本文整理汇总了Python中vocabulary.Vocabulary.from_documents方法的典型用法代码示例。如果您正苦于以下问题：Python Vocabulary.from_documents方法的具体用法？Python Vocabulary.from_documents怎么用？Python Vocabulary.from_documents使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类vocabulary.Vocabulary的用法示例。
在下文中一共展示了Vocabulary.from_documents方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: parse_args
# 需要导入模块: from vocabulary import Vocabulary [as 别名]
# 或者: from vocabulary.Vocabulary import from_documents [as 别名]
if __name__ == '__main__':
arguments = parse_args()
logger.info('Loading config')
with open(arguments.config) as config_file:
config = yaml.load(config_file)
logger.info('Initializing input stream')
input_stream = LineSentence(
arguments.corpus,
max_sentence_length=config['sliding_window']['change_every_words']
)
min_word_freq = config['vocabulary']['min_freq']
logger.info('Building vocabulary with min_freq={}'.format(min_word_freq))
vocab = Vocabulary.from_documents(input_stream, min_word_freq)
vocabulary_size = len(vocab)
logger.info('Vocabulary size: {}'.format(vocabulary_size))
logger.info('Building negative sampling distribution')
negative_sampler = HierarchicalSampler(
vocab=vocab,
alpha=config['negative_sampling']['alpha'],
chunks_num=config['negative_sampling']['vocab_chunks_num']
)
logger.info('Building model computation graph')
optimizer = tf.train.AdagradOptimizer(
learning_rate=config['training_params']['initial_learning_rate']
)