//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0 > NEW FEATURES p | This section contains an overview of the most important | #[strong new features and improvements]. The #[+a("/api") API docs] | include additional deprecation notes. New methods and functions that | were introduced in this version are marked with a | #[span.u-text-tag.u-text-tag--spaced v2.0] tag. +h(3, "features-models") Convolutional neural network models +aside-code("Example", "bash") for model in ["en", "de", "fr", "es", "pt", "it"] | spacy download #{model} # default #{LANGUAGES[model]} model!{'\n'} | spacy download xx_ent_wiki_sm # multi-language NER p | spaCy v2.0 features new neural models for tagging, | parsing and entity recognition. The models have | been designed and implemented from scratch specifically for spaCy, to | give you an unmatched balance of speed, size and accuracy. The new | models are #[strong 10× smaller], #[strong 20% more accurate], | and #[strong just as fast] as the previous generation. | #[strong GPU usage] is now supported via | #[+a("http://chainer.org") Chainer]'s CuPy module. +infobox | #[+label-inline Usage:] #[+a("/models") Models directory], | #[+a("/models/comparison") Models comparison], | #[+a("/usage/#gpu") Using spaCy with GPU] +h(3, "features-pipelines") Improved processing pipelines +aside-code("Example"). # Set custom attributes Doc.set_extension('my_attr', default=False) Token.set_extension('my_attr', getter=my_token_getter) assert doc._.my_attr, token._.my_attr # Add components to the pipeline my_component = lambda doc: doc nlp.add_pipe(my_component) 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. Extensions let you write any | #[strong attributes, properties and methods] to the #[code Doc], | #[code Token] and #[code Span]. You can add data, implement new | features, integrate other libraries with spaCy or plug in your own | machine learning models. +image include ../../assets/img/pipeline.svg +infobox | #[+label-inline API:] #[+api("language") #[code Language]], | #[+api("doc#set_extension") #[code Doc.set_extension]], | #[+api("span#set_extension") #[code Span.set_extension]], | #[+api("token#set_extension") #[code Token.set_extension]] | #[+label-inline Usage:] | #[+a("/usage/processing-pipelines") Processing pipelines] | #[+label-inline Code:] | #[+src("/usage/examples#section-pipeline") Pipeline examples] +h(3, "features-text-classification") Text classification +aside-code("Example"). textcat = nlp.create_pipe('textcat') nlp.add_pipe(textcat, last=True) optimizer = nlp.begin_training() for itn in range(100): for doc, gold in train_data: nlp.update([doc], [gold], sgd=optimizer) doc = nlp(u'This is a text.') print(doc.cats) 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 | #[+label-inline API:] #[+api("textcategorizer") #[code TextCategorizer]], | #[+api("doc#attributes") #[code Doc.cats]], | #[+api("goldparse#attributes") #[code GoldParse.cats]]#[br] | #[+label-inline Usage:] #[+a("/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 | #[+label-inline API:] #[+api("stringstore") #[code StringStore]] | #[+label-inline Usage:] #[+a("/usage/spacy-101#vocab") Vocab, hashes and lexemes 101] +h(3, "features-vectors") Improved word vectors support +aside-code("Example"). for word, vector in vector_data: nlp.vocab.set_vector(word, vector) nlp.vocab.vectors.from_glove('/path/to/vectors') # keep 10000 unique vectors and remap the rest nlp.vocab.prune_vectors(10000) nlp.to_disk('/model') p | The new #[+api("vectors") #[code Vectors]] class helps the | #[code Vocab] manage the vectors assigned to strings, and lets you | assign vectors individually, or | #[+a("/usage/vectors-similarity#custom-loading-glove") load in GloVe vectors] | from a directory. To help you strike a good balance between coverage | and memory usage, the #[code Vectors] class lets you map | #[strong multiple keys] to the #[strong same row] of the table. If | you're using the #[+api("cli#vocab") #[code spacy vocab]] command to | create a vocabulary, pruning the vectors will be taken care of | automatically. Otherwise, you can use the new | #[+api("vocab#prune_vectors") #[code Vocab.prune_vectors]]. +infobox | #[+label-inline API:] #[+api("vectors") #[code Vectors]], | #[+api("vocab") #[code Vocab]] | #[+label-inline Usage:] #[+a("/usage/vectors-similarity") Word vectors and semantic similarity] +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("/usage/models#usage") shortcut link], the name of an installed | #[+a("/models") model package] or a path. The #[code Language] class to | initialise will be determined based on the model's settings. For a blank l | anguage, you can import the class directly, e.g. | #[code.u-break from spacy.lang.en import English] or use | #[+api("spacy#blank") #[code spacy.blank()]]. +infobox | #[+label-inline API:] #[+api("spacy#load") #[code spacy.load]], | #[+api("language#to_disk") #[code Language.to_disk]] | #[+label-inline Usage:] #[+a("/usage/models#usage") Models], | #[+a("/usage/training#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 | #[+label-inline API:] #[+api("top-level#displacy") #[code displacy]] | #[+label-inline Usage:] #[+a("/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 – and #[strong #{LANG_COUNT} languages] in total! +infobox | #[+label-inline API:] #[+api("language") #[code Language]] | #[+label-inline Code:] #[+src(gh("spaCy", "spacy/lang")) #[code spacy/lang]] | #[+label-inline Usage:] #[+a("/usage/adding-languages") Adding languages] +h(3, "features-matcher") Revised matcher API and phrase matcher +aside-code("Example"). from spacy.matcher import Matcher, PhraseMatcher matcher = Matcher(nlp.vocab) matcher.add('HEARTS', None, [{'ORTH': '❤️', 'OP': '+'}]) phrasematcher = PhraseMatcher(nlp.vocab) phrasematcher.add('OBAMA', None, nlp(u"Barack Obama")) 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. The new | #[+api("phrasematcher") #[code PhraseMatcher]] lets you efficiently | match very large terminology lists using #[code Doc] objects as match | patterns. +infobox | #[+label-inline API:] #[+api("matcher") #[code Matcher]], | #[+api("phrasematcher") #[code PhraseMatcher]] | #[+label-inline Usage:] | #[+a("/usage/linguistic-features#rule-based-matching") Rule-based matching]