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Update universe.json [ci skip]

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Ines Montani 2020-03-17 19:53:31 +01:00
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website/meta/universe.json
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@ -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",