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        "description": "# library.qai.spacy\n\nCustomized SpaCy pipeline\n\n## Installing\n\nThis is available on [PyPi](https://pypi.org/project/en-qai-sm/)\n\n```sh\n$ pip install en-qai-sm\n> installs the package and deps including spacy\n```\n\n## Usage\n\n```python\nimport spacy\nnlp = spacy.load('en_qai_sm')\n>>> doc = nlp(\"I ain't got no hands!\")\n>>> for token in doc: print(token, token.pos_)\n...\nI PRON\nain't VERB\ngot VERB\nno DET\nhands NOUN\n! PUNCT\n```\n\n## About SpaCy pipelines\n\nDefault spaCy pipeline consists of 4 steps (components):\n\n![spaCy pipeline](img/pipeline.png)\n\n- `tokenizer` - splits text into tokens\n- `tagger` - assigns part-of-speech tags\n- `parser` - assigns dependency labels\n- `ner` - detects and label named entities\n\nCustom components (ex. any functions on `doc`) can be inserted into the pipeline (at any place after the `tokenizer`. For simplicity, `tokenizer` is not listed in pipelines descriptions.)\n\nReference: [spaCy docs](https://spacy.io/usage/processing-pipelines).\n\n## Pipeline components\n\n### v1.0.0\n\nThe pipeline consists of:\n\n```python\npipeline = [\n    \"merge_matcher\",\n    \"tagger\",\n    \"parser\",\n    \"ner\"\n    ]\n```\n\nwhere ```merge_matcher``` matches and merges into 1 token spans of type:\n\n- connected by hyphens ex.  ```rock-hard```\n- contractions ex. ```don't```\n- special (informal) short forms ex. ```gonna```\n\n\n## License\n\nAs this is just a small extension of spaCy's `en_core_web_sm`, we include the same license - MIT.",
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        "summary": "English multi-task CNN trained on OntoNotes. Assigns context-specific token vectors, POS tags, dependency parse and named entities.",
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