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Status](https://travis-ci.org/bureaucratic-labs/models.svg?branch=master)](https://travis-ci.org/bureaucratic-labs/models)\n\nГотовые к использованию статистические модели для русского языка  \nВ качестве обучающего корпуса использовался словарь [OpenCorpora](http://opencorpora.org/) (4,000+ документов на конец мая 2017 года, в основном - новости и художественная литература)  \n\n# Установка\n```bash\n$ pip install b-labs-models\n```\n\n# Определение границ предложений\n\nРезультат по F1-мере при кросс-валидации (1/3 данных использовались как тестовые): 0.99  \n\n```python\nfrom b_labs_models import SentenceSegmentator\n\ntext = '''\nРазница цепей Маркова от сетей Маркова заключается в том, что первые генеративны (т.е. предсказывают вероятность следующего шага), а вторые — дискриминатины, т.е. рассчитывают вероятность текущего состояния.\nИспользовать тот или иной алгоритм зависит от решаемой задачи. А второе, и наиболее важное отличие — это то, что сети Маркова учитывают не только шаг (два и т.д.) вправо-влево по какому-либо из параметров, а по пучку взаимосвязанных параметров.\nСкажем, для перевода это не только все его варианты, а и тематический контекст перевода, синтаксис и пр.\n'''\n\nsegmentator = SentenceSegmentator()\nsentences = segmentator.split(text)\n\nassert len(list(sentences)) == 4\n```\n\n# Токенизация\n\nРезультат по F1-мере при кросс-валидации (условия те же): 0.98  \n*Здесь стоит отметить то, что у проекта OpenCorpora свой взгляд на токенизацию: например, токены могут содержать внутри себя точки (как Яндекс.Деньги) или одно слово может быть разбито на несколько токенов (например, Жан-Поль - это три токена)*  \n\n```python\nfrom b_labs_models import Tokenizer\n\ntext = 'Плита дорожная железобетонная ПДН.м Серия 3.503.1-91, выпуск 1'\ntokenizer = Tokenizer()\n\ntokens = tokenizer.split(text)\n\nassert list(tokens) == [\n    'Плита',\n    'дорожная',\n    'железобетонная',\n    'ПДН',\n    '.м',\n    'Серия',\n    '3.503.1-91',\n    ',',\n    'выпуск',\n    '1',\n]\n\n```\n\n# Частеречная разметка (part of speech tagging)\n\n```python\nfrom b_labs_models import POSTagger\n\ntagger = POSTagger()\n\ntokens = ['Весело', 'стучали', 'храбрые', 'сердца']\nlabels = tagger.tag(tokens)\n\nassert list(zip(tokens, labels)) == [\n    ('Весело', 'ADJS'),\n    ('стучали', 'VERB'),\n    ('храбрые', 'ADJF'),\n    ('сердца', 'NOUN'),\n]\n```\n\n## Обучение\n\nTBD\n\n## License\n\nSource code licensed under MIT license, but source data (OpenCorpora annotated corpus, for example) may have different license.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbureaucratic-labs%2Fmodels","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbureaucratic-labs%2Fmodels","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbureaucratic-labs%2Fmodels/lists"}