TEXT CLASSIFICATION WITH TORCHTEXT

本文译自PYTORCH官网TEXT系列。本节主要利用torchtext中的文本分类数据集，包括：

这个例子展示了如何利用这些TextClassfication数据集中的一个来训练监督学习算法。

 

用ngrams加载数据

一个ngrams包特性被用来捕获一些关于本地词序的部分信息。在实际应用中，双字元（bi-gram）或三字元(tri-gram)作为词组比只使用一个词更有益处。例如：

TextClassfication 数据集支持ngrams方法。通过设定ngrams为2，数据集中的text将包含单个单词和bi-grams字符串。

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

模型由EmbeddingBag层和线性层组成。nn.EmbeddingBag计算embedding包的均值。不同的文本长度是不同的。nn.EmbeddingBag无需padding，因为文本长度在offsets已保存。

此外，因为nn.EmbeddingBag动态累加嵌入的平均值，nn.EmbeddingBag动可以提高性能和记忆效率来处理tensor序列。

一个例子

AG_NEWS数据集含有四个标签，因此类别数为4。

 vocab大小等于vocab长度(包括单词和ngrams)。类别数目等于标签数，在AG_NEWS这个例子中为4。

 

批量生成

由于文本长度不同，一个定制的generate_batch()用来生成批量和offsets。该函数传到collate_fn里面吗（不会用collate_fn函数的参考这里->）。collate_fn的输入为：batch_size大小的列表，将其大包围mini-batch。整个文本输入批量被整理为list，并连接作为单一tensor作为nn.EmbeddingBag的输入。offset是一个tensor表征文本tensor中队里序列的起始索引。Label是tensor保留了整个文本的标签。

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

 

函数定义与模型训练评估

推荐pytorch boys利用torch.utils.data.DataLoader来整，并且并行处理也方便（a tutorial is here）。我们利用DataLoader来加载AG_NEWS数据集并用来训练/评估。

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_)

 

划分数据集并训练模型

因为原始AG_NEWS数据没有验证集，将训练集分为训练/验证集合，比例为0.95/0.05。利用pytorch库中的 torch.utils.data.dataset.random_spllt实现。利用CrossEntropyLoss集合 nn.LogSoftmax() and nn.NLLLoss() 在一个类里。对于训练C个类别的分类任务是有用的。SGD作为优化器。初始学习率为4.0。StepLR用来调整学习率。

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)')

 

 

利用测试集评估

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)')

 

随机进行测试

利用最好的模型进行测试，标签信息：

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)])