spaCy/website/docs/usage/v2.jade

//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0

include ../../_includes/_mixins

p
    |  We're very excited to finally introduce spaCy v2.0! On this page, you'll
    |  find a summary of the new features, information on the backwards
    |  incompatibilities, including a handy overview of what's been renamed or
    |  deprecated. To help you make the most of v2.0, we also
    |  #[strong re-wrote almost all of the usage guides and API docs], and added
    |  more real-world examples. If you're new to spaCy, or just want to brush
    |  up on some NLP basics and the details of the library, check out
    |  the #[+a("/docs/usage/spacy-101") spaCy 101 guide] that explains the most
    |  important concepts with examples and illustrations.

+h(2, "summary") Summary

+grid.o-no-block
    +grid-col("half")

        p This release features
            |  entirely new #[strong deep learning-powered models] for spaCy's tagger,
            |  parser and entity recognizer. The new models are #[strong 20x smaller]
            |  than the linear models that have powered spaCy until now: from 300 MB to
            |  only 15 MB.

        p
            |  We've also made several usability improvements that are
            |  particularly helpful for #[strong production deployments]. spaCy
            |  v2 now fully supports the Pickle protocol, making it easy to use
            |  spaCy with #[+a("https://spark.apache.org/") Apache Spark]. The
            |  string-to-integer mapping is #[strong no longer stateful], making
            |  it easy to reconcile annotations made in different processes.
            |  Models are smaller and use less memory, and the APIs for serialization
            |  are now much more consistent.

    +table-of-contents
        +item #[+a("#summary") Summary]
        +item #[+a("#features") New features]
        +item #[+a("#features-pipelines") Improved processing pipelines]
        +item #[+a("#features-text-classification") Text classification]
        +item #[+a("#features-hash-ids") Hash values instead of integer IDs]
        +item #[+a("#features-serializer") Saving, loading and serialization]
        +item #[+a("#features-displacy") displaCy visualizer]
        +item #[+a("#features-language") Language data and lazy loading]
        +item #[+a("#features-matcher") Revised matcher API]
        +item #[+a("#features-models") Neural network models]
        +item #[+a("#incompat") Backwards incompatibilities]
        +item #[+a("#migrating") Migrating from spaCy v1.x]
        +item #[+a("#benchmarks") Benchmarks]

p
    |  The main usability improvements you'll notice in spaCy v2.0 are around
    |  #[strong defining, training and loading your own models] and components.
    |  The new neural network models make it much easier to train a model from
    |  scratch, or update an existing model with a few examples. In v1.x, the
    |  statistical models depended on the state of the #[code Vocab]. If you
    |  taught the model a new word, you would have to save and load a lot of
    |  data — otherwise the model wouldn't correctly recall the features of your
    |  new example. That's no longer the case.

p
    |  Due to some clever use of hashing, the statistical models
    |  #[strong never change size], even as they learn new vocabulary items.
    |  The whole pipeline is also now fully differentiable. Even if you don't
    |  have explicitly annotated data, you can update spaCy using all the
    |  #[strong latest deep learning tricks] like adversarial training, noise
    |  contrastive estimation or reinforcement learning.

+h(2, "features") New features

p
    |  This section contains an overview of the most important
    |  #[strong new features and improvements]. The #[+a("/docs/api") API docs]
    |  include additional  deprecation notes. New methods and functions that
    |  were introduced in this version are marked with a #[+tag-new(2)] tag.

+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 #[strong customise the pipeline] with your own
    |  components, functions that receive a #[code Doc] object, modify and
    |  return it. If your component is stateful, you can define and register a
    |  factory which receives the shared #[code Vocab] object and returns a
    |  component. spaCy's default components can be added to your pipeline by
    |  using their string IDs. This way, you won't have to worry about finding
    |  and implementing them – simply add #[code "tagger"] to the pipeline,
    |  and spaCy will know what to do.

