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        "author": "Nicolas Thiebaut",
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        "description": ".. -*- mode: rst -*-\n\n|Python36|_ |TravisBuild|_ |Coveralls|_\n\n.. |Python36| image:: https://img.shields.io/badge/python-3.6-blue.svg\n.. _Python36: https://badge.fury.io/py/scikit-learn\n\n.. |TravisBuild| image:: https://travis-ci.org/nkthiebaut/zeugma.svg?branch=master&service=github\n.. _TravisBuild: https://travis-ci.org/nkthiebaut/zeugma\n\n.. |Coveralls| image:: https://img.shields.io/coveralls/github/nkthiebaut/zeugma.svg\n.. _Coveralls: https://coveralls.io/github/nkthiebaut/zeugma?branch=master\n\n======\nZeugma\n======\n\n\ud83d\udcdd Natural language processing (NLP) utils: word embeddings (Word2Vec, GloVe, FastText, ...) and preprocessing transformers, compatible with `scikit-learn Pipelines <http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html>`_. \ud83d\udee0\n\n\nInstallation\n============\n\nInstall package with ``pip install zeugma``.\n\n\nExamples\n========\n\nEmbedding transformers can be either be used with downloaded embeddings (they\nall come with a default embedding URL) or trained.\n\nPretrained embeddings\n--------------------------------\n\nAs an illustrative example the cosine similarity of the sentences *what is zeugma* and *a figure of speech* is computed using the `GloVe <https://nlp.stanford.edu/projects/glove/>`_ pretrained embeddings.::\n\n    >>> from zeugma.embeddings import EmbeddingTransformer\n    >>> glove = EmbeddingTransformer('glove')\n    >>> embeddings = glove.transform(['what is zeugma', 'a figure of speech'])\n    >>> from sklearn.metrics.pairwise import cosine_similarity\n    >>> cosine_similarity(embeddings)[0, 1]\n    0.8721696\n\nTraining embeddings\n-------------------\n\nTo train your own Word2Vec embeddings use the `Gensim sklearn API <https://radimrehurek.com/gensim/sklearn_api/w2vmodel.html>`_.\n\n\nFine-tuning embeddings\n----------------------\n\nEmbeddings fine tuning (training embeddings with preloaded values) will be implemented in the future.\n",
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