def main():
    parser = optparse.OptionParser()
    parser.add_option('-d', '--dataset', default='sample')
    parser.add_option('--size', default=300, type='int', help='vectors dimension. Default: %default')
    parser.add_option('--window', default=5, type='int', help='window size. Default: %default')
    parser.add_option('--min_count', default=5, type='int', help='Min count. Default: %default')
    (options, args) = parser.parse_args()
    documents = list(Utils.read_json(('%s-tokenized.json' % options.dataset)))
    model = gensim.models.word2vec.Word2Vec(documents, size=options.size, window=options.window, min_count=options.min_count, workers=4)
    model.save(('%s-word-vector-model' % options.dataset))
def main():
    parser = optparse.OptionParser()
    parser.add_option('-d', '--dataset', default='sample')
    parser.add_option('--size', default=300, type='int', help='vectors dimension. Default: %default')
    parser.add_option('--window', default=5, type='int', help='window size. Default: %default')
    parser.add_option('--min_count', default=5, type='int', help='Min count. Default: %default')
    (options, args) = parser.parse_args()
    documents = list(Utils.read_json(('%s-tokenized.json' % options.dataset)))
    model = gensim.models.word2vec.Word2Vec(documents, size=options.size, window=options.window, min_count=options.min_count, workers=4)
    model.save(('%s-word-vector-model' % options.dataset))