模型原理：利用LSTM模型，用前面的单词预测下一个单词。例如 I like the world.我们在这句话的首尾加上<s>和</s>，<s>代表开始</s>代表结束。 Input data是 <s> I like theworld.，labels 为I like the world.</s>.代码：import torchi

RNN LSTM GRU 代码实战 ---- 简单的文本生成任务import torchif torch.cuda.is_available():# Tell PyTorch to use the GPU.device = torch.device("cuda")print('There are %d GPU(s) available.' % torch.cuda.device_count())p

使用kashgari快速搭建文本分类模型，对比各个模型间的正确率。import kashgarifrom kashgari.embeddings import BERTEmbedding# chinese_roberta_wwm_largebert_embed = BERTEmbedding('chinese_roberta_wwm_large', task=kashgari.CLASSIFICA

import sysimport osimport copyimport jsonimport loggingimport argparseimport torchimport numpy as npfrom tqdm import tqdm, trangeimport torch.nn as nnfrom torchcrf import CRFfrom torch.utils.data impo

导入命名实体import torchimport pandas as pdimport numpy as nppath = './'comments = pd.read_csv(path + '英文命名实体信息.csv', encoding="latin1").fillna(method="ffill")print('命名实体总数：%d' % comments.shape[0])Tags = li

Bert 结构详解1 Bert 模型结构图1，我们导入bert 14 分类model，并且打印出模型结构。https://blog.csdn.net/frank_zhaojianbo/article/details/107547338?spm=1001.2014.3001.5501图 2 是BertForSequenceClassification 模型的结构，可以看出 bert Model 有两

查看GPU版本和使用情况import torchif torch.cuda.is_available():device = torch.device("cuda")print('There are %d GPU(s) available.' % torch.cuda.device_count())print('We will use the GPU:', torch.cuda.get_device

查看GPU版本和使用情况import torchif torch.cuda.is_available():device = torch.device("cuda")print('There are %d GPU(s) available.' % torch.cuda.device_count())print('We will use the GPU:', torch.cuda.get_device

import torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.utils.data as tudfrom collections import Counterimport numpy as npimport randomimport mathimport pandas as pdimp...

import torchimport numpy as npimport torch.nn.functional as Ffrom torch.autograd import Variablefrom collections import Counterimport randomMAX_VOCAB_SIZE = 30000S = 'F:\\机器学习高级'path = S + "...