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        "author": "Ines Montani",
        "author_email": "ines@explosion.ai",
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            "Development Status :: 4 - Beta",
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        "description": "<a href=\"https://explosion.ai\"><img src=\"https://explosion.ai/assets/img/logo.svg\" width=\"125\" height=\"125\" align=\"right\" /></a>\n\n# spaCy + StanfordNLP\n\nThis package wraps the [StanfordNLP](https://github.com/stanfordnlp/stanfordnlp)\nlibrary, so you can use Stanford's models as a [spaCy](https://spacy.io)\npipeline. The Stanford models achieved top accuracy in the CoNLL 2017 and 2018\nshared task, which involves tokenization, part-of-speech tagging, morphological\nanalysis, lemmatization and labelled dependency parsing in 58 languages.\n\n[![PyPi](https://img.shields.io/pypi/v/spacy-stanfordnlp.svg?style=flat-square)](https://pypi.python.org/pypi/spacy-stanfordnlp)\n[![GitHub](https://img.shields.io/github/release/explosion/spacy-stanfordnlp/all.svg?style=flat-square)](https://github.com/explosion/spacy-stanfordnlp)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)\n\nUsing this wrapper, you'll be able to use the following annotations, computed by\nyour pretrained `stanfordnlp` 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- Sentence segmentation (`doc.sents`)\n\n## \ufe0f\ufe0f\ufe0f\u231b\ufe0f Installation\n\n```bash\npip install spacy-stanfordnlp\n```\n\nMake sure to also install one of the\n[pre-trained StanfordNLP models](https://stanfordnlp.github.io/stanfordnlp/installation_download.html). It's recommended to run StanfordNLP on Python 3.6.8+ or Python 3.7.2+.\n\n## \ud83d\udcd6 Usage & Examples\n\nThe `StanfordNLPLanguage` class can be initialized with a loaded StanfordNLP\npipeline and returns a spaCy [`Language` object](https://spacy.io/api/language),\ni.e. the `nlp` object you can use to process text and create a\n[`Doc` object](https://spacy.io/api/doc).\n\n```python\nimport stanfordnlp\nfrom spacy_stanfordnlp import StanfordNLPLanguage\n\nsnlp = stanfordnlp.Pipeline(lang=\"en\")\nnlp = StanfordNLPLanguage(snlp)\n\ndoc = nlp(\"Barack Obama was born in Hawaii. He was elected president in 2008.\")\nfor token in doc:\n    print(token.text, token.lemma_, token.pos_, token.dep_)\n```\n\nIf language data for the given language is available in spaCy, the respective\nlanguage class will be used as the base for the `nlp` object \u2013 for example,\n`English()`. This lets you use spaCy's lexical attributes like `is_stop` or\n`like_num`. The `nlp` object follows the same API as any other spaCy `Language`\nclass \u2013 so you can visualize the `Doc` objects with displaCy, add custom\ncomponents to the pipeline, use the rule-based matcher and do pretty much\nanything else you'd normally do in spaCy.\n\n```python\n# Access spaCy's lexical attributes\nprint([token.is_stop for token in doc])\nprint([token.like_num for token in doc])\n\n# Visualize dependencies\nfrom spacy import displacy\ndisplacy.serve(doc)  # or displacy.render if you're in a Jupyter notebook\n\n# Efficient processing with nlp.pipe\nfor doc in nlp.pipe([\"Lots of texts\", \"Even more texts\", \"...\"]):\n    print(doc.text)\n\n# Combine with your own custom pipeline components\ndef custom_component(doc):\n    # Do something to the doc here\n    return doc\n\nnlp.add_pipe(custom_component)\n\n# Serialize it to a numpy array\nnp_array = doc.to_array(['ORTH', 'LEMMA', 'POS'])\n```\n\n### Experimental: Mixing and matching pipeline components\n\nBy default, the `nlp` object's pipeline will be empty, because all attributes\nare computed once and set in the custom\n[`Tokenizer`](spacy_stanfordnlp/language.py). But since it's a regular `nlp`\nobject, you can add your own components to the pipeline.\n\nFor example, the entity recognizer from one of spaCy's pre-trained models:\n\n```python\nimport spacy\nimport spacy_stanfordnlp\nimport stanfordnlp\n\nsnlp = stanfordnlp.Pipeline(lang=\"en\", models_dir=\"./models\")\nnlp = StanfordNLPLanguage(snlp)\n\n# Load spaCy's pre-trained en_core_web_sm model, get the entity recognizer and\n# add it to the StanfordNLP model's pipeline\nspacy_model = spacy.load(\"en_core_web_sm\")\nner = spacy_model.get_pipe(\"ner\")\nnlp.add_pipe(ner)\n\ndoc = nlp(\"Barack Obama was born in Hawaii. He was elected president in 2008.\")\nprint([(ent.text, ent.label_) for ent in doc.ents])\n# [('Barack Obama', 'PERSON'), ('Hawaii', 'GPE'), ('2008', 'DATE')]\n```\n\nYou could also add and train\n[your own custom text classification component](https://spacy.io/usage/training#textcat).\n\n### Advanced: serialization and entry points\n\nThe spaCy `nlp` object created by `StanfordNLPLanguage` exposes its language as\n`stanfordnlp_xx`.\n\n```python\nfrom spacy.util import get_lang_class\nlang_cls = get_lang_class(\"stanfordnlp_en\")\n```\n\nNormally, the above would fail because spaCy doesn't include a language class\n`stanfordnlp_en`. But because this package exposes a `spacy_languages` entry\npoint in its [`setup.py`](setup.py) that points to `StanfordNLPLanguage`, spaCy\nknows how to initialize it.\n\nThis means that saving to and loading from disk works:\n\n```python\nsnlp = stanfordnlp.Pipeline(lang=\"en\")\nnlp = StanfordNLPLanguage(snlp)\nnlp.to_disk(\"./stanfordnlp-spacy-model\")\n```\n\nAdditional arguments on `spacy.load` are automatically passed down to the\nlanguage class and pipeline components. So when loading the saved model, you can\npass in the `snlp` argument:\n\n```python\nsnlp = stanfordnlp.Pipeline(lang=\"en\")\nnlp = spacy.load(\"./stanfordnlp-spacy-model\", snlp=snlp)\n```\n\nNote that this **will not save any model data by default**. The StanfordNLP\nmodels are very large, so for now, this package expects that you load them\nseparately.",
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