+image
    include ../../assets/img/docs/pipeline.svg

+infobox
    |  #[strong API:] #[+api("language") #[code Language]]
    |  #[strong Usage:] #[+a("/docs/usage/language-processing-pipeline") Processing text]

+h(3, "features-text-classification") Text classification

+aside-code("Example").
    from spacy.lang.en import English
    nlp = English(pipeline=['tensorizer', 'tagger', 'textcat'])

p
    |  spaCy v2.0 lets you add text categorization models to spaCy pipelines.
    |  The model supports classification with multiple, non-mutually exclusive
    |  labels – so multiple labels can apply at once. You can change the model
    |  architecture rather easily, but by default, the #[code TextCategorizer]
    |  class uses a convolutional neural network to assign position-sensitive
    |  vectors to each word in the document.

+infobox
    |  #[strong API:] #[+api("textcategorizer") #[code TextCategorizer]],
    |  #[+api("doc#attributes") #[code Doc.cats]],
    |  #[+api("goldparse#attributes") #[code GoldParse.cats]]#[br]
    |  #[strong Usage:] #[+a("/docs/usage/text-classification") Text classification]

+h(3, "features-hash-ids") Hash values instead of integer IDs

+aside-code("Example").
    doc = nlp(u'I love coffee')
    assert doc.vocab.strings[u'coffee'] == 3197928453018144401
    assert doc.vocab.strings[3197928453018144401] == u'coffee'

    beer_hash = doc.vocab.strings.add(u'beer')
    assert doc.vocab.strings[u'beer'] == beer_hash
    assert doc.vocab.strings[beer_hash] == u'beer'

p
    |  The #[+api("stringstore") #[code StringStore]] now resolves all strings
    |  to hash values instead of integer IDs. This means that the string-to-int
    |  mapping #[strong no longer depends on the vocabulary state], making a lot
    |  of workflows much simpler, especially during training. Unlike integer IDs
    |  in spaCy v1.x, hash values will #[strong always match] – even across
    |  models. Strings can now be added explicitly using the new
    |  #[+api("stringstore#add") #[code Stringstore.add]] method. A token's hash
    |  is available via #[code token.orth].

+infobox
    |  #[strong API:] #[+api("stringstore") #[code StringStore]]
    |  #[strong Usage:] #[+a("/docs/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]

+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, i.e. #[code Language], #[code Doc],
    |  #[code Vocab] and #[code StringStore] 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").
    from spacy import displacy
    doc = nlp(u'This is a sentence about Facebook.')
    displacy.serve(doc, style='dep') # run the web server
    html = displacy.render(doc, style='ent') # generate HTML

p
    |  Our popular dependency and named entity visualizers are now an official
    |  part of the spaCy library! displaCy can run a simple web server, or
    |  generate raw HTML markup or SVG files to be exported. You can pass in one
    |  or more docs, and customise the style. displaCy also auto-detects whether
    |  you're running #[+a("https://jupyter.org") Jupyter] and will render the
    |  visualizations in your notebook.

+infobox
    |  #[strong API:] #[+api("displacy") #[code displacy]]
    |  #[strong Usage:] #[+a("/docs/usage/visualizers") Visualizing spaCy]

+h(3, "features-language") Improved language data and lazy loading

p
    |  Language-specfic data now lives in its own submodule, #[code spacy.lang].
    |  Languages are lazy-loaded, i.e. only loaded when you import a
    |  #[code Language] class, or load a model that initialises one. This allows
    |  languages to contain more custom data, e.g. lemmatizer lookup tables, or
    |  complex regular expressions. The language data has also been tidied up
    |  and simplified. spaCy now also supports simple lookup-based lemmatization.

+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-matcher") Revised matcher API

+aside-code("Example").
    from spacy.matcher import Matcher
    matcher = Matcher(nlp.vocab)
    matcher.add('HEARTS', None, [{'ORTH': '❤️', 'OP': '+'}])
    assert len(matcher) == 1
    assert 'HEARTS' in matcher

p
    |  Patterns can now be added to the matcher by calling
    |  #[+api("matcher-add") #[code matcher.add()]] with a match ID, an optional
    |  callback function to be invoked on each match, and one or more patterns.
    |  This allows you to write powerful, pattern-specific logic using only one
    |  matcher. For example, you might only want to merge some entity types,
    |  and set custom flags for other matched patterns.

