{
    "info": {
        "author": "Bureaucratic Labs",
        "author_email": "hello@b-labs.pro",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Text Processing :: Linguistic"
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        "description": "# Dostoevsky [![Build Status](https://travis-ci.org/bureaucratic-labs/dostoevsky.svg?branch=master)](https://travis-ci.org/bureaucratic-labs/dostoevsky) [![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fbureaucratic-labs%2Fdostoevsky.svg?type=shield)](https://app.fossa.io/projects/git%2Bgithub.com%2Fbureaucratic-labs%2Fdostoevsky?ref=badge_shield)\n\n<img align=\"right\" src=\"https://i.imgur.com/uLMWPuL.png\">\n\nSentiment analysis library for russian language\n\n## Install\n\nPlease note that `Dostoevsky` supports only Python 3.6+\n\n```bash\n$ pip install dostoevsky\n```\n\n## Social network model [FastText]\n\nThis model was trained on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) and achieves up to ~0.71 F1 score.  \nHyperparameters used for training:\n```\nepoch = 10\nlr = 0.21909\ndim = 64\nminCount = 1\nwordNgrams = 3\nminn = 2\nmaxn = 5\nbucket = 259929\ndsub = 2\nloss = one-vs-all\n```\n\n### Usage\n\nFirst of all, you'll need to download binary model:\n\n```bash\n$ dostoevsky download fasttext-social-network-model\n```\n\nThen you can use sentiment analyzer:\n\n```python\nfrom dostoevsky.tokenization import RegexTokenizer\nfrom dostoevsky.models import FastTextSocialNetworkModel\n\ntokenizer = RegexTokenizer()\ntokens = tokenizer.split('\u0432\u0441\u0451 \u043e\u0447\u0435\u043d\u044c \u043f\u043b\u043e\u0445\u043e')  # [('\u0432\u0441\u0451', None), ('\u043e\u0447\u0435\u043d\u044c', None), ('\u043f\u043b\u043e\u0445\u043e', None)]\n\nmodel = FastTextSocialNetworkModel(tokenizer=tokenizer)\n\nmessages = [\n    '\u043f\u0440\u0438\u0432\u0435\u0442',\n    '\u044f \u043b\u044e\u0431\u043b\u044e \u0442\u0435\u0431\u044f!!',\n    '\u043c\u0430\u043b\u043e\u043b\u0435\u0442\u043d\u0438\u0435 \u0434\u0435\u0431\u0438\u043b\u044b'\n]\n\nresults = model.predict(messages, k=2)\n\nfor message, sentiment in zip(messages, results):\n    \"\"\"\n    \u043f\u0440\u0438\u0432\u0435\u0442 -> {'speech': 1.0000100135803223, 'skip': 0.0020607432816177607}\n    \u044f \u043b\u044e\u0431\u043b\u044e \u0442\u0435\u0431\u044f!! -> {'positive': 0.9886782765388489, 'skip': 0.005394937004894018}\n    \u043c\u0430\u043b\u043e\u043b\u0435\u0442\u043d\u0438\u0435 \u0434\u0435\u0431\u0438\u043b\u044b -> {'negative': 0.9525841474533081, 'neutral': 0.13661839067935944}]\n    \"\"\"\n    print(message, '->', sentiment)\n```\n\n## Social network model [CNN]\n\nThis model was trained on RuSentiment dataset too, but uses pretrained embeddings from RuSentiment dataset and achieves up to ~0.70 F1 score. Also, this model is implemented using Keras, so its possible to run on GPU.  \n![](https://i.imgur.com/bGAEWvg.png)\n\n### Usage\n\nFirst of all, you'll need to download pretrained word embeddings and model:\n\n```bash\n$ dostoevsky download vk-embeddings cnn-social-network-model\n```\n\nThen, we can build our pipeline: `text -> tokenizer -> word embeddings -> CNN`\n\n```python\nfrom dostoevsky.tokenization import UDBaselineTokenizer, RegexTokenizer\nfrom dostoevsky.embeddings import SocialNetworkEmbeddings\nfrom dostoevsky.models import SocialNetworkModel\n\ntokenizer = UDBaselineTokenizer() or RegexTokenizer()\ntokens = tokenizer.split('\u0432\u0441\u0451 \u043e\u0447\u0435\u043d\u044c \u043f\u043b\u043e\u0445\u043e')  # [('\u0432\u0441\u0451', 'ADJ'), ('\u043e\u0447\u0435\u043d\u044c', 'ADV'), ('\u043f\u043b\u043e\u0445\u043e', 'ADV')]\n\nembeddings_container = SocialNetworkEmbeddings()\n\nvectors = embeddings_container.get_word_vectors(tokens)\nvectors.shape  # (3, 300) - three words/vectors with dim=300\n\nmodel = SocialNetworkModel(\n  tokenizer=tokenizer,\n  embeddings_container=embeddings_container,\n  lemmatize=False,\n)\n\nmessages = [\n    '\u043d\u0430\u0441\u0442\u0443\u043f\u0438\u043b\u0438 \u043d\u0430 \u043d\u043e\u0433\u0443',\n    '\u0432\u0441\u0451 \u0441\u0443\u043f\u0435\u0440\u0441\u043a\u0438',\n]\n\nresults = model.predict(messages)\n\nfor message, sentiment in zip(messages, results):\n    print(message, '->', sentiment)  # \u043d\u0430\u0441\u0442\u0443\u043f\u0438\u043b\u0438 \u043d\u0430 \u043d\u043e\u0433\u0443 -> negative\n```\n\n\n## License\n[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fbureaucratic-labs%2Fdostoevsky.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fbureaucratic-labs%2Fdostoevsky?ref=badge_large)\n\n\n",
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