文本分类¶
文本分类(Text classification)任务是将一句话或一段话划分到某个具体的类别。比如垃圾邮件识别,文本情绪分类等。这篇教程可以带你从零开始了解 fastNLP 的使用
注解
本教程推荐使用 GPU 进行实验
1, 商务大床房,房间很大,床有2M宽,整体感觉经济实惠不错!
其中开头的1是只这条评论的标签,表示是正面的情绪。我们将使用到的数据可以通过 此链接 下载并解压,当然也可以通过fastNLP自动下载该数据。
数据中的内容如下图所示。接下来,我们将用fastNLP在这个数据上训练一个分类网络。
步骤¶
一共有以下的几个步骤:
(1) 读取数据¶
fastNLP提供多种数据的自动下载与自动加载功能,对于这里我们要用到的数据,我们可以用 Loader
自动下载并加载该数据。
更多有关Loader的使用可以参考 loader
from fastNLP.io import ChnSentiCorpLoader
loader = ChnSentiCorpLoader() # 初始化一个中文情感分类的loader
data_dir = loader.download() # 这一行代码将自动下载数据到默认的缓存地址, 并将该地址返回
data_bundle = loader.load(data_dir) # 这一行代码将从{data_dir}处读取数据至DataBundle
DataBundle的相关介绍,可以参考 DataBundle
。我们可以打印该data_bundle的基本信息。
print(data_bundle)
In total 3 datasets:
dev has 1200 instances.
train has 9600 instances.
test has 1200 instances.
In total 0 vocabs:
可以看出,该data_bundle中一个含有三个 DataSet
。通过下面的代码,我们可以查看DataSet的基本情况
print(data_bundle.get_dataset('train')[:2]) # 查看Train集前两个sample
+-----------------------------+--------+
| raw_chars | target |
+-----------------------------+--------+
| 选择珠江花园的原因就是方... | 1 |
| 15.4寸笔记本的键盘确实爽... | 1 |
+-----------------------------+--------+
(2) 预处理数据¶
在NLP任务中,预处理一般包括:
- 将一整句话切分成汉字或者词;
- 将文本转换为index
fastNLP中也提供了多种数据集的处理类,这里我们直接使用fastNLP的ChnSentiCorpPipe。更多关于Pipe的说明可以参考 pipe
。
from fastNLP.io import ChnSentiCorpPipe
pipe = ChnSentiCorpPipe()
data_bundle = pipe.process(data_bundle) # 所有的Pipe都实现了process()方法,且输入输出都为DataBundle类型
print(data_bundle) # 打印data_bundle,查看其变化
In total 3 datasets:
dev has 1200 instances.
train has 9600 instances.
test has 1200 instances.
In total 2 vocabs:
chars has 4409 entries.
target has 2 entries.
可以看到除了之前已经包含的3个 DataSet
,还新增了两个 Vocabulary
。我们可以打印DataSet中的内容
print(data_bundle.get_dataset('train')[:2])
+-----------------+--------+-----------------+---------+
| raw_chars | target | chars | seq_len |
+-----------------+--------+-----------------+---------+
| 选择珠江花园... | 0 | [338, 464, 1... | 106 |
| 15.4寸笔记本... | 0 | [50, 133, 20... | 56 |
+-----------------+--------+-----------------+---------+
新增了一列为数字列表的chars,以及变为数字的target列。可以看出这两列的名称和刚好与data_bundle中两个Vocabulary的名称是一致的,我们可以打印一下Vocabulary看一下里面的内容。
char_vocab = data_bundle.get_vocab('chars')
print(char_vocab)
Vocabulary(['选', '择', '珠', '江', '花']...)
Vocabulary是一个记录着词语与index之间映射关系的类,比如
index = char_vocab.to_index('选')
print("'选'的index是{}".format(index)) # 这个值与上面打印出来的第一个instance的chars的第一个index是一致的
print("index:{}对应的汉字是{}".format(index, char_vocab.to_word(index)))
'选'的index是338
index:338对应的汉字是选
(3) 选择预训练词向量¶
由于Word2vec, Glove, Elmo,
Bert等预训练模型可以增强模型的性能,所以在训练具体任务前,选择合适的预训练词向量非常重要。
在fastNLP中我们提供了多种Embedding使得加载这些预训练模型的过程变得更加便捷。
这里我们先给出一个使用word2vec的中文汉字预训练的示例,之后再给出一个使用Bert的文本分类。
这里使用的预训练词向量为'cn-fastnlp-100d',fastNLP将自动下载该embedding至本地缓存,
fastNLP支持使用名字指定的Embedding以及相关说明可以参见 fastNLP.embeddings
from fastNLP.embeddings import StaticEmbedding
word2vec_embed = StaticEmbedding(char_vocab, model_dir_or_name='cn-char-fastnlp-100d')
Found 4321 out of 4409 compound in the pre-training embedding.
(4) 创建模型¶
from torch import nn
from fastNLP.modules import LSTM
import torch
# 定义模型
class BiLSTMMaxPoolCls(nn.Module):
def __init__(self, embed, num_classes, hidden_size=400, num_layers=1, dropout=0.3):
super().__init__()
self.embed = embed
self.lstm = LSTM(self.embed.embedding_dim, hidden_size=hidden_size//2, num_layers=num_layers,
batch_first=True, bidirectional=True)
self.dropout_layer = nn.Dropout(dropout)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, chars, seq_len): # 这里的名称必须和DataSet中相应的field对应,比如之前我们DataSet中有chars,这里就必须为chars
# chars:[batch_size, max_len]
# seq_len: [batch_size, ]
chars = self.embed(chars)
outputs, _ = self.lstm(chars, seq_len)
outputs = self.dropout_layer(outputs)
outputs, _ = torch.max(outputs, dim=1)
outputs = self.fc(outputs)
return {'pred':outputs} # [batch_size,], 返回值必须是dict类型,且预测值的key建议设为pred
# 初始化模型
model = BiLSTMMaxPoolCls(word2vec_embed, len(data_bundle.get_vocab('target')))
(5) 训练模型¶
fastNLP提供了Trainer对象来组织训练过程,包括完成loss计算(所以在初始化Trainer的时候需要指定loss类型),梯度更新(所以在初始化Trainer的时候需要提供优化器optimizer)以及在验证集上的性能验证(所以在初始化时需要提供一个Metric)
from fastNLP import Trainer
from fastNLP import CrossEntropyLoss
from torch.optim import Adam
from fastNLP import AccuracyMetric
loss = CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=0.001)
metric = AccuracyMetric()
device = 0 if torch.cuda.is_available() else 'cpu' # 如果有gpu的话在gpu上运行,训练速度会更快
trainer = Trainer(train_data=data_bundle.get_dataset('train'), model=model, loss=loss,
optimizer=optimizer, batch_size=32, dev_data=data_bundle.get_dataset('dev'),
metrics=metric, device=device)
trainer.train() # 开始训练,训练完成之后默认会加载在dev上表现最好的模型
# 在测试集上测试一下模型的性能
from fastNLP import Tester
print("Performance on test is:")
tester = Tester(data=data_bundle.get_dataset('test'), model=model, metrics=metric, batch_size=64, device=device)
tester.test()
input fields after batch(if batch size is 2):
target: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
chars: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 106])
seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
target fields after batch(if batch size is 2):
target: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
Evaluate data in 0.01 seconds!
training epochs started 2019-09-03-23-57-10
Evaluate data in 0.43 seconds!
Evaluation on dev at Epoch 1/10. Step:300/3000:
AccuracyMetric: acc=0.81
Evaluate data in 0.44 seconds!
Evaluation on dev at Epoch 2/10. Step:600/3000:
AccuracyMetric: acc=0.8675
Evaluate data in 0.44 seconds!
Evaluation on dev at Epoch 3/10. Step:900/3000:
AccuracyMetric: acc=0.878333
....
Evaluate data in 0.48 seconds!
Evaluation on dev at Epoch 9/10. Step:2700/3000:
AccuracyMetric: acc=0.8875
Evaluate data in 0.43 seconds!
Evaluation on dev at Epoch 10/10. Step:3000/3000:
AccuracyMetric: acc=0.895833
In Epoch:7/Step:2100, got best dev performance:
AccuracyMetric: acc=0.8975
Reloaded the best model.
Evaluate data in 0.34 seconds!
[tester]
AccuracyMetric: acc=0.8975
{'AccuracyMetric': {'acc': 0.8975}}
使用Bert进行文本分类¶
# 只需要切换一下Embedding即可
from fastNLP.embeddings import BertEmbedding
# 这里为了演示一下效果,所以默认Bert不更新权重
bert_embed = BertEmbedding(char_vocab, model_dir_or_name='cn', auto_truncate=True, requires_grad=False)
model = BiLSTMMaxPoolCls(bert_embed, len(data_bundle.get_vocab('target')))
import torch
from fastNLP import Trainer
from fastNLP import CrossEntropyLoss
from torch.optim import Adam
from fastNLP import AccuracyMetric
loss = CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=2e-5)
metric = AccuracyMetric()
device = 0 if torch.cuda.is_available() else 'cpu' # 如果有gpu的话在gpu上运行,训练速度会更快
trainer = Trainer(train_data=data_bundle.get_dataset('train'), model=model, loss=loss,
optimizer=optimizer, batch_size=16, dev_data=data_bundle.get_dataset('test'),
metrics=metric, device=device, n_epochs=3)
trainer.train() # 开始训练,训练完成之后默认会加载在dev上表现最好的模型
# 在测试集上测试一下模型的性能
from fastNLP import Tester
print("Performance on test is:")
tester = Tester(data=data_bundle.get_dataset('test'), model=model, metrics=metric, batch_size=64, device=device)
tester.test()
loading vocabulary file ~/.fastNLP/embedding/bert-chinese-wwm/vocab.txt
Load pre-trained BERT parameters from file ~/.fastNLP/embedding/bert-chinese-wwm/chinese_wwm_pytorch.bin.
Start to generating word pieces for word.
Found(Or segment into word pieces) 4286 words out of 4409.
input fields after batch(if batch size is 2):
target: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
chars: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 106])
seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
target fields after batch(if batch size is 2):
target: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
Evaluate data in 0.05 seconds!
training epochs started 2019-09-04-00-02-37
Evaluate data in 15.89 seconds!
Evaluation on dev at Epoch 1/3. Step:1200/3600:
AccuracyMetric: acc=0.9
Evaluate data in 15.92 seconds!
Evaluation on dev at Epoch 2/3. Step:2400/3600:
AccuracyMetric: acc=0.904167
Evaluate data in 15.91 seconds!
Evaluation on dev at Epoch 3/3. Step:3600/3600:
AccuracyMetric: acc=0.918333
In Epoch:3/Step:3600, got best dev performance:
AccuracyMetric: acc=0.918333
Reloaded the best model.
Performance on test is:
Evaluate data in 29.24 seconds!
[tester]
AccuracyMetric: acc=0.919167
{'AccuracyMetric': {'acc': 0.919167}}