Embeddings

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:target: https://travis-ci.org/vzhong/embeddings

Embeddings is a python package that provides pretrained word embeddings for natural language processing and machine learning.

Instead of loading a large file to query for embeddings, embeddings is backed by a database and fast to load and query:

.. code-block:: python

>>> %timeit GloveEmbedding('common_crawl_840', d_emb=300)
100 loops, best of 3: 12.7 ms per loop

>>> %timeit GloveEmbedding('common_crawl_840', d_emb=300).emb('canada')
100 loops, best of 3: 12.9 ms per loop

>>> g = GloveEmbedding('common_crawl_840', d_emb=300)

>>> %timeit -n1 g.emb('canada')
1 loop, best of 3: 38.2 µs per loop

Installation

.. code-block:: sh

pip install embeddings  # from pypi
pip install git+https://github.com/vzhong/embeddings.git  # from github

Usage

Upon first use, the embeddings are first downloaded to disk in the form of a SQLite database.
This may take a long time for large embeddings such as GloVe.
Further usage of the embeddings are directly queried against the database.
Embedding 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.

.. code-block:: python

from embeddings import GloveEmbedding, FastTextEmbedding, KazumaCharEmbedding, ConcatEmbedding

g = GloveEmbedding('common_crawl_840', d_emb=300, show_progress=True)
f = FastTextEmbedding()
k = KazumaCharEmbedding()
c = ConcatEmbedding([g, f, k])
for w in ['canada', 'vancouver', 'toronto']:
    print('embedding {}'.format(w))
    print(g.emb(w))
    print(f.emb(w))
    print(k.emb(w))
    print(c.emb(w))

Docker

If 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>_.
To mount volumes from this container, set $EMBEDDINGS_ROOT in your container to /opt/embeddings.

For example:

.. code-block:: bash

docker run --volumes-from vzhong/embeddings -e EMBEDDINGS_ROOT='/opt/embeddings' myimage python train.py

Contribution

Pull requests welcome!

Dependency	Optional
numpy	false
requests	false
tqdm	false
check-manifest	true
coverage	true
nose	true
sphinx	true
sphinx-rtd-theme	true

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embeddings

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