Metadata-Version: 1.1
Name: embeddings
Version: 0.0.3
Summary: Pretrained word embeddings in Python.
Home-page: https://github.com/vzhong/embeddings
Author: Victor Zhong
Author-email: victor@victorzhong.com
License: MIT
Description: Embeddings
        ==========
        
        .. image:: https://readthedocs.org/projects/embeddings/badge/?version=latest
            :target: http://embeddings.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        .. image:: https://travis-ci.org/vzhong/embeddings.svg?branch=master
            :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``).
        
        
        .. code-block:: python
        
            from embeddings import GloveEmbedding, FastTextEmbedding, KazumaCharEmbedding
            
            g = GloveEmbedding('common_crawl_840', d_emb=300, show_progress=True)
            f = FastTextEmbedding()
            k = KazumaCharEmbedding()
            for w in ['canada', 'vancouver', 'toronto']:
                print('embedding {}'.format(w))
                print(g.emb(w))
                print(f.emb(w))
                print(k.emb(w))
        
        
        Contribution
        ------------
        
        Pull requests welcome!
        
Keywords: text nlp machine-learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
