Fast, DB Backed pretrained word embeddings for natural language processing.

Related tags

Text Data & NLP nlp deep-learning neural-network
Overview

Embeddings

Documentation Status https://travis-ci.org/vzhong/embeddings.svg?branch=master

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:

>>> %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

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.

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

For example:

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

Contribution

Pull requests welcome!

Comments
  • Memory Error

    Memory Error

    embeddings = [glove embedding(), kazuma char embedding()] generates a memory error on my system having 4Gb on installed memory. What is the memory requirements to run embeedings?

    opened by Hayat2018 3
  • Where is the db file on Windows?

    Where is the db file on Windows?

    I got the 1st load through but there are still only 2 files . One called 'common_crawl_840' the other is the zip I downloaded from stanford. The common_crawl_840 file is only 0kb is it the generated database file?

    opened by kurophali 2
  • badzipfile

    badzipfile

    I run the following code

    from embeddings import GloveEmbedding GloveEmbedding('common_crawl_840', d_emb=300, show_progress=True)

    with python3.6.5, embeddings==0.0.6

    and get the error: zipfile.BadZipFile: File is not a zip file

    Thanks!

    opened by libing125 2

  • "TypeError: a float is required" in Python 2.7

    I tried to use embeddings on Python 2.7 but got the following error:

      File embeddings/embeddings/glove.py", line 58, in emb
    g = self.lookup(word)
  File "embeddings/embedding.py", line 182, in lookup
    return array('f', q[0]).tolist() if q else None
TypeError: a float is required

    The above error does not show when I ran in Python 3.6.

    opened by ducalpha 2
  • Add numberbatch embeddings

    Add numberbatch embeddings

    This PR adds all currently available versions of the ConceptNet Numberbatch embeddings, as available from https://github.com/commonsense/conceptnet-numberbatch.

    I was able to mostly re-use the code from the GloVe embedding implementation and added a few comments here and there.

    Let me know if anything is missing or needs to be changed. :)

    opened by mspl13 1
  • Update embedding.py

    Update embedding.py

    Small change, but this greatly reduces the amount of time it takes to load embeddings into a database.

    I am creating a new class to load an Embedding file with a total of 13.8m embeddings. With the default implementation, this would have taken approx 13h. With these two lines, it was reduced to approx 1h45m.

    Thanks for this great package!

    opened by mickvanhulst 1
  • Create database from given embeddings

    Create database from given embeddings

    Hi,

    For a project I am working on I need to create a database to increase the efficiency of looking up embeddings. I would like to use your project as a basis for this, so I was wondering if you could give me some pointers on how I can best tackle this given the codebase you provided? If successful, I could send a PR afterwards.

    Thanks!

    opened by mickvanhulst 0
  • pip list and .__version__ are different

    pip list and .__version__ are different

    When I type pip list I got embeddings 0.0.8 But in python I got Python 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> import embeddings >>> embeddings.__version__ '0.0.6'

    opened by ymliunlp 0
Owner
Victor Zhong
I am a PhD student at the University of Washington. Formerly Salesforce Research / MetaMind, @stanfordnlp, and ECE at UToronto.
Victor Zhong
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