+infobox
    |  #[strong API:] #[+api("matcher") #[code Matcher]]
    |  #[strong Usage:] #[+a("/docs/usage/rule-based-matching") Rule-based matching]

+h(3, "features-models") Neural network models for English, German, French, Spanish and multi-language NER

+aside-code("Example", "bash").
    spacy download en # default English model
    spacy download de # default German model
    spacy download fr # default French model
    spacy download es # default Spanish model
    spacy download xx_ent_wiki_sm # multi-language NER

p
    |  spaCy v2.0 comes with new and improved neural network models for English,
    |  German, French and Spanish, as well as a multi-language named entity
    |  recognition model trained on Wikipedia. #[strong GPU usage] is now
    |  supported via #[+a("http://chainer.org") Chainer]'s CuPy module.

+infobox
    |  #[strong Details:] #[+a("/docs/api/language-models") Languages],
    |  #[+src(gh("spacy-models")) spacy-models]
    |  #[strong Usage:] #[+a("/docs/usage/models") Models],
    |  #[+a("/docs/usage#gpu") Using spaCy with GPU]

+h(2, "incompat") Backwards incompatibilities

+table(["Old", "New"])
    +row
        +cell
            |  #[code spacy.en]
            |  #[code spacy.xx]
        +cell
            |  #[code spacy.lang.en]
            |  #[code spacy.lang.xx]

    +row
        +cell #[code orth]
        +cell #[code lang.xx.lex_attrs]

    +row
        +cell #[code syntax.iterators]
        +cell #[code lang.xx.syntax_iterators]

    +row
        +cell #[code Language.save_to_directory]
        +cell #[+api("language#to_disk") #[code Language.to_disk]]

    +row
        +cell #[code Language.create_make_doc]
        +cell #[+api("language#attributes") #[code Language.tokenizer]]

    +row
        +cell
            |  #[code Vocab.load]
            |  #[code Vocab.load_lexemes]
        +cell
            |  #[+api("vocab#from_disk") #[code Vocab.from_disk]]
            |  #[+api("vocab#from_bytes") #[code Vocab.from_bytes]]

    +row
        +cell
            |  #[code Vocab.dump]
        +cell
            |  #[+api("vocab#to_disk") #[code Vocab.to_disk]]#[br]
            |  #[+api("vocab#to_bytes") #[code Vocab.to_bytes]]

    +row
        +cell
            |  #[code Vocab.load_vectors]
            |  #[code Vocab.load_vectors_from_bin_loc]
        +cell
            |  #[+api("vectors#from_disk") #[code Vectors.from_disk]]
            |  #[+api("vectors#from_bytes") #[code Vectors.from_bytes]]

    +row
        +cell
            |  #[code Vocab.dump_vectors]
        +cell
            |  #[+api("vectors#to_disk") #[code Vectors.to_disk]]
            |  #[+api("vectors#to_bytes") #[code Vectors.to_bytes]]

    +row
        +cell
            |  #[code StringStore.load]
        +cell
            |  #[+api("stringstore#from_disk") #[code StringStore.from_disk]]
            |  #[+api("stringstore#from_bytes") #[code StringStore.from_bytes]]

    +row
        +cell
            |  #[code StringStore.dump]
        +cell
            |  #[+api("stringstore#to_disk") #[code StringStore.to_disk]]
            |  #[+api("stringstore#to_bytes") #[code StringStore.to_bytes]]

    +row
        +cell #[code Tokenizer.load]
        +cell
            |  #[+api("tokenizer#from_disk") #[code Tokenizer.from_disk]]
            |  #[+api("tokenizer#from_bytes") #[code Tokenizer.from_bytes]]

    +row
        +cell #[code Tagger.load]
        +cell
            |  #[+api("tagger#from_disk") #[code Tagger.from_disk]]
            |  #[+api("tagger#from_bytes") #[code Tagger.from_bytes]]

    +row
        +cell #[code DependencyParser.load]
        +cell
            |  #[+api("dependencyparser#from_disk") #[code DependencyParser.from_disk]]
            |  #[+api("dependencyparser#from_bytes") #[code DependencyParser.from_bytes]]

    +row
        +cell #[code EntityRecognizer.load]
        +cell
            |  #[+api("entityrecognizer#from_disk") #[code EntityRecognizer.from_disk]]
            |  #[+api("entityrecognizer#from_bytes") #[code EntityRecognizer.from_bytes]]

