使用torchtext的文本分类

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。

../_img/text_sentiment_ngrams_model.png

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)])