Albert 预训练示例 - Python

Albert（A Lite BERT）是一个轻量版的BERT，采用了压缩方法和参数共享来减少参数数量，从而提高训练和推理的速度。在本文中，我们将介绍如何使用Python中的Albert预训练模型进行文本分类任务。

准备

我们需要安装transformers库，这里使用pip进行安装。

!pip install transformers

加载Albert预训练模型

加载阿里云提供的中文Albert预训练模型，可以通过AutoModelForSequenceClassification.from_pretrained()方法进行加载。

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("voidful/albert_chinese_tiny")
model = AutoModelForSequenceClassification.from_pretrained("voidful/albert_chinese_tiny")

数据处理

我们使用torchtext库来处理数据，torchtext可用于文本、图像和音频数据。

import torch
from torchtext.legacy.datasets import AG_NEWS
from torch.utils.data import DataLoader
from torchtext.legacy.data.utils import get_tokenizer
from torchtext.legacy.data.vocab import build_vocab_from_iterator
from torchtext.legacy.data import (DataLoader, Dataset, Example, Field,
                                    LabelField)

# 定义我们的文本和标签字段
tokenizer = get_tokenizer('basic_english')
TEXT = Field(tokenize=tokenizer, lower=True, sequential=True, use_vocab=True)
LABEL = LabelField(dtype=torch.float)

train_dataset, test_dataset = AG_NEWS(split=('train', 'test'))

def get_vocab(dataset):
    """从数据集中构建词汇表"""
    token_iterator = iter([tokenizer(item[1])
                           for item in dataset])
    vocab = build_vocab_from_iterator(token_iterator,
                                       specials=['<unk>', '<pad>', '<bos>', '<eos>'],
                                       specials_first=True)
    return vocab

# 为文本和标签定义批量化操作
train_batch_size = 32
test_batch_size = 256

TEXT.build_vocab(train_dataset, min_freq=2, max_size=int(1e4))
LABEL.build_vocab(train_dataset)

# 为训练数据和测试数据创建迭代器
train_iterator, test_iterator = (DataLoader(dataset=train_dataset,
                                            batch_size=train_batch_size,
                                            shuffle=True),
                                 DataLoader(dataset=test_dataset,
                                            batch_size=test_batch_size,
                                            shuffle=True))

模型训练

定义训练模型的函数，使用CrossEntropyLoss损失函数和随机梯度下降法进行模型优化。在每个epoch结束时，评估模型并打印出准确率。

import time
from torch import nn
from torch import optim

def train(model, iterator, optimizer, criterion):
    model.train()
    epoch_loss = 0

    for batch in iterator:
        text = batch[0]
        target = batch[1]
        target = target - 1

        optimizer.zero_grad()
        output = model(text)[0]
        loss = criterion(output, target.long())
        loss.backward()
        optimizer.step()

        epoch_loss += loss.item()

    return epoch_loss / len(iterator)

def evaluate(model, iterator, criterion):
    model.eval()
    epoch_loss = 0
    correct = 0

    with torch.no_grad():
        for batch in iterator:
            text = batch[0]
            target = batch[1]
            target = target - 1

            output = model(text)[0]
            loss = criterion(output, target.long())

            epoch_loss += loss.item()

            ## 计算准确率
            softmax = nn.Softmax(dim=1)
            pred = output.argmax(dim=1)
            target = target.type(torch.LongTensor)
            correct += (pred.eq(target).sum().item())

    acc = correct / len(iterator.dataset)
    return epoch_loss / len(iterator), acc

def run_training_loop(model, train_iterator, test_iterator, optimizer, criterion, num_epochs):
    for epoch in range(num_epochs):
        start_time = time.time()

        train_loss = train(model, train_iterator, optimizer, criterion)
        test_loss, test_acc = evaluate(model, test_iterator, criterion)

        end_time = time.time()

        epoch_mins, epoch_secs = int((end_time - start_time) / 60), int((end_time - start_time) % 60)

        print(f'Epoch: {epoch + 1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')
        print(f'\tTrain Loss: {train_loss:.3f}')
        print(f'\t● Test Loss: {test_loss:.3f} | Test Acc: {test_acc * 100:.2f}%\n')

optimizer = optim.SGD(model.parameters(), lr=1e-5)
criterion = nn.CrossEntropyLoss()

num_epochs = 5

run_training_loop(model, train_iterator, test_iterator, optimizer, criterion, num_epochs)

总结

通过本文，我们了解了如何使用Python中的Albert预训练模型进行文本分类任务。我们介绍了如何加载预训练模型，并使用torchtext库来处理数据。在训练模型时，我们采用了CrossEntropyLoss损失函数和随机梯度下降法进行模型优化。在每个epoch结束时，评估模型并打印出准确率。