简介

使用浅层网络构建新闻主题分类器。
以一段新闻报道中的文本描述内容为输入, 使用模型帮助我们判断它最有可能属于哪一种类型的新闻, 这是典型的文本分类问题, 我们这里假定每种类型是互斥的, 即文本描述有且只有一种类型。

导入相关的torch工具包

import timeimport torch
import torch.nn as nn
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
from TextClassificationModule import TextClassificationModule

访问原始数据集迭代器

torchtext 库提供了一些原始数据集迭代器，它们产生原始文本字符串。例如，AG_NEWS数据集迭代器将原始数据生成为标签和文本的元组。

# 可用设备检测, 有GPU的话将优先使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 基本的英文分词器
tokenizer = get_tokenizer('basic_english')
# 训练数据加载器
train_iter = AG_NEWS(split="train")
test_iter = AG_NEWS(split="test")

对读取到的数据进行测试，该读取的数据是从网上自动下载到缓存，其中读取到的 train_iter 和 test_iter 为训练集和测试集，且均为迭代器类型。

print('test:')
train_data = iter(train_iter)
test_data = iter(test_iter)
print(next(train_data))
print(next(test_data))

运行结果

test:
(3, "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green again.")
(3, "Fears for T N pension after talks Unions representing workers at Turner   Newall say they are 'disappointed' after talks with stricken parent firm Federal Mogul.")

使用原始训练数据集构建词汇表

其中分词生成器中的 “_” 表示一个不用的变量即类别，text 表示新闻文本，如：
_ = 3
text = Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green again.
python 中 yield 的作用就是把一个函数变成一个 generator，带有 yield 的函数不再是一个普通函数，Python 解释器会将其视为一个 generator，调用 fab(5) 不会执行 fab 函数，而是返回一个 iterable 对象。
示例

def yield_test(n):  for i in range(n):  yield call(i)  print("i=",i)  #做一些其它的事情print("do something.")      print("end.")  def call(i):  return i*2  #使用for循环
for i in yield_test(5):  print(i,",")

运行结果

0 ,
i= 0
2 ,
i= 1
4 ,
i= 2
6 ,
i= 3
8 ,
i= 4
do something.
end.

使用原始训练数据集构建词汇表

# 分词生成器
def yield_tokens(data_iter):for _, text in data_iter:yield tokenizer(text)# 根据训练数据构建词汇表，torchtext.vocab
vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["<unk>"])
# 设置默认索引，当某个单词不在词汇表 vocab 时（OOV)，返回该单词索引
vocab.set_default_index(vocab["<unk>"])# 词汇表会将 token 映射到词汇表中的索引上
print(vocab(["here", "is", "an", "example"]))# 构建数据加载器 dataloader
# text_pipeline 将一个文本字符串转换为整数 List, List 中每项对应词汇表 vocab 中的单词的索引号
text_pipeline = lambda x: vocab(tokenizer(x))# label_pipeline 将 label 转换为整数
label_pipeline = lambda x: int(x) - 1# pipeline example
print(text_pipeline("hello world! I'am happy"))
print(label_pipeline("10"))

运行结果

[475, 21, 30, 5297]
[12544, 50, 764, 282, 16, 1913, 2734]
9

生成数据批处理和迭代器

def collate_batch(batch):label_list, text_list, offsets = [], [], [0]for (_label, _text) in batch:label_list.append(label_pipeline(_label))processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)text_list.append(processed_text)offsets.append(processed_text.size(0))label_list = torch.tensor(label_list, dtype=torch.int64)offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)text_list = torch.cat(text_list)return label_list.to(device), text_list.to(device), offsets.to(device)# 加载数据集合，转换为张量
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)

定义模型

该模型由 nn.EmbeddingBag 层和用于分类目的的线性层组成。nn.EmbeddingBag 使用默认模式“mean”计算嵌入“bag”的平均值。尽管此处的文本条目具有不同的长度，但 nn.EmbeddingBag 模块在此处不需要填充，因为文本长度保存在偏移量中。
nn.EmbeddingBag 可以提高性能和内存效率以处理一系列张量。

import torch.nn as nnclass TextClassificationModule(nn.Module):def __init__(self, vocab_size, embed_dim, num_class):"""文本分类模型description: 类的初始化函数:param vocab_size: 整个语料包含的不同词汇总数:param embed_dim: 指定词嵌入的维度:param num_class: 文本分类的类别总数"""super(TextClassificationModule, self).__init__()# 实例化embedding层, sparse=True代表每次对该层求解梯度时, 只更新部分权重self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)# 实例化全连接层, 参数分别是embed_dim和num_classself.fc = nn.Linear(embed_dim, num_class)# 为各层初始化权重self.init_weights()def init_weights(self):"""初始化权重函数"""# 指定初始权重的取值范围数initrange = 0.5# 各层的权重参数都是初始化为均匀分布self.embedding.weight.data.uniform_(-initrange, initrange)self.fc.weight.data.uniform_(-initrange, initrange)# 偏置初始化为0self.fc.bias.data.zero_()def forward(self, text, offsets):""":param text: 文本数值映射后的结果:return: 与类别数尺寸相同的张量, 用以判断文本类别"""embedded = self.embedding(text, offsets)return self.fc(embedded)

定义函数来训练模型和评估结果

def train(dataloader):model.train()total_acc, total_count = 0, 0log_interval = 500start_time = time.time()for idx, (label, text, offsets) in enumerate(dataloader):optimizer.zero_grad()predicted_label = model(text, offsets)loss = criterion(predicted_label, label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)optimizer.step()total_acc += (predicted_label.argmax(1) == label).sum().item()total_count += label.size(0)if idx % log_interval == 0 and idx > 0:elapsed = time.time() - start_timeprint('| epoch {:3d} | {:5d}/{:5d} batches ''| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),total_acc / total_count))total_acc, total_count = 0, 0start_time = time.time()def evaluate(dataloader):model.eval()total_acc, total_count = 0, 0with torch.no_grad():for idx, (label, text, offsets) in enumerate(dataloader):predicted_label = model(text, offsets)loss = criterion(predicted_label, label)total_acc += (predicted_label.argmax(1) == label).sum().item()total_count += label.size(0)return total_acc / total_count

实例化并运行模型

# 加载数据集合，转换为张量
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)
# 一个嵌入维度为 64 的模型。词汇大小等于词汇实例的长度。类的数量等于标签的数量，
num_class = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
emsize = 64
model = TextClassificationModule(vocab_size, emsize, num_class).to(device)# 训练轮数
EPOCHS = 10
# 学习率
LR = 5
# 训练数据规模
BATCH_SIZE = 64
# 交叉熵损失函数
criterion = torch.nn.CrossEntropyLoss()
# 优化器
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
# 调整学习率机制
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
total_accu = None
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
# 划分训练集中5%的数据最为验证集
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = random_split(train_dataset, [num_train, len(train_dataset) - num_train])train_dataloader = DataLoader(split_train_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)for epoch in range(1, EPOCHS + 1):epoch_start_time = time.time()train(train_dataloader)accu_val = evaluate(valid_dataloader)if total_accu is not None and total_accu > accu_val:scheduler.step()else:total_accu = accu_valprint('-' * 59)print('| end of epoch {:3d} | time: {:5.2f}s | ''valid accuracy {:8.3f} '.format(epoch,time.time() - epoch_start_time,accu_val))print('-' * 59)

运行结果

| epoch   1 |   500/ 1782 batches | accuracy    0.689
| epoch   1 |  1000/ 1782 batches | accuracy    0.856
| epoch   1 |  1500/ 1782 batches | accuracy    0.873
-----------------------------------------------------------
| end of epoch   1 | time: 23.38s | valid accuracy    0.879
-----------------------------------------------------------
| epoch   2 |   500/ 1782 batches | accuracy    0.896
| epoch   2 |  1000/ 1782 batches | accuracy    0.904
| epoch   2 |  1500/ 1782 batches | accuracy    0.900
-----------------------------------------------------------
| end of epoch   2 | time: 32.21s | valid accuracy    0.891
-----------------------------------------------------------
| epoch   3 |   500/ 1782 batches | accuracy    0.915
| epoch   3 |  1000/ 1782 batches | accuracy    0.916
| epoch   3 |  1500/ 1782 batches | accuracy    0.915
-----------------------------------------------------------
| end of epoch   3 | time: 36.85s | valid accuracy    0.899
-----------------------------------------------------------
| epoch   4 |   500/ 1782 batches | accuracy    0.925
| epoch   4 |  1000/ 1782 batches | accuracy    0.925
| epoch   4 |  1500/ 1782 batches | accuracy    0.922
-----------------------------------------------------------
| end of epoch   4 | time: 20.15s | valid accuracy    0.897
-----------------------------------------------------------
| epoch   5 |   500/ 1782 batches | accuracy    0.937
| epoch   5 |  1000/ 1782 batches | accuracy    0.938
| epoch   5 |  1500/ 1782 batches | accuracy    0.936
-----------------------------------------------------------
| end of epoch   5 | time: 28.52s | valid accuracy    0.905
-----------------------------------------------------------
| epoch   6 |   500/ 1782 batches | accuracy    0.939
| epoch   6 |  1000/ 1782 batches | accuracy    0.938
| epoch   6 |  1500/ 1782 batches | accuracy    0.941
-----------------------------------------------------------
| end of epoch   6 | time: 33.47s | valid accuracy    0.905
-----------------------------------------------------------
| epoch   7 |   500/ 1782 batches | accuracy    0.940
| epoch   7 |  1000/ 1782 batches | accuracy    0.941
| epoch   7 |  1500/ 1782 batches | accuracy    0.939
-----------------------------------------------------------
| end of epoch   7 | time: 20.75s | valid accuracy    0.904
-----------------------------------------------------------
| epoch   8 |   500/ 1782 batches | accuracy    0.941
| epoch   8 |  1000/ 1782 batches | accuracy    0.941
| epoch   8 |  1500/ 1782 batches | accuracy    0.940
-----------------------------------------------------------
| end of epoch   8 | time: 27.11s | valid accuracy    0.906
-----------------------------------------------------------
| epoch   9 |   500/ 1782 batches | accuracy    0.942
| epoch   9 |  1000/ 1782 batches | accuracy    0.942
| epoch   9 |  1500/ 1782 batches | accuracy    0.942
-----------------------------------------------------------
| end of epoch   9 | time: 34.83s | valid accuracy    0.906
-----------------------------------------------------------
| epoch  10 |   500/ 1782 batches | accuracy    0.942
| epoch  10 |  1000/ 1782 batches | accuracy    0.942
| epoch  10 |  1500/ 1782 batches | accuracy    0.940
-----------------------------------------------------------
| end of epoch  10 | time: 22.78s | valid accuracy    0.906
-----------------------------------------------------------

使用测试数据集评估模型

print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(accu_test))

运行结果

Checking the results of test dataset.
test accuracy    0.906

测试随机新闻

# 测试随机新闻
# 使用迄今为止最好的模型并测试高尔夫新闻。
ag_news_label = {1: "World",2: "Sports",3: "Business",4: "Sci/Tec"}def predict(text, text_pipeline):with torch.no_grad():text = torch.tensor(text_pipeline(text))output = model(text, torch.tensor([0]))return output.argmax(1).item() + 1ex_text_str = "MEMPHIS, Tenn. – Four days ago, Jon Rahm was \enduring the season’s worst weather conditions on Sunday at The \Open on his way to a closing 75 at Royal Portrush, which \considering the wind and the rain was a respectable showing. \Thursday’s first round at the WGC-FedEx St. Jude Invitational \was another story. With temperatures in the mid-80s and hardly any \wind, the Spaniard was 13 strokes better in a flawless round. \Thanks to his best putting performance on the PGA Tour, Rahm \finished with an 8-under 62 for a three-stroke lead, which \was even more impressive considering he’d never played the \front nine at TPC Southwind."model = model.to("cpu")print("This is a %s news" % ag_news_label[predict(ex_text_str, text_pipeline)])

运行结果

This is a Sports news

完整代码

import timeimport torch
import torch.nn as nn
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset# 可用设备检测, 有GPU的话将优先使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 基本的英文分词器
tokenizer = get_tokenizer('basic_english')
# 训练数据加载器
train_iter = AG_NEWS(split="train")
test_iter = AG_NEWS(split="test")# print('test:')
# train_data = iter(train_iter)
# test_data = iter(test_iter)
# print(next(train_data))
# print(next(test_data))# 分词生成器
def yield_tokens(data_iter):for _, text in data_iter:yield tokenizer(text)# 根据训练数据构建词汇表
vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["<unk>"])
# 设置默认索引，当某个单词不在词汇表 vocab 时（OOV)，返回该单词索引
vocab.set_default_index(vocab["<unk>"])# 词汇表会将 token 映射到词汇表中的索引上
# print(vocab(["here", "is", "an", "example"]))# 构建数据加载器 dataloader
# text_pipeline 将一个文本字符串转换为整数 List, List 中每项对应词汇表 vocab 中的单词的索引号
text_pipeline = lambda x: vocab(tokenizer(x))# label_pipeline 将 label 转换为整数
label_pipeline = lambda x: int(x) - 1# pipeline example
# print(text_pipeline("hello world! I'am happy"))
# print(label_pipeline("10"))# 模型
class TextClassificationModule(nn.Module):def __init__(self, vocab_size, embed_dim, num_class):"""文本分类模型description: 类的初始化函数:param vocab_size: 整个语料包含的不同词汇总数:param embed_dim: 指定词嵌入的维度:param num_class: 文本分类的类别总数"""super(TextClassificationModule, self).__init__()# 实例化embedding层, sparse=True代表每次对该层求解梯度时, 只更新部分权重self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)# 实例化全连接层, 参数分别是embed_dim和num_classself.fc = nn.Linear(embed_dim, num_class)# 为各层初始化权重self.init_weights()def init_weights(self):"""初始化权重函数"""# 指定初始权重的取值范围数initrange = 0.5# 各层的权重参数都是初始化为均匀分布self.embedding.weight.data.uniform_(-initrange, initrange)self.fc.weight.data.uniform_(-initrange, initrange)# 偏置初始化为0self.fc.bias.data.zero_()def forward(self, text, offsets):""":param text: 文本数值映射后的结果:return: 与类别数尺寸相同的张量, 用以判断文本类别"""embedded = self.embedding(text, offsets)return self.fc(embedded)def collate_batch(batch):label_list, text_list, offsets = [], [], [0]for (_label, _text) in batch:label_list.append(label_pipeline(_label))processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)text_list.append(processed_text)offsets.append(processed_text.size(0))label_list = torch.tensor(label_list, dtype=torch.int64)offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)text_list = torch.cat(text_list)return label_list.to(device), text_list.to(device), offsets.to(device)def train(dataloader):model.train()total_acc, total_count = 0, 0log_interval = 500start_time = time.time()for idx, (label, text, offsets) in enumerate(dataloader):optimizer.zero_grad()predicted_label = model(text, offsets)loss = criterion(predicted_label, label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)optimizer.step()total_acc += (predicted_label.argmax(1) == label).sum().item()total_count += label.size(0)if idx % log_interval == 0 and idx > 0:elapsed = time.time() - start_timeprint('| epoch {:3d} | {:5d}/{:5d} batches ''| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),total_acc / total_count))total_acc, total_count = 0, 0start_time = time.time()def evaluate(dataloader):model.eval()total_acc, total_count = 0, 0with torch.no_grad():for idx, (label, text, offsets) in enumerate(dataloader):predicted_label = model(text, offsets)loss = criterion(predicted_label, label)total_acc += (predicted_label.argmax(1) == label).sum().item()total_count += label.size(0)return total_acc / total_count# 加载数据集合，转换为张量
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)
# 一个嵌入维度为 64 的模型。词汇大小等于词汇实例的长度。类的数量等于标签的数量，
num_class = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
emsize = 64
model = TextClassificationModule(vocab_size, emsize, num_class).to(device)# 训练轮数
EPOCHS = 10
# 学习率
LR = 5
# 训练数据规模
BATCH_SIZE = 64
# 交叉熵损失函数
criterion = torch.nn.CrossEntropyLoss()
# 优化器
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
# 调整学习率机制
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
total_accu = None
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)# 划分训练集中5%的数据最为验证集
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = random_split(train_dataset, [num_train, len(train_dataset) - num_train])train_dataloader = DataLoader(split_train_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)for epoch in range(1, EPOCHS + 1):epoch_start_time = time.time()train(train_dataloader)accu_val = evaluate(valid_dataloader)if total_accu is not None and total_accu > accu_val:scheduler.step()else:total_accu = accu_valprint('-' * 59)print('| end of epoch {:3d} | time: {:5.2f}s | ''valid accuracy {:8.3f} '.format(epoch,time.time() - epoch_start_time,accu_val))print('-' * 59)'''使用测试数据集评估模型'''
print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(accu_test))# 测试随机新闻
# 使用迄今为止最好的模型并测试高尔夫新闻。
ag_news_label = {1: "World",2: "Sports",3: "Business",4: "Sci/Tec"}def predict(text, text_pipeline):with torch.no_grad():text = torch.tensor(text_pipeline(text))output = model(text, torch.tensor([0]))return output.argmax(1).item() + 1ex_text_str = "MEMPHIS, Tenn. – Four days ago, Jon Rahm was \enduring the season’s worst weather conditions on Sunday at The \Open on his way to a closing 75 at Royal Portrush, which \considering the wind and the rain was a respectable showing. \Thursday’s first round at the WGC-FedEx St. Jude Invitational \was another story. With temperatures in the mid-80s and hardly any \wind, the Spaniard was 13 strokes better in a flawless round. \Thanks to his best putting performance on the PGA Tour, Rahm \finished with an 8-under 62 for a three-stroke lead, which \was even more impressive considering he’d never played the \front nine at TPC Southwind."model = model.to("cpu")print("This is a %s news" % ag_news_label[predict(ex_text_str, text_pipeline)])

