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
        
        This python package contains utilities to download and make available pretrained word embeddings.
        
        Embeddings are stored in the `$EMBEDDINGS_ROOT` directory (defaults to `~/.embeddings`) in a SQLite 3 database for minimal load time and fast retrieval.
        
        Instead of loading a large file to query for embeddings, `embeddings` is fast:
        
        ```python
        In [1]: %timeit GloveEmbedding('common_crawl_840', d_emb=300)
        100 loops, best of 3: 12.7 ms per loop
        
        In [2]: %timeit GloveEmbedding('common_crawl_840', d_emb=300).emb('canada')
        100 loops, best of 3: 12.9 ms per loop
        
        In [3]: g = GloveEmbedding('common_crawl_840', d_emb=300)
        
        In [4]: %timeit -n1 g.emb('canada')
        1 loop, best of 3: 38.2 µs per loop
        ```
        
        ## Installation
        
        ```bash
        pip install embeddings  # from pypi
        pip install git+https://github.com/vzhong/embeddings.git  # from github
        ```
        
        
        ## Usage
        
        Note that on first usage, the embeddings will be downloaded. This may take a long time for large embeddings such as GloVe.
        
        ```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
