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        "author": "Cosmos Zhou",
        "author_email": "744984949@qq.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n# sentence splitting and intent classification\n\n# intent classification\n\nto split a paragraph into sentences, to classify the intent of a sentence, implement the following: \n\n\narr = classification.intent_splitting.split('\u6765\u70b9\u8212\u7f13\u4e00\u70b9\u7684\u97f3\u4e50\u7136\u540e\u5f00\u59cb\u5bfc\u822a') \n\nprint(arr)\n\nresult: [('\u6765\u70b9\u8212\u7f13\u4e00\u70b9\u7684\u97f3\u4e50', 'genre'),  ('\u7136\u540e\u5f00\u59cb\u5bfc\u822a', 'navi')]\n\n\n#the usage of bert_semantic:\n#retrieve qa pairs from solr:\n            question, answer = most_similar(s)\n            if question:\n                user_question = [s] * len(question)\n                similarity = bert_semantic.instance.predict(user_question, question)\n\n                for sent, score in zip(question, similarity):\n                    print('%s / %s = %f\\t%f' % (s, sent, score))\n\n                index = similarity.argmax()\n                print('highest score for bert semantic')\n                print('%s / %s = %f' % (s, question[index], similarity[index]))\n                print('answer = ', answer[index])\n                question = question[index]\n                answer = answer[index]\n                similarity = similarity[index]\n\n                if similarity >= thredshold:\n                \treturn answer            \n\n            return None\n\n\n",
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        "license": "MIT",
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        "name": "cosmosnlp",
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        "summary": "My short description for my project.",
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