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作者 |DR. VAIBHAV KUMAR
编译 |VK
起源 |Analytics In Diamag
文本分类是自然语言解决的重要利用之一。在机器学习中有多种办法能够对文本进行分类。然而这些分类技术大多须要大量的预处理和大量的计算资源。在这篇文章中，咱们应用 PyTorch 来进行多类文本分类，因为它有如下长处：
- PyTorch 提供了一种弱小的办法来实现简单的模型体系结构和算法，其预处理量绝对较少，计算资源（包含执行工夫）的耗费也较少。
- PyTorch 的根本单元是张量，它具备在运行时扭转架构和跨 gpu 散布训练的长处。
- PyTorch 提供了一个名为 TorchText 的弱小库，其中蕴含用于预处理文本的脚本和一些风行的 NLP 数据集的源代码。
在本文中，咱们将应用 TorchText 演示多类文本分类，TorchText 是 PyTorch 中一个弱小的自然语言解决库。
对于这种分类，将应用由 EmbeddingBag 层和线性层组成的模型。EmbeddingBag 通过计算嵌入的平均值来解决长度可变的文本条目。
这个模型将在 DBpedia 数据集上进行训练，其中文本属于 14 个类。训练胜利后，模型将预测输出文本的类标签。
DBpedia 数据集
DBpedia 是自然语言解决畛域中风行的基准数据集。它蕴含 14 个类别的文本，如公司、教育机构、艺术家、电影等。
它实际上是从维基百科我的项目创立的信息中提取的结构化内容集。TorchText 提供的 DBpedia 数据集有 63000 个属于 14 个类的文本实例。它包含 5600 个训练实例和 70000 个测试实例。
用 TorchText 实现文本分类
首先，咱们须要装置最新版本的 TorchText。
!pip install torchtext==0.4
之后，咱们将导入所有必须的库。
import torch
import torchtext
from torchtext.datasets import text_classification
import os
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import time
from torch.utils.data.dataset import random_split
import re
from torchtext.data.utils import ngrams_iterator
from torchtext.data.utils import get_tokenizer在下一步中，咱们将定义 ngrams 和 batch 大小。ngrams 特色用于捕捉无关本地语序的重要信息。
咱们应用 bigram，数据集中的示例文本将是单个单词加上 bigrams 字符串的列表。
NGRAMS = 2
BATCH_SIZE = 16当初，咱们将读取 TorchText 提供的 DBpedia 数据集。
if not os.path.isdir('./.data'):
os.mkdir('./.data')
train_dataset, test_dataset = text_classification.DATASETS['DBpedia'](root='./.data', ngrams=NGRAMS, vocab=None)
下载数据集后，咱们将验证下载数据集的长度和标签数量。
print(len(train_dataset))
print(len(test_dataset))
print(len(train_dataset.get_labels()))
print(len(test_dataset.get_labels()))
咱们将应用 CUDA 架构来减速运行和执行。
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
在下一步中，咱们将定义分类的模型。
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)
print(model)
当初，咱们将初始化超参数并定义函数以生成训练 batch。
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)
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]
offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
text = torch.cat(text)
return text, offsets, label在下一步中，咱们将定义用于训练和测试模型的函数。
def train_func(sub_train_):
# 训练模型
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()
# 调整学习率
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_)咱们将用 5 个 epoch 训练模型。
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)')
下一步，咱们将在测试数据集上测试咱们的模型，并查看模型的准确性。
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)')
当初，咱们将在单个新闻文本字符串上测试咱们的模型，并预测给定新闻文本的类标签。
DBpedia_label = {0: 'Company',
1: 'EducationalInstitution',
2: 'Artist',
3: 'Athlete',
4: 'OfficeHolder',
5: 'MeanOfTransportation',
6: 'Building',
7: 'NaturalPlace',
8: 'Village',
9: 'Animal',
10: 'Plant',
11: 'Album',
12: 'Film',
13: 'WrittenWork'}
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
vocab = train_dataset.get_vocab()
model = model.to("cpu")当初，咱们将从测试数据中随机抽取一些文本并查看预测的类标签。
第一个预测：
ex_text_str = "Brekke Church (Norwegian: Brekke kyrkje) is a parish church in Gulen Municipality in Sogn og Fjordane county, Norway. It is located in the village of Brekke. The church is part of the Brekke parish in the Nordhordland deanery in the Diocese of Bjørgvin. The white, wooden church, which has 390 seats, was consecrated on 19 November 1862 by the local Dean Thomas Erichsen. The architect Christian Henrik Grosch made the designs for the church, which is the third church on the site."
print("This is a %s news" %DBpedia_label[predict(ex_text_str, model, vocab, 2)])
第二个预测：
ex_text_str2 = "Cerithiella superba is a species of very small sea snail, a marine gastropod mollusk in the family Newtoniellidae. This species is known from European waters. It was described by Thiele, 1912."
print("This text belongs to %s class" %DBpedia_label[predict(ex_text_str2, model, vocab, 2)])
第三个预测：
ex_text_str3 = "Nithari is a village in the western part of the state of Uttar Pradesh India bordering on New Delhi. Nithari forms part of the New Okhla Industrial Development Authority's planned industrial city Noida falling in Sector 31. Nithari made international news headlines in December 2006 when the skeletons of a number of apparently murdered women and children were unearthed in the village."print("This text belongs to %s class" %DBpedia_label[predict(ex_text_str3, model, vocab, 2)])
因而，通过这种形式，咱们应用 TorchText 实现了多类文本分类。
这是一种简单易行的文本分类办法，应用这个 PyTorch 库只需很少的预处理量。在 5600 个训练实例上训练模型只花了不到 5 分钟。
通过将 ngram 从 2 更改为 3 来从新运行这些代码并查看后果是否有改良。同样的实现也能够在 TorchText 提供的其余数据集上实现。
参考文献：
- ‘Text Classification with TorchText’, PyTorch tutorial
- Allen Nie,‘A Tutorial on TorchText’
原文链接：https://analyticsindiamag.com…
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