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        "author": "Ivan Bondarenko",
        "author_email": "bond005@yandex.ru",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development",
            "Topic :: Text Processing",
            "Topic :: Text Processing :: Linguistic"
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        "description": "\nImpartial Text Classifier\n=========================\nText classifier, based on the BERT and a Bayesian neural network, which\ncan train on small labeled texts and doubt its decision.\n\nThe goal of this project is developing of simple and power text classifier\nbased on transfer learning and bayesian neural networks.\n\nA transfer learning (particulary, well-known BERT model) helps to generate\nspecial contextual embeddings for text tokens, which provide a better\ndiscrimination ability in feature space, than classical word embeddings.\nTherefore we can use smaller labeled data for training of final classifier.\n\nBayesian neural network in final classifier models uncertainty in data,\nowing to this fact probabilities of recognized classes returned by this\nnetwork are more fair, and bayesian neural network is more robust to\noverfitting.\n\n\n",
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        "license": "Apache License Version 2.0",
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        "name": "impartial-text-cls",
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        "summary": "Text classifier, based on the BERT and a Bayesian neural network, which can train on small labeled texts and doubt its decision",
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