code Matcher.add_entity]
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+cell #[+api("matcher#add") #[code Matcher.add]]
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+row
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+cell #[code Matcher.get_entity]
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+cell #[+api("matcher#get") #[code Matcher.get]]
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+row
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+cell #[code Matcher.has_entity]
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+cell #[+api("matcher#contains") #[code Matcher.__contains__]]
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+row
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+cell #[code Doc.read_bytes]
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+cell
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| #[+api("doc#to_bytes") #[code Doc.to_bytes]]
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| #[+api("doc#from_bytes") #[code Doc.from_bytes]]
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| #[+api("doc#to_disk") #[code Doc.to_disk]]
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| #[+api("doc#from_disk") #[code Doc.from_disk]]
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+row
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+cell #[code Token.is_ancestor_of]
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+cell #[+api("token#is_ancestor") #[code Token.is_ancestor]]
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+row
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+cell #[code cli.model]
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+cell -
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+section("migrating")
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+h(2, "migrating") Migrating from spaCy 1.x
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p
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| Because we'e made so many architectural changes to the library, we've
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| tried to #[strong keep breaking changes to a minimum]. A lot of projects
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| follow the philosophy that if you're going to break anything, you may as
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| well break everything. We think migration is easier if there's a logic to
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| what has changed.
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p
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| We've therefore followed a policy of avoiding breaking changes to the
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| #[code Doc], #[code Span] and #[code Token] objects. This way, you can
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| focus on only migrating the code that does training, loading and
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| serialization — in other words, code that works with the #[code nlp]
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| object directly. Code that uses the annotations should continue to work.
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+infobox("Important note")
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| If you've trained your own models, keep in mind that your train and
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| runtime inputs must match. This means you'll have to
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| #[strong retrain your models] with spaCy v2.0.
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+h(3, "migrating-saving-loading") Saving, loading and serialization
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|
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p
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| Double-check all calls to #[code spacy.load()] and make sure they don't
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| use the #[code path] keyword argument. If you're only loading in binary
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| data and not a model package that can construct its own #[code Language]
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| class and pipeline, you should now use the
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| #[+api("language#from_disk") #[code Language.from_disk()]] method.
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+code-new.
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nlp = spacy.load('/model')
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nlp = English().from_disk('/model/data')
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+code-old nlp = spacy.load('en', path='/model')
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p
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| Review all other code that writes state to disk or bytes.
|
||
| All containers, now share the same, consistent API for saving and
|
||
| loading. Replace saving with #[code to_disk()] or #[code to_bytes()], and
|
||
| loading with #[code from_disk()] and #[code from_bytes()].
|
||
|
||
+code-new.
|
||
nlp.to_disk('/model')
|
||
nlp.vocab.to_disk('/vocab')
|
||
|
||
+code-old.
|
||
nlp.save_to_directory('/model')
|
||
nlp.vocab.dump('/vocab')
|
||
|
||
p
|
||
| If you've trained models with input from v1.x, you'll need to
|
||
| #[strong retrain them] with spaCy v2.0. All previous models will not
|
||
| be compatible with the new version.
|
||
|
||
+h(3, "migrating-strings") Strings and hash values
|
||
|
||
p
|
||
| The change from integer IDs to hash values may not actually affect your
|
||
| code very much. However, if you're adding strings to the vocab manually,
|
||
| you now need to call #[+api("stringstore#add") #[code StringStore.add()]]
|
||
| explicitly. You can also now be sure that the string-to-hash mapping will
|
||
| always match across vocabularies.
|
||
|
||
+code-new.
|
||
nlp.vocab.strings.add(u'coffee')
|
||
nlp.vocab.strings[u'coffee'] # 3197928453018144401
|
||
other_nlp.vocab.strings[u'coffee'] # 3197928453018144401
|
||
|
||
+code-old.
|
||
nlp.vocab.strings[u'coffee'] # 3672
|
||
other_nlp.vocab.strings[u'coffee'] # 40259
|
||
|
||
+h(3, "migrating-languages") Processing pipelines and language data
|
||
|
||
p
|
||
| If you're importing language data or #[code Language] classes, make sure
|
||
| to change your import statements to import from #[code spacy.lang]. If
|
||
| you've added your own custom language, it needs to be moved to
|
||
| #[code spacy/lang/xx] and adjusted accordingly.
|
||
|
||
+code-new from spacy.lang.en import English
|
||
+code-old from spacy.en import English
|
||
|
||
p
|
||
| If you've been using custom pipeline components, check out the new
|
||
| guide on #[+a("/usage/language-processing-pipelines") processing pipelines].
|
||
| Appending functions to the pipeline still works – but the
|
||
| #[+api("language#add_pipe") #[code add_pipe]] methods now makes this
|
||
| much more convenient. Components of the processing pipeline can now
|
||
| be disabled by passing a list of their names to the #[code disable]
|
||
| keyword argument on load, or by simply demoving them from the
|
||
| pipeline alltogether.
|
||
|
||
+code-new.
|
||
nlp = spacy.load('en', disable=['tagger', 'ner'])
|
||
doc = nlp(u"I don't want parsed", disable['parser'])
|
||
nlp.remove_pipe('parser')
|
||
+code-old.
|
||
nlp = spacy.load('en', tagger=False, entity=False)
|
||
doc = nlp(u"I don't want parsed", parse=False)
|
||
|
||
+h(3, "migrating-matcher") Adding patterns and callbacks to the matcher
|
||
|
||
p
|
||
| If you're using the matcher, you can now add patterns in one step. This
|
||
| should be easy to update – simply merge the ID, callback and patterns
|
||
| into one call to #[+api("matcher#add") #[code Matcher.add()]].
|
||
|
||
+code-new.
|
||
matcher.add('GoogleNow', merge_phrases, [{ORTH: 'Google'}, {ORTH: 'Now'}])
|
||
|
||
+code-old.
|
||
matcher.add_entity('GoogleNow', on_match=merge_phrases)
|
||
matcher.add_pattern('GoogleNow', [{ORTH: 'Google'}, {ORTH: 'Now'}])
|
||
|
||
p
|
||
| If you've been using #[strong acceptor functions], you'll need to move
|
||
| this logic into the
|
||
| #[+a("/usage/rule-based-matching#on_match") #[code on_match] callbacks].
|
||
| The callback function is invoked on every match and will give you access to
|
||
| the doc, the index of the current match and all total matches. This lets
|
||
| you both accept or reject the match, and define the actions to be
|
||
| triggered.
|
||
|
||
+section("benchmarks")
|
||
+h(2, "benchmarks") Benchmarks
|
||
|
||
include _facts-figures/_benchmarks-models
|