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        "author": "JBMountford",
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            "License :: OSI Approved :: MIT License",
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        "description": "# Saniti\n\n**Sanitise lists of text documents quickly, easily and whilst maintaining your sanity**\n\nThe aim was to streamline processing lists of documents into the same outputs into simply specifying the list of texts and defining the sanitization pipeline.\n\n### Usage:\n\n**As a function-ish**\n\n```\nimport saniti\noriginal_text = [\"I like to moves it, move its\", \"I likeing to move it!\", \"the of\"]\ntext = saniti.saniti(original_text, [\"token\", \"destop\", \"depunct\", \"unempty\", \"stem\", \"out_corp_dict\"]) #sanitise the text while initalising the class\nprint(text.text)\n\n{'dictionary': <gensim.corpora.dictionary.Dictionary object at 0x000002BA9F5FFEF0>, 'corpus': [[(0, 1), (1, 1), (2, 2)], [(0, 1), (1, 1), (2, 1)], []]}\n```\n\n**As a class**\n\n```\nimport saniti\nsani1 = saniti.saniti() # initialise the santising class\ntext = sani1.process(original_text, [\"token\", \"destop\", \"depunct\", \"unempty\", \"lemma\", \"out_tag_doc\"]) # sanitise the text\nprint(text)\n\n[TaggedDocument(words=['I', 'like', 'move', 'move'], tags=['I like move move']), TaggedDocument(words=['I', 'likeing', 'move'], tags=['I likeing move']), TaggedDocument(words=[], tags=[''])]\n```\n\n## Pipeline Components\n\n* \"token\" - tokenise texts\n* \"depunct\" - remove punctuation\n* \"unempty\" - remove empty words within documents\n* \"lemma\" - lemmatize text\n* \"destop\" - remove stopwords\n* \"stem\" - stem texts\n* \"out_tag_doc\" - turns the texts into gensim tagged documents for Doc2Vec\n* \"out_corp_dict\" - turns the texts into gensim corpus and dictionary",
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