qq_37418807的博客

1.使用torch定义模型# 使用torch版本import torchimport torch.nn as nnclass LSTMText(nn.Module): def __init__(self,vocab_size,embedding_dim,output_dim,hidden_dim = 64,num_layers = 2,dropout = 0.5): super(LSTMText,self).__init__() self.embedding

1.构建vocab(1) 直接构建vocabfrom fastNLP import Vocabularyvocab = Vocabulary()vocab.add_word_lst(['小','黑','无','敌'])vocab.add_word('小黑')print('小:',vocab.to_index('小'))print('小黑:',vocab.to_index('小黑'))print('词典全貌:',list(vocab))print('---------------------

DataSet的构建字典构建from fastNLP import DataSet# 传入字典构建datasetdata = {'raw_words':["This is the first instance .", "Second instance .", "Third instance ."], 'words': [['this', 'is', 'the', 'first', 'instance', '.'], ['Second', 'instance', '.'], ['Thi

1.数据读取import osdata_dir = './data/atis/'train_dir = os.path.join(data_dir,'train')test_dir = os.path.join(data_dir,'test')dev_dir = os.path.join(data_dir,'dev')# 定义数据集text与labels的路径train_text_path = os.path.join(train_dir,'seq.in') train_label_path

文章目录1.载入数据2.将数据转化为DataBundle形式输出:3.创建词典输出:4.载入词向量5.定义与训练模型输出:6.测试模型输出:1.载入数据import osdata_dir = './data/atis/'train_dir = os.path.join(data_dir,'train')test_dir = os.path.join(data_dir,'test')dev_dir = os.path.join(data_dir,'dev')train_text_path =