Skip to content

使用 torchtext 库进行文本分类

译者:片刻小哥哥 人工校正:xiaoxstz

项目地址:https://pytorch.apachecn.org/2.0/tutorials/beginner/text_sentiment_ngrams_tutorial

原始地址:https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html

在本教程中,我们将展示如何使用 torchtext 库构建用于文本分类分析的数据集。用户可以灵活地

  • 作为迭代器访问原始数据
  • 构建数据处理管道,将原始文本字符串转换为torch.Tensor可用于训练模型
  • 使用torch.utils.data.DataLoader

先决条件

在运行本教程之前,需要安装最新 2.x 版本的 portalocker 软件包。例如,在 Colab 环境中,可以通过在顶部添加以下行来完成脚本:

!pip install -U portalocker>=2.0.0`

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,可生成原始文本字符串。例如, AG_NEWS 数据集迭代器生成作为标签和文本元组形式的原始数据。

要访问 torchtext 数据集,请按照以下位置的说明安装 torchdata https://github.com/pytorch/data

import torch
from torchtext.datasets import AG_NEWS

train_iter = iter(AG_NEWS(split="train"))

输出:

next(train_iter)
>>> (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.")

next(train_iter)
>>> (4, "The Race is On: Second Private Team Sets Launch Date for Human
Spaceflight (SPACE.com) SPACE.com - TORONTO, Canada -- A second\\team of
rocketeers competing for the  #36;10 million Ansari X Prize, a contest
for\\privately funded suborbital space flight, has officially announced
the first\\launch date for its manned rocket.")

next(train_iter)
>>> (4, 'Ky. Company Wins Grant to Study Peptides (AP) AP - A company founded
by a chemistry researcher at the University of Louisville won a grant to develop
a method of producing better peptides, which are short chains of amino acids, the
building blocks of proteins.')

准备数据处理管道

我们重新审视了 torchtext 库的非常基本的组件,包括词汇、词向量、分词器。这些是原始文本字符串的基本数据处理构建块。

这是使用分词器和词汇进行典型 NLP 数据处理的示例。第一步是使用原始训练数据集构建词汇表。这里我们使用内置工厂函数build_vocab_from_iterator 接受生成列表或标记迭代器的迭代器。用户还可以传递要添加到词汇表中的任何特殊符号。

from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator

tokenizer = get_tokenizer("basic_english")
train_iter = AG_NEWS(split="train")


def yield_tokens(data_iter):
    for _, text in data_iter:
        yield tokenizer(text)


vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])

词汇块将标记列表转换为整数。

vocab(['here', 'is', 'an', 'example'])
>>> [475, 21, 30, 5297]

使用分词器和词汇表准备文本处理管道。文本和标签管道将用于处理来自数据集迭代器的原始数据字符串。

text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1

文本管道根据词汇表中定义的查找表将文本字符串转换为整数列表。标签管道将标签转换为整数。例如,

text_pipeline('here is the an example')
>>> [475, 21, 2, 30, 5297]
label_pipeline('10')
>>> 9

生成数据批次和迭代器

torch.utils.data.DataLoader 建议 PyTorch 用户使用(教程是此处 )。它适用于实现getitem()和的地图样式数据集n len()协议,表示从索引/键到数据样本的映射。它还适用于 shuffle 参数为False的可迭代数据集。

在发送到模型之前, collat​​e_fn函数会处理从 DataLoader生成的一批样本。 collat​​e_fn的输入是一批数据,其批量大小为 DataLoadercollat​​e_fn.根据数据处理管道对其进行处理之前声明过。请注意此处并确保collat​​e_fn 被声明为顶级定义。这可确保该函数在每个工作线程中都可用。

在此示例中,原始数据批量输入中的文本条目被打包到一个列表中,并连接为单个tensor,用于 nn.EmbeddingBag的输入。偏移量是分隔符tensor,表示文本tensor中各个序列的起始索引。标签是一个tensor,保存各个文本条目的标签。

from torch.utils.data import DataLoader

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


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)


train_iter = AG_NEWS(split="train")
dataloader = DataLoader(
    train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch
)

定义模型

该模型由 nn.EmbeddingBag 层加上一个线性层组成分类目的。 nn.EmbeddingBag 默认模式为 “mean” 计算 “bag” 嵌入的平均值。尽管此处的文本条目具有不同的长度, nn.EmbeddingBag 模块不需要此处填充,因为文本长度保存在偏移量中。

此外,由于 nn.EmbeddingBag 会动态累积嵌入的平均值, nn.EmbeddingBag 可以提高处理tensor序列的性能和内存效率。

https://pytorch.org/tutorials/_images/text_sentiment_ngrams_model.png

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=False)
        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 数据集有四个标签,因此类别数为四个。

1 : World
2 : Sports
3 : Business
4 : Sci/Tec

我们构建一个嵌入维度为 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,

train_iter = AG_NEWS(split="train")
num_class = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
emsize = 64
model = TextClassificationModel(vocab_size, emsize, num_class).to(device)

定义函数来训练模型并评估结果。

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

拆分数据集并运行模型

由于原始 AG_NEWS 没有有效数据集,因此我们将训练数据集分为训练集/有效集,分割比为 0.95(训练)和0.05(有效)。这里我们使用 torch.utils.data.dataset.random_split PyTorch 核心库中的函数。

CrossEntropyLoss 标准结合 nn.LogSoftmax()nn.NLLLoss() 在单个类中。在使用 C 类训练分类问题时非常有用。 SGD 实现随机梯度下降法作为优化器。初始 学习率设置为5.0。 StepLR 此处用于调整学习率历经纪元。

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    0.694
| epoch   1 |  1000/ 1782 batches | accuracy    0.856
| epoch   1 |  1500/ 1782 batches | accuracy    0.877
-----------------------------------------------------------
| end of epoch   1 | time: 11.00s | valid accuracy    0.886
-----------------------------------------------------------
| epoch   2 |   500/ 1782 batches | accuracy    0.898
| epoch   2 |  1000/ 1782 batches | accuracy    0.899
| epoch   2 |  1500/ 1782 batches | accuracy    0.906
-----------------------------------------------------------
| end of epoch   2 | time: 10.60s | valid accuracy    0.895
-----------------------------------------------------------
| epoch   3 |   500/ 1782 batches | accuracy    0.916
| epoch   3 |  1000/ 1782 batches | accuracy    0.913
| epoch   3 |  1500/ 1782 batches | accuracy    0.915
-----------------------------------------------------------
| end of epoch   3 | time: 10.63s | valid accuracy    0.894
-----------------------------------------------------------
| epoch   4 |   500/ 1782 batches | accuracy    0.930
| epoch   4 |  1000/ 1782 batches | accuracy    0.932
| epoch   4 |  1500/ 1782 batches | accuracy    0.929
-----------------------------------------------------------
| end of epoch   4 | time: 10.61s | valid accuracy    0.902
-----------------------------------------------------------
| epoch   5 |   500/ 1782 batches | accuracy    0.932
| epoch   5 |  1000/ 1782 batches | accuracy    0.933
| epoch   5 |  1500/ 1782 batches | accuracy    0.931
-----------------------------------------------------------
| end of epoch   5 | time: 10.59s | valid accuracy    0.902
-----------------------------------------------------------
| epoch   6 |   500/ 1782 batches | accuracy    0.933
| epoch   6 |  1000/ 1782 batches | accuracy    0.932
| epoch   6 |  1500/ 1782 batches | accuracy    0.935
-----------------------------------------------------------
| end of epoch   6 | time: 10.57s | valid accuracy    0.903
-----------------------------------------------------------
| epoch   7 |   500/ 1782 batches | accuracy    0.934
| epoch   7 |  1000/ 1782 batches | accuracy    0.933
| epoch   7 |  1500/ 1782 batches | accuracy    0.935
-----------------------------------------------------------
| end of epoch   7 | time: 10.56s | valid accuracy    0.903
-----------------------------------------------------------
| epoch   8 |   500/ 1782 batches | accuracy    0.935
| epoch   8 |  1000/ 1782 batches | accuracy    0.933
| epoch   8 |  1500/ 1782 batches | accuracy    0.935
-----------------------------------------------------------
| end of epoch   8 | time: 10.59s | valid accuracy    0.904
-----------------------------------------------------------
| epoch   9 |   500/ 1782 batches | accuracy    0.934
| epoch   9 |  1000/ 1782 batches | accuracy    0.934
| epoch   9 |  1500/ 1782 batches | accuracy    0.934
-----------------------------------------------------------
| end of epoch   9 | time: 10.63s | valid accuracy    0.904
-----------------------------------------------------------
| epoch  10 |   500/ 1782 batches | accuracy    0.934
| epoch  10 |  1000/ 1782 batches | accuracy    0.936
| epoch  10 |  1500/ 1782 batches | accuracy    0.933
-----------------------------------------------------------
| end of epoch  10 | time: 10.62s | valid accuracy    0.905
-----------------------------------------------------------

使用测试数据集评估模型

检查测试数据集的结果…

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    0.907

对随机新闻进行测试

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() + 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")

print("This is a %s news" % ag_news_label[predict(ex_text_str, text_pipeline)])

输出:

This is a Sports news

脚本总运行时间: ( 1 分 58.358 秒)



回到顶部