sequence_pattern_class = a SequencePattern class instance, which defines pattern parameters (input, output

Create the model. Args: source_vocab_size: size of the source vocabulary. target_vocab_size: size of the target vocabulary. buc

Compute accuracy of the prediction, based on the true labels. Use the average number of equal values.

向分词结果集添加词元 @param lexeme

批量加载新词条 @param words Collection<String>词条列表

分词

实现Comparable接口 @param o @return int

重载Analyzer接口，构造分词组件

批量移除（屏蔽）词条 @param words

Embedding sequence-to-sequence model with attention. This model first embeds encoder_inputs by a newly created embedding (of shape [num_encoder_s

获取扩展字典配置路径 @return List<String> 相对类加载器的路径

获取扩展停止词典配置路径 @return List<String> 相对类加载器的路径

获取主词典路径 @return String 主词典路径

获取Path的有效词长 @return

获取量词词典路径 @return String 量词词典路径

get_local_group_id_map

get_local_word_id_map

get_vec_dim

get_x_dim

Unzips from gz_path into new_path.

模拟： 创建一个单条记录的索引，并对其进行搜索 @param args

Generate sequence with [max, min, rest of original entries]

Get WMT data into data_dir, create vocabularies and tokenize data. Args: data_dir: directory in which the data sets will be stored. en_voca

重置子分词器状态

重置分词器到初始状态 @param input

重置分词上下文状态

Generate reversed version of original sequence

设置useSmart标志位 useSmart =true ，分词器使用智能切分策略， =false则使用细粒度切分 @param useSmart

Generate sorted version of original sequence

@return the suite of tests being tested

Rigourous Test :-)