An implementation of word2vec skip-gram algorithm for word embedding, with sub-sampling and negative sampling as in the origin implementation of Mikolov paper.
The training function is organized as follows:
- corpus is generated parsing all txt files found in the specified folder
- training samples are generated and saved to disk
- in case of negative sampling, unigram table is generated too
- training process is started
- embedding data are saved to disk (with pickle)
Please note that code is written for convenience over performance and no specific optimization is in place, so embedding process for large corpora requires a very very long time!
Clone and install:
git clone https://github.com/acapitanelli/word-embedding.git
cd word-embedding
pip install .From console:
foo@bar:~$ pyembed -h
usage: pyembed [-h] [--win-size] [--dry-run] [--min-count] [--sample]
[--embedding-size] [--learning-rate] [--epochs] [--negative]
[--unigram-table]
data_dir
An implementation of word2vec algorithm for word embedding.
positional arguments:
data_dir Folder with documents of corpus
optional arguments:
-h, --help show this help message and exit
--win-size Size of moving window for context words. (default: 5)
--dry-run If true, loads corpus, generates and saves training
samples without performing NN training (default: False)
--min-count Words appearing less than min-count are excluded from
corpus (default: 5)
--sample Scale factor for subsampling probability (default: 0.001)
--embedding-size Embedding size (default: 300)
--learning-rate NN learning rate for gradient descent (default: 0.025)
--epochs Num. epochs to train (default: 10)
--negative Num. of negative samples. Negative sampling is applied
only if greater than 0 (default: 5)
--unigram-table Size of table for unigram distribution (default:
100000000)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent