自然语言处理笔记总目录
关于新闻主题分类任务： 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
本案例取自Pytorch官网的：TEXT CLASSIFICATION WITH THE TORCHTEXT LIBRARY，在此基础上增加了完整的注释以及通俗的讲解
本案例分为以下九个步骤
Step 1：Access to the raw dataset iterators
AG_NEWS数据集介绍：
AG_NEWS：新闻语料库，包含4个大类新闻：World、Sports、Business、Sci/Tec。 AG_NEWS共包含120000条训练样本集（train.csv)， 7600测试样本数据集(test.csv)。每个类别分别拥有 30000 个训练样本及 1900 个测试样本。
import torch
from torchtext.datasets import AG_NEWS
train_iter = AG_NEWS(split='train')
返回的是一个训练集的迭代器，通过以下方法可以查看训练集的内容：
next(train_iter)
>>> (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.")
next(train_iter)
>>> (3, 'Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private
investment firm Carlyle Group,\\which has a reputation for making well-timed
and occasionally\\controversial plays in the defense industry, has quietly
placed\\its bets on another part of the market.')
Step 2：Prepare data processing pipelines
在训练之前，首先我们要处理新闻数据，对文本进行分词，构建词汇表vocab
使用get_tokenizer进行分词，同时build_vocab_from_iterator提供了使用迭代器构建词汇表的方法
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
tokenizer = get_tokenizer('basic_english') # 基本的英文分词器
train_iter = AG_NEWS(split='train') # 训练数据迭代器
# 分词生成器
def yield_tokens(data_iter):
for _, text in data_iter:
yield tokenizer(text)
# 构建词汇表
vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["
# 设置默认索引，当某个单词不在词汇表中，则返回0
vocab.set_default_index(vocab["
vocab(['here', 'is', 'an', 'example'])
>>> [475, 21, 30, 5286]
print(vocab(["haha", "hehe", "xixi"]))
>>> [0, 0, 0]
接下来使用分词器以及词汇表构建Pipeline
text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1
text_pipeline('here is an example')
>>> [475, 21, 30, 5286]
label_pipeline('10')
>>> 9
Step 3：Generate data batch and iterator
from torch.utils.data import DataLoader
# 使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 定义collate_batch函数，在DataLoader中会使用，对传入的样本数据进行批量处理
def collate_batch(batch):
# 存放label以及text的列表，offses存放每条text的偏移量
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)
# 将每一条数据的长度放入offsets列表当中
offsets.append(processed_text.size(0))
label_list = torch.tensor(label_list, dtype=torch.int64)
# 计算出每一条text的偏移量
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)
train_iter = AG_NEWS(split='train')
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)
cumsum()用于计算一个数组各行的累加值，示例如下：
>>>a = [1, 2, 3, 4, 5, 6, 7]
>>>cumsum(a)
array([1, 3, 6, 10, 15, 21, 28])
Step 4：Define the model
定义神经网络模型： 由EmbeddingBag、隐藏层和全连接层组成
from torch import nn
class TextClassificationModel(nn.Module):
def __init__(self, vocab_size, embed_dim, num_class):
super(TextClassificationModel, self).__init__()
self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)
self.fc = nn.Linear(embed_dim, num_class)
self.init_weights()
def init_weights(self):
initrange = 0.5
self.embedding.weight.data.uniform_(-initrange, initrange)
self.fc.weight.data.uniform_(-initrange, initrange)
self.fc.bias.data.zero_()
def forward(self, text, offsets):
embedded = self.embedding(text, offsets)
return self.fc(embedded)
Step 5：Initiate an instance
AG_NEWS 数据集有四个标签，因此类的数量是四个
1 : World
2 : Sports
3 : Business
4 : Sci/Tec
实例一个模型
train_iter = AG_NEWS(split='train')
num_class = len(set([label for (label, text) in train_iter])) # 获取分类数量
vocab_size = len(vocab) # 词汇表大小
emsize = 64 # 词嵌入维度
model = TextClassificationModel(vocab_size, emsize, num_class).to(device)
Step 6：Define functions to train the model and evaluate results
import time
def train(dataloader):
model.train()
total_acc, total_count = 0, 0
log_interval = 500
start_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_time
print('| epoch {:3d} | {:5d}/{:5d} batches '
'| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),
total_acc/total_count))
total_acc, total_count = 0, 0
start_time = time.time()
def evaluate(dataloader):
model.eval()
total_acc, total_count = 0, 0
with 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
梯度裁剪 torch.nn.utils.clip_grad_norm_() 的使用应该在loss.backward()之后，optimizer.step()之前. 注意这个方法只在训练的时候使用，在测试的时候验证和测试的时候不用。
Step 7：Split the dataset and run the model
拆分训练集：拆分比率为训练集95%，验证集5%，使用torch.utils.data.dataset.random_split函数
to_map_style_dataset函数是将数据集从iterator变为map的形式，可以直接索引
from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
# Hyperparameters
EPOCHS = 10 # epoch
LR = 5 # learning rate
BATCH_SIZE = 64 # batch size for training
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
total_accu = None
train_iter, test_iter = AG_NEWS()
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
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_val
print('-' * 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)
输出：
| epoch 1 | 500/ 1782 batches | accuracy 0.689
| epoch 1 | 1000/ 1782 batches | accuracy 0.856
| epoch 1 | 1500/ 1782 batches | accuracy 0.876
-----------------------------------------------------------
| end of epoch 1 | time: 8.17s | valid accuracy 0.882
-----------------------------------------------------------
| epoch 2 | 500/ 1782 batches | accuracy 0.897
| epoch 2 | 1000/ 1782 batches | accuracy 0.904
| epoch 2 | 1500/ 1782 batches | accuracy 0.900
-----------------------------------------------------------
| end of epoch 2 | time: 8.39s | valid accuracy 0.893
-----------------------------------------------------------
| epoch 3 | 500/ 1782 batches | accuracy 0.914
| epoch 3 | 1000/ 1782 batches | accuracy 0.916
| epoch 3 | 1500/ 1782 batches | accuracy 0.913
-----------------------------------------------------------
| end of epoch 3 | time: 8.44s | valid accuracy 0.903
-----------------------------------------------------------
| epoch 4 | 500/ 1782 batches | accuracy 0.924
| epoch 4 | 1000/ 1782 batches | accuracy 0.923
| epoch 4 | 1500/ 1782 batches | accuracy 0.924
-----------------------------------------------------------
| end of epoch 4 | time: 8.43s | valid accuracy 0.908
-----------------------------------------------------------
| epoch 5 | 500/ 1782 batches | accuracy 0.932
| epoch 5 | 1000/ 1782 batches | accuracy 0.930
| epoch 5 | 1500/ 1782 batches | accuracy 0.926
-----------------------------------------------------------
| end of epoch 5 | time: 8.37s | valid accuracy 0.903
-----------------------------------------------------------
| epoch 6 | 500/ 1782 batches | accuracy 0.941
| epoch 6 | 1000/ 1782 batches | accuracy 0.943
| epoch 6 | 1500/ 1782 batches | accuracy 0.941
-----------------------------------------------------------
| end of epoch 6 | time: 8.14s | valid accuracy 0.908
-----------------------------------------------------------
| epoch 7 | 500/ 1782 batches | accuracy 0.944
| epoch 7 | 1000/ 1782 batches | accuracy 0.942
| epoch 7 | 1500/ 1782 batches | accuracy 0.944
-----------------------------------------------------------
| end of epoch 7 | time: 8.15s | valid accuracy 0.907
-----------------------------------------------------------
| epoch 8 | 500/ 1782 batches | accuracy 0.943
| epoch 8 | 1000/ 1782 batches | accuracy 0.943
| epoch 8 | 1500/ 1782 batches | accuracy 0.945
-----------------------------------------------------------
| end of epoch 8 | time: 8.15s | valid accuracy 0.907
-----------------------------------------------------------
| epoch 9 | 500/ 1782 batches | accuracy 0.943
| epoch 9 | 1000/ 1782 batches | accuracy 0.944
| epoch 9 | 1500/ 1782 batches | accuracy 0.945
-----------------------------------------------------------
| end of epoch 9 | time: 8.15s | valid accuracy 0.907
-----------------------------------------------------------
| epoch 10 | 500/ 1782 batches | accuracy 0.943
| epoch 10 | 1000/ 1782 batches | accuracy 0.944
| epoch 10 | 1500/ 1782 batches | accuracy 0.945
-----------------------------------------------------------
| end of epoch 10 | time: 8.15s | valid accuracy 0.907
-----------------------------------------------------------
Step 8：Evaluate the model with test dataset
检验模型在测试集上的效能
print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(accu_test))
输出：
Checking the results of test dataset.
test accuracy 0.909
Step 9：Test on a random news
随机输入一段新闻，测试模型效果：
ag_news_label = {1: "World",
2: "Sports",
3: "Business",
4: "Sci/Tec"}
def predict(text, pipeline):
with torch.no_grad():
text = torch.tensor(pipeline(text))
output = model(text, torch.tensor([0]))
return output.argmax(1).item() + 1
ex_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')
res = predict(ex_text_str, text_pipeline)
print("This is a %s news" % ag_news_label[res])
结果：
This is a Sports news