mirror of https://github.com/explosion/spaCy.git
Update docs for new version [ci skip]
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@ -346,6 +346,7 @@ p
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+row
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+cell #[code --verbose]
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+tag-new("2.0.13")
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+cell flag
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+cell Show more detail message during training.
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@ -349,6 +349,30 @@ p Retokenize the document, such that the span is merged into a single token.
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+cell #[code Token]
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+cell The newly merged token.
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+h(2, "ents") Span.ents
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+tag property
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+tag-model("NER")
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p
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| Iterate over the entities in the span. Yields named-entity
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| #[code Span] objects, if the entity recognizer has been applied to the
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| parent document.
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+aside-code("Example").
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doc = nlp(u'Mr. Best flew to New York on Saturday morning.')
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span = doc[0:6]
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ents = list(span.ents)
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assert ents[0].label == 346
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assert ents[0].label_ == 'PERSON'
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assert ents[0].text == 'Mr. Best'
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell yields
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+cell #[code Span]
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+cell Entities in the document.
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+h(2, "as_doc") Span.as_doc
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p
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@ -27,17 +27,6 @@
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}
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},
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"comparison": {
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"title": "Model Comparison",
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"teaser": "Compare spaCy's statistical models and their accuracy.",
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"tag": "experimental",
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"compare_models": true,
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"default_models": {
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"model1": "en_core_web_sm",
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"model2": "en_core_web_lg"
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}
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},
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"MODELS": {
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"en": ["en_core_web_sm", "en_core_web_md", "en_core_web_lg", "en_vectors_web_lg"],
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"de": ["de_core_news_sm"],
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@ -122,6 +111,8 @@
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"fa": "Persian",
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"ur": "Urdu",
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"tt": "Tatar",
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"te": "Telugu",
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"si": "Sinhala",
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"ga": "Irish",
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"bn": "Bengali",
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"hi": "Hindi",
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