    +row
        +cell #[code Matcher.load]
        +cell -

    +row
        +cell
            |  #[code Matcher.add_pattern]
            |  #[code Matcher.add_entity]
        +cell #[+api("matcher#add") #[code Matcher.add]]

    +row
        +cell #[code Matcher.get_entity]
        +cell #[+api("matcher#get") #[code Matcher.get]]

    +row
        +cell #[code Matcher.has_entity]
        +cell #[+api("matcher#contains") #[code Matcher.__contains__]]

    +row
        +cell #[code Doc.read_bytes]
        +cell #[+api("binder") #[code Binder]]

    +row
        +cell #[code Token.is_ancestor_of]
        +cell #[+api("token#is_ancestor") #[code Token.is_ancestor]]

    +row
        +cell #[code cli.model]
        +cell -

+h(2, "migrating") Migrating from spaCy 1.x

p
    |  Because we'e made so many architectural changes to the library, we've
    |  tried to #[strong keep breaking changes to a minimum]. A lot of projects
    |  follow the philosophy that if you're going to break anything, you may as
    |  well break everything. We think migration is easier if there's a logic to
    |  what has changed.

p
    |  We've therefore followed a policy of avoiding breaking changes to the
    |  #[code Doc], #[code Span] and #[code Token] objects. This way, you can
    |  focus on only migrating the code that does training, loading and
    |  serialization — in other words, code that works with the #[code nlp]
    |  object directly. Code that uses the annotations should continue to work.

+infobox("Important note")
    |  If you've trained your own models, keep in mind that your train and
    |  runtime inputs must match. This means you'll have to
    |  #[strong retrain your models] with spaCy v2.0.

+h(3, "migrating-saving-loading") Saving, loading and serialization

p
    |  Double-check all calls to #[code spacy.load()] and make sure they don't
    |  use the #[code path] keyword argument. If you're only loading in binary
    |  data and not a model package that can construct its own #[code Language]
    |  class and pipeline, you should now use the
    |  #[+api("language#from_disk") #[code Language.from_disk()]] method.

+code-new.
    nlp = spacy.load('/model')
    nlp = English().from_disk('/model/data')
+code-old nlp = spacy.load('en', path='/model')

p
    |  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("/docs/usage/language-processing-pipelines") processing pipelines].
    |  Appending functions to the pipeline still works – but you might be able
    |  to make this more convenient by registering "component factories".
    |  Components of the processing pipeline can now be disabled by passing a
    |  list of their names to the #[code disable] keyword argument on loading
    |  or processing.

+code-new.
    nlp = spacy.load('en', disable=['tagger', 'ner'])
    doc = nlp(u"I don't want parsed", disable=['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("/docs/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.

+h(2, "benchmarks") Benchmarks

+under-construction

+aside("Data sources")
    |  #[strong Parser, tagger, NER:] #[+a("https://www.gabormelli.com/RKB/OntoNotes_Corpus") OntoNotes 5]#[br]
    |  #[strong Word vectors:] #[+a("http://commoncrawl.org") Common Crawl]#[br]

p The evaluation was conducted on raw text with no gold standard information.

+table(["Model", "Version", "Type", "UAS", "LAS", "NER F", "POS", "w/s"])
    mixin benchmark-row(name, details, values, highlight, style)
        +row(style)
            +cell #[code=name]
            for cell in details
                +cell=cell
            for cell, i in values
                +cell.u-text-right
                    if highlight && highlight[i]
                        strong=cell
                    else
                        !=cell

    +benchmark-row("en_core_web_sm", ["2.0.0", "neural"], ["91.2", "89.2", "82.6", "96.6", "10,300"], [1, 1, 1, 0, 0])
    +benchmark-row("en_core_web_sm", ["1.2.0", "linear"], ["86.6", "83.8", "78.5", "96.6", "25,700"], [0, 0, 0, 0, 1], "divider")
    +benchmark-row("en_core_web_md", ["1.2.1", "linear"], ["90.6", "88.5", "81.4", "96.7", "18,800"], [0, 0, 0, 1, 0])