使用torchtext
的文本分类
原文:https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html
本教程说明如何使用torchtext
中的文本分类数据集,包括
- AG_NEWS,
- SogouNews,
- DBpedia,
- YelpReviewPolarity,
- YelpReviewFull,
- YahooAnswers,
- AmazonReviewPolarity,
- AmazonReviewFull
此示例显示了如何使用这些TextClassification
数据集之一训练用于分类的监督学习算法。
使用 N 元组加载数据
一袋 N 元组特征用于捕获有关本地单词顺序的一些部分信息。 在实践中,应用二元语法或三元语法作为单词组比仅一个单词提供更多的好处。 一个例子:
"load data with ngrams"
Bi-grams results: "load data", "data with", "with ngrams"
Tri-grams results: "load data with", "data with ngrams"
TextClassification
数据集支持ngrams
方法。 通过将ngrams
设置为 2,数据集中的示例文本将是一个单字加二元组字符串的列表。
import torch
import torchtext
from torchtext.datasets import text_classification
NGRAMS = 2
import os
if not os.path.isdir('./.data'):
os.mkdir('./.data')
train_dataset, test_dataset = text_classification.DATASETS['AG_NEWS'](
root='./.data', ngrams=NGRAMS, vocab=None)
BATCH_SIZE = 16
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
定义模型
该模型由EmbeddingBag
层和线性层组成(请参见下图)。 nn.EmbeddingBag
计算嵌入“袋”的平均值。 此处的文本条目具有不同的长度。 nn.EmbeddingBag
此处不需要填充,因为文本长度以偏移量保存。
另外,由于nn.EmbeddingBag
会动态累积嵌入中的平均值,因此nn.EmbeddingBag
可以提高性能和存储效率,以处理张量序列。
import torch.nn as nn
import torch.nn.functional as F
class TextSentiment(nn.Module):
def __init__(self, vocab_size, embed_dim, num_class):
super().__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)
启动实例
AG_NEWS
数据集具有四个标签,因此类别数是四个。
1 : World
2 : Sports
3 : Business
4 : Sci/Tec
词汇的大小等于词汇的长度(包括单个单词和 N 元组)。 类的数量等于标签的数量,在AG_NEWS
情况下为 4。
VOCAB_SIZE = len(train_dataset.get_vocab())
EMBED_DIM = 32
NUN_CLASS = len(train_dataset.get_labels())
model = TextSentiment(VOCAB_SIZE, EMBED_DIM, NUN_CLASS).to(device)
用于生成批量的函数
由于文本条目的长度不同,因此使用自定义函数generate_batch()
生成数据批和偏移量。 该函数被传递到torch.utils.data.DataLoader
中的collate_fn
。 collate_fn
的输入是张量列表,其大小为batch_size
,collate_fn
函数将它们打包成一个小批量。 请注意此处,并确保将collate_fn
声明为顶级def
。 这样可以确保该函数在每个工作程序中均可用。
原始数据批量输入中的文本条目打包到一个列表中,并作为单个张量级联,作为nn.EmbeddingBag
的输入。 偏移量是定界符的张量,表示文本张量中各个序列的起始索引。 Label
是一个张量,用于保存单个文本条目的标签。
def generate_batch(batch):
label = torch.tensor([entry[0] for entry in batch])
text = [entry[1] for entry in batch]
offsets = [0] + [len(entry) for entry in text]
# torch.Tensor.cumsum returns the cumulative sum
# of elements in the dimension dim.
# torch.Tensor([1.0, 2.0, 3.0]).cumsum(dim=0)
offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
text = torch.cat(text)
return text, offsets, label
定义函数来训练模型并评估结果
建议 PyTorch 用户使用torch.utils.data.DataLoader
,它可以轻松地并行加载数据(教程在这里)。 我们在此处使用DataLoader
加载AG_NEWS
数据集,并将其发送到模型以进行训练/验证。
from torch.utils.data import DataLoader
def train_func(sub_train_):
# Train the model
train_loss = 0
train_acc = 0
data = DataLoader(sub_train_, batch_size=BATCH_SIZE, shuffle=True,
collate_fn=generate_batch)
for i, (text, offsets, cls) in enumerate(data):
optimizer.zero_grad()
text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)
output = model(text, offsets)
loss = criterion(output, cls)
train_loss += loss.item()
loss.backward()
optimizer.step()
train_acc += (output.argmax(1) == cls).sum().item()
# Adjust the learning rate
scheduler.step()
return train_loss / len(sub_train_), train_acc / len(sub_train_)
def test(data_):
loss = 0
acc = 0
data = DataLoader(data_, batch_size=BATCH_SIZE, collate_fn=generate_batch)
for text, offsets, cls in data:
text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)
with torch.no_grad():
output = model(text, offsets)
loss = criterion(output, cls)
loss += loss.item()
acc += (output.argmax(1) == cls).sum().item()
return loss / len(data_), acc / len(data_)
分割数据集并运行模型
由于原始的AG_NEWS
没有有效的数据集,因此我们将训练数据集分为训练/有效集,其分割比率为 0.95(训练)和 0.05(有效)。 在这里,我们在 PyTorch 核心库中使用torch.utils.data.dataset.random_split
函数。
CrossEntropyLoss
标准将nn.LogSoftmax()
和nn.NLLLoss()
合并到一个类中。 在训练带有C
类的分类问题时很有用。 SGD
实现了随机梯度下降方法作为优化程序。 初始学习率设置为 4.0。 StepLR
在此处用于通过历时调整学习率。
import time
from torch.utils.data.dataset import random_split
N_EPOCHS = 5
min_valid_loss = float('inf')
criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=4.0)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.9)
train_len = int(len(train_dataset) * 0.95)
sub_train_, sub_valid_ = \
random_split(train_dataset, [train_len, len(train_dataset) - train_len])
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss, train_acc = train_func(sub_train_)
valid_loss, valid_acc = test(sub_valid_)
secs = int(time.time() - start_time)
mins = secs / 60
secs = secs % 60
print('Epoch: %d' %(epoch + 1), " | time in %d minutes, %d seconds" %(mins, secs))
print(f'\tLoss: {train_loss:.4f}(train)\t|\tAcc: {train_acc * 100:.1f}%(train)')
print(f'\tLoss: {valid_loss:.4f}(valid)\t|\tAcc: {valid_acc * 100:.1f}%(valid)')
出:
Epoch: 1 | time in 0 minutes, 11 seconds
Loss: 0.0262(train) | Acc: 84.7%(train)
Loss: 0.0002(valid) | Acc: 89.3%(valid)
Epoch: 2 | time in 0 minutes, 11 seconds
Loss: 0.0119(train) | Acc: 93.6%(train)
Loss: 0.0002(valid) | Acc: 89.6%(valid)
Epoch: 3 | time in 0 minutes, 11 seconds
Loss: 0.0069(train) | Acc: 96.3%(train)
Loss: 0.0000(valid) | Acc: 91.8%(valid)
Epoch: 4 | time in 0 minutes, 11 seconds
Loss: 0.0038(train) | Acc: 98.1%(train)
Loss: 0.0000(valid) | Acc: 91.5%(valid)
Epoch: 5 | time in 0 minutes, 11 seconds
Loss: 0.0022(train) | Acc: 99.0%(train)
Loss: 0.0000(valid) | Acc: 91.4%(valid)
使用以下信息在 GPU 上运行模型:
周期:1 | 时间在 0 分 11 秒内
Loss: 0.0263(train) | Acc: 84.5%(train)
Loss: 0.0001(valid) | Acc: 89.0%(valid)
周期:2 | 时间在 0 分钟 10 秒内
Loss: 0.0119(train) | Acc: 93.6%(train)
Loss: 0.0000(valid) | Acc: 89.6%(valid)
周期:3 | 时间在 0 分钟 9 秒内
Loss: 0.0069(train) | Acc: 96.4%(train)
Loss: 0.0000(valid) | Acc: 90.5%(valid)
周期:4 | 时间在 0 分 11 秒内
Loss: 0.0038(train) | Acc: 98.2%(train)
Loss: 0.0000(valid) | Acc: 90.4%(valid)
周期:5 | 时间在 0 分 11 秒内
Loss: 0.0022(train) | Acc: 99.0%(train)
Loss: 0.0000(valid) | Acc: 91.0%(valid)
使用测试数据集评估模型
print('Checking the results of test dataset...')
test_loss, test_acc = test(test_dataset)
print(f'\tLoss: {test_loss:.4f}(test)\t|\tAcc: {test_acc * 100:.1f}%(test)')
出:
Checking the results of test dataset...
Loss: 0.0002(test) | Acc: 90.9%(test)
正在检查测试数据集的结果…
Loss: 0.0237(test) | Acc: 90.5%(test)
测试随机新闻
使用到目前为止最好的模型并测试高尔夫新闻。 标签信息在这里。
import re
from torchtext.data.utils import ngrams_iterator
from torchtext.data.utils import get_tokenizer
ag_news_label = {1 : "World",
2 : "Sports",
3 : "Business",
4 : "Sci/Tec"}
def predict(text, model, vocab, ngrams):
tokenizer = get_tokenizer("basic_english")
with torch.no_grad():
text = torch.tensor([vocab[token]
for token in ngrams_iterator(tokenizer(text), ngrams)])
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."
vocab = train_dataset.get_vocab()
model = model.to("cpu")
print("This is a %s news" %ag_news_label[predict(ex_text_str, model, vocab, 2)])
出:
This is a Sports news
这是体育新闻
脚本的总运行时间:(1 分 38.483 秒)
下载 Python 源码:text_sentiment_ngrams_tutorial.py
下载 Jupyter 笔记本:text_sentiment_ngrams_tutorial.ipynb
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