.. _sec_natural-language-inference-and-dataset:
自然语言推断与数据集
====================
在
:numref:`sec_sentiment`\ 中，我们讨论了情感分析问题。这个任务的目的是将单个文本序列分类到预定义的类别中，例如一组情感极性中。然而，当需要决定一个句子是否可以从另一个句子推断出来，或者需要通过识别语义等价的句子来消除句子间冗余时，知道如何对一个文本序列进行分类是不够的。相反，我们需要能够对成对的文本序列进行推断。
自然语言推断
------------
*自然语言推断*\ （natural language inference）主要研究
*假设*\ （hypothesis）是否可以从\ *前提*\ （premise）中推断出来，
其中两者都是文本序列。
换言之，自然语言推断决定了一对文本序列之间的逻辑关系。这类关系通常分为三种类型：
- *蕴涵*\ （entailment）：假设可以从前提中推断出来。
- *矛盾*\ （contradiction）：假设的否定可以从前提中推断出来。
- *中性*\ （neutral）：所有其他情况。
自然语言推断也被称为识别文本蕴涵任务。
例如，下面的一个文本对将被贴上“蕴涵”的标签，因为假设中的“表白”可以从前提中的“拥抱”中推断出来。
前提：两个女人拥抱在一起。
..
假设：两个女人在示爱。
下面是一个“矛盾”的例子，因为“运行编码示例”表示“不睡觉”，而不是“睡觉”。
前提：一名男子正在运行Dive Into Deep Learning的编码示例。
..
假设：该男子正在睡觉。
第三个例子显示了一种“中性”关系，因为“正在为我们表演”这一事实无法推断出“出名”或“不出名”。
前提：音乐家们正在为我们表演。
..
假设：音乐家很有名。
自然语言推断一直是理解自然语言的中心话题。它有着广泛的应用，从信息检索到开放领域的问答。为了研究这个问题，我们将首先研究一个流行的自然语言推断基准数据集。
斯坦福自然语言推断（SNLI）数据集
--------------------------------
斯坦福自然语言推断语料库（Stanford Natural Language
Inference，SNLI）是由500000多个带标签的英语句子对组成的集合
:cite:`Bowman.Angeli.Potts.ea.2015`\ 。我们在路径\ ``../data/snli_1.0``\ 中下载并存储提取的SNLI数据集。
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import os
import re
from mxnet import gluon, np, npx
from d2l import mxnet as d2l
npx.set_np()
#@save
d2l.DATA_HUB['SNLI'] = (
'https://nlp.stanford.edu/projects/snli/snli_1.0.zip',
'9fcde07509c7e87ec61c640c1b2753d9041758e4')
data_dir = d2l.download_extract('SNLI')
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import os
import re
import torch
from torch import nn