运行结果

| epoch   1 |   500/ 1782 batches | accuracy    0.689
| epoch   1 |  1000/ 1782 batches | accuracy    0.856
| epoch   1 |  1500/ 1782 batches | accuracy    0.873
-----------------------------------------------------------
| end of epoch   1 | time: 23.38s | valid accuracy    0.879
-----------------------------------------------------------
| epoch   2 |   500/ 1782 batches | accuracy    0.896
| epoch   2 |  1000/ 1782 batches | accuracy    0.904
| epoch   2 |  1500/ 1782 batches | accuracy    0.900
-----------------------------------------------------------
| end of epoch   2 | time: 32.21s | valid accuracy    0.891
-----------------------------------------------------------
| epoch   3 |   500/ 1782 batches | accuracy    0.915
| epoch   3 |  1000/ 1782 batches | accuracy    0.916
| epoch   3 |  1500/ 1782 batches | accuracy    0.915
-----------------------------------------------------------
| end of epoch   3 | time: 36.85s | valid accuracy    0.899
-----------------------------------------------------------
| epoch   4 |   500/ 1782 batches | accuracy    0.925
| epoch   4 |  1000/ 1782 batches | accuracy    0.925
| epoch   4 |  1500/ 1782 batches | accuracy    0.922
-----------------------------------------------------------
| end of epoch   4 | time: 20.15s | valid accuracy    0.897
-----------------------------------------------------------
| epoch   5 |   500/ 1782 batches | accuracy    0.937
| epoch   5 |  1000/ 1782 batches | accuracy    0.938
| epoch   5 |  1500/ 1782 batches | accuracy    0.936
-----------------------------------------------------------
| end of epoch   5 | time: 28.52s | valid accuracy    0.905
-----------------------------------------------------------
| epoch   6 |   500/ 1782 batches | accuracy    0.939
| epoch   6 |  1000/ 1782 batches | accuracy    0.938
| epoch   6 |  1500/ 1782 batches | accuracy    0.941
-----------------------------------------------------------
| end of epoch   6 | time: 33.47s | valid accuracy    0.905
-----------------------------------------------------------
| epoch   7 |   500/ 1782 batches | accuracy    0.940
| epoch   7 |  1000/ 1782 batches | accuracy    0.941
| epoch   7 |  1500/ 1782 batches | accuracy    0.939
-----------------------------------------------------------
| end of epoch   7 | time: 20.75s | valid accuracy    0.904
-----------------------------------------------------------
| epoch   8 |   500/ 1782 batches | accuracy    0.941
| epoch   8 |  1000/ 1782 batches | accuracy    0.941
| epoch   8 |  1500/ 1782 batches | accuracy    0.940
-----------------------------------------------------------
| end of epoch   8 | time: 27.11s | valid accuracy    0.906
-----------------------------------------------------------
| epoch   9 |   500/ 1782 batches | accuracy    0.942
| epoch   9 |  1000/ 1782 batches | accuracy    0.942
| epoch   9 |  1500/ 1782 batches | accuracy    0.942
-----------------------------------------------------------
| end of epoch   9 | time: 34.83s | valid accuracy    0.906
-----------------------------------------------------------
| epoch  10 |   500/ 1782 batches | accuracy    0.942
| epoch  10 |  1000/ 1782 batches | accuracy    0.942
| epoch  10 |  1500/ 1782 batches | accuracy    0.940
-----------------------------------------------------------
| end of epoch  10 | time: 22.78s | valid accuracy    0.906
-----------------------------------------------------------
Checking the results of test dataset.
test accuracy    0.906
This is a Sports newsProcess finished with exit code 0

参考链接

https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html

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新闻主题分类任务——torchtext 库进行文本分类

目录

简介

导入相关的torch工具包

访问原始数据集迭代器

使用原始训练数据集构建词汇表

生成数据批处理和迭代器

定义模型

定义函数来训练模型和评估结果

实例化并运行模型

使用测试数据集评估模型

测试随机新闻

完整代码

参考链接

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