pyemblib 0.1.2

Lightweight package for reading/writing pre-trained word embedding files

# pyemblib

A module for reading, writing, and using trained word embeddings.

## Installation

Install with pip!

`bash pip install pyemblib `

## Usage

This package currently supports word embeddings trained by the following packages:

• [word2vec](https://code.google.com/archive/p/word2vec/)

• [GloVe](https://nlp.stanford.edu/projects/glove/)

### Reading

Both text-format and binary embedding files are supported.

The example below shows reading each format of embedding: `python ## import text embeddings text_embs = pyemblib.read('/tmp/text_embeddings.txt', mode=pyemblib.Mode.Text) ## import binary embeddings bin_embs = pyemblib.read('/tmp/bin_embeddings.bin', mode=pyemblib.Mode.Binary) `

Embeddings are read as a pyemblib.Embeddings object, which inherits from Python’s dictionary class; keys are words, and values are the embedding arrays.

To get the word vector for “python”, just use dictionary access: `python vec = embs['python'] print(vec) # [ 0.001 -0.237 ... ] `

### Writing

The same text and binary modes can be used for writing out embedding files as for reading.

`python embs = { 'a' : np.array([0.3 0.1 -0.2]), 'b' : np.array([-0.9, -0.2, -0.2]) } ## write as text pyemblib.write(embs, '/tmp/text_embeddings.txt', mode=pyemblib.Mode.Text) ## write as binary pyemblib.write(embs, '/tmp/bin_embeddings.bin', mode=pyemblib.Mode.Binary) `

## Feedback Please report any issues you encounter to the [Github Issues page](https://github.com/drgriffis/pyemblib/issues)!