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	Merge branch 'master' into spacy.io
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{
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    "resources": [
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        {
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            "id": "spacy-stanza",
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            "title": "spacy-stanza",
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            "slogan": "Use the latest Stanza (StanfordNLP) research models directly in spaCy",
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            "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`)",
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            "github": "explosion/spacy-stanza",
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            "thumb": "https://i.imgur.com/myhLjMJ.png",
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            "code_example": [
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                "import stanza",
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                "from spacy_stanza import StanzaLanguage",
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                "",
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                "snlp = stanza.Pipeline(lang=\"en\")",
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                "nlp = StanzaLanguage(snlp)",
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                "",
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                "doc = nlp(\"Barack Obama was born in Hawaii. He was elected president in 2008.\")",
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                "for token in doc:",
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                "    print(token.text, token.lemma_, token.pos_, token.dep_, token.ent_type_)",
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                "print(doc.ents)"
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            ],
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            "category": ["pipeline", "standalone", "models", "research"],
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            "author": "Explosion",
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            "author_links": {
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                "twitter": "explosion_ai",
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                "github": "explosion",
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                "website": "https://explosion.ai"
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            }
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        },
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        {
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            "id": "spacy-server",
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            "title": "spaCy Server",
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