mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
395 lines
15 KiB
Plaintext
395 lines
15 KiB
Plaintext
//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0
|
||
|
||
include ../../_includes/_mixins
|
||
|
||
p
|
||
| We also re-wrote a large part of the documentation and usage workflows,
|
||
| and added more examples.
|
||
|
||
+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.
|
||
|
||
p
|
||
| 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(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-hash-ids") Hash values instead of integer IDs
|
||
|
||
+aside-code("Example").
|
||
doc = nlp(u'I love coffee')
|
||
assert doc.vocab.strings[u'coffee'] == 3197928453018144401L
|
||
assert doc.vocab.strings[3197928453018144401L] == 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").
|
||
python -m spacy download en # default English model
|
||
python -m spacy download de # default German model
|
||
python -m spacy download fr # default French model
|
||
python -m spacy download es # default Spanish model
|
||
python -m spacy download xx_ent_web_md # 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 spacy.orth]
|
||
+cell #[code spacy.lang.xx.lex_attrs]
|
||
|
||
+row
|
||
+cell #[code cli.model]
|
||
+cell -
|
||
|
||
+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]
|
||
| #[code Vocab.load_vectors]
|
||
| #[code Vocab.load_vectors_from_bin_loc]
|
||
+cell
|
||
| #[+api("vocab#from_disk") #[code Vocab.from_disk]]
|
||
| #[+api("vocab#from_bytes") #[code Vocab.from_bytes]]
|
||
|
||
+row
|
||
+cell
|
||
| #[code Vocab.dump]
|
||
| #[code Vocab.dump_vectors]
|
||
+cell
|
||
| #[+api("vocab#to_disk") #[code Vocab.to_disk]]
|
||
| #[+api("vocab#to_bytes") #[code Vocab.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 -
|
||
|
||
+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]]
|
||
|
||
+h(2, "migrating") Migrating from spaCy 1.x
|
||
|
||
+list
|
||
+item Saving, loading and serialization.
|
||
+item Processing pipelines and language data.
|
||
+item Adding patterns and callbacks to the matcher.
|
||
+item Models trained with spaCy 1.x.
|
||
|
||
+infobox("Some tips")
|
||
| Before migrating, we strongly recommend writing a few
|
||
| #[strong simple tests] specific to how you're using spaCy in your
|
||
| application. This makes it easier to check whether your code requires
|
||
| changes, and if so, which parts are affected.
|
||
| (By the way, feel free contribute your tests to
|
||
| #[+src(gh("spaCy", "spacy/tests")) our test suite] – this will also ensure
|
||
| we never accidentally introduce a bug in a workflow that's
|
||
| important to you.) 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 to make them compatible.
|
||
|
||
|
||
+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')
|
||
|
||
+h(3, "migrating-strings") Strings and hash values
|
||
|
||
+code-new.
|
||
nlp.vocab.strings.add(u'coffee')
|
||
nlp.vocab.strings[u'coffee'] # 3197928453018144401L
|
||
other_nlp.vocab.strings[u'coffee'] # 3197928453018144401L
|
||
|
||
+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'}])
|
||
|
||
+h(3, "migrating-models") Trained models
|