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        "author": "Nick Haynes",
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        "description": "# spaCy-CLD: Bringing simple language detection to spaCy\n\n## Installation\n\n`pip install spacy_cld`\n\n## Usage\n\nAdding the spaCy-CLD component to the processing pipeline is relatively simple:\n\n```\nimport spacy\nfrom spacy_cld import LanguageDetector\n\nnlp = spacy.load('en')\nlanguage_detector = LanguageDetector()\nnlp.add_pipe(language_detector)\ndoc = nlp('This is some English text.')\n\ndoc._.languages  # ['en']\ndoc._.language_scores['en']  # 0.96\n```\n\nspaCy-CLD operates on `Doc` and `Span` spaCy objects. When called on a `Doc` or `Span`, the object is given two attributes: `languages` (a list of up to 3 language codes) and `language_scores` (a dictionary mapping language codes to confidence scores between 0 and 1).\n\n## Under the hood\n\nspacy-cld is a little extension that wraps the [PYCLD2](https://github.com/aboSamoor/pycld2) Python library, which in turn wraps the [Compact Language Detector 2](https://github.com/CLD2Owners/cld2) C library originally built at Google for the Chromium project. CLD2 uses character n-grams as features and a Naive Bayes classifier to identify 80+ languages from Unicode text strings (or XML/HTML). It can detect up to 3 different languages in a given document, and reports a confidence score (reported in with each language.\n\nFor additional details, see the linked project pages for PYCLD2 and CLD2.",
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