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    "info": {
        "author": "Victor Zhong",
        "author_email": "victor@victorzhong.com",
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        "classifiers": [
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.5"
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        "description": "Embeddings\n==========\n\n.. image:: https://readthedocs.org/projects/embeddings/badge/?version=latest\n    :target: http://embeddings.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n.. image:: https://travis-ci.org/vzhong/embeddings.svg?branch=master\n    :target: https://travis-ci.org/vzhong/embeddings\n\nEmbeddings is a python package that provides pretrained word embeddings for natural language processing and machine learning.\n\nInstead of loading a large file to query for embeddings, ``embeddings`` is backed by a database and fast to load and query:\n\n.. code-block:: python\n\n    >>> %timeit GloveEmbedding('common_crawl_840', d_emb=300)\n    100 loops, best of 3: 12.7 ms per loop\n\n    >>> %timeit GloveEmbedding('common_crawl_840', d_emb=300).emb('canada')\n    100 loops, best of 3: 12.9 ms per loop\n\n    >>> g = GloveEmbedding('common_crawl_840', d_emb=300)\n\n    >>> %timeit -n1 g.emb('canada')\n    1 loop, best of 3: 38.2 \u00b5s per loop\n\n\nInstallation\n------------\n\n.. code-block:: sh\n\n    pip install embeddings  # from pypi\n    pip install git+https://github.com/vzhong/embeddings.git  # from github\n\n\nUsage\n-----\n\nUpon first use, the embeddings are first downloaded to disk in the form of a SQLite database.\nThis may take a long time for large embeddings such as GloVe.\nFurther usage of the embeddings are directly queried against the database.\nEmbedding databases are stored in the ``$EMBEDDINGS_ROOT`` directory (defaults to ``~/.embeddings``). Note that this location is probably **undesirable** if your home directory is on NFS, as it would slow down database queries significantly.\n\n\n.. code-block:: python\n\n    from embeddings import GloveEmbedding, FastTextEmbedding, KazumaCharEmbedding, ConcatEmbedding\n\n    g = GloveEmbedding('common_crawl_840', d_emb=300, show_progress=True)\n    f = FastTextEmbedding()\n    k = KazumaCharEmbedding()\n    c = ConcatEmbedding([g, f, k])\n    for w in ['canada', 'vancouver', 'toronto']:\n        print('embedding {}'.format(w))\n        print(g.emb(w))\n        print(f.emb(w))\n        print(k.emb(w))\n        print(c.emb(w))\n\n\nDocker\n------\n\nIf you use Docker, an image prepopulated with the Common Crawl 840 GloVe embeddings and Kazuma Hashimoto's character ngram embeddings is available at `vzhong/embeddings <https://hub.docker.com/r/vzhong/embeddings>`_.\nTo mount volumes from this container, set ``$EMBEDDINGS_ROOT`` in your container to ``/opt/embeddings``.\n\nFor example:\n\n.. code-block:: bash\n\n    docker run --volumes-from vzhong/embeddings -e EMBEDDINGS_ROOT='/opt/embeddings' myimage python train.py\n\n\nContribution\n------------\n\nPull requests welcome!\n\n\n",
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        "keywords": "text nlp machine-learning",
        "license": "MIT",
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        "name": "embeddings",
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