From b2b01a5c8bfd90a78f4c15e75c5cd60122389bb0 Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Tue, 17 Mar 2020 19:53:31 +0100 Subject: [PATCH] Update universe.json [ci skip] --- website/meta/universe.json | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/website/meta/universe.json b/website/meta/universe.json index 91361e234..56f4f31a3 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -1,5 +1,32 @@ { "resources": [ + { + "id": "spacy-stanza", + "title": "spacy-stanza", + "slogan": "Use the latest Stanza (StanfordNLP) research models directly in spaCy", + "description": "This package wraps the Stanza (formerly StanfordNLP) library, so you can use Stanford's models as a spaCy pipeline. Using this wrapper, you'll be able to use the following annotations, computed by your pretrained `stanza` model:\n\n- Statistical tokenization (reflected in the `Doc` and its tokens)\n - Lemmatization (`token.lemma` and `token.lemma_`)\n - Part-of-speech tagging (`token.tag`, `token.tag_`, `token.pos`, `token.pos_`)\n - Dependency parsing (`token.dep`, `token.dep_`, `token.head`)\n - Named entity recognition (`doc.ents`, `token.ent_type`, `token.ent_type_`, `token.ent_iob`, `token.ent_iob_`)\n - Sentence segmentation (`doc.sents`)", + "github": "explosion/spacy-stanza", + "thumb": "https://i.imgur.com/myhLjMJ.png", + "code_example": [ + "import stanza", + "from spacy_stanza import StanzaLanguage", + "", + "snlp = stanza.Pipeline(lang=\"en\")", + "nlp = StanzaLanguage(snlp)", + "", + "doc = nlp(\"Barack Obama was born in Hawaii. He was elected president in 2008.\")", + "for token in doc:", + " print(token.text, token.lemma_, token.pos_, token.dep_, token.ent_type_)", + "print(doc.ents)" + ], + "category": ["pipeline", "standalone", "models", "research"], + "author": "Explosion", + "author_links": { + "twitter": "explosion_ai", + "github": "explosion", + "website": "https://explosion.ai" + } + }, { "id": "spacy-server", "title": "spaCy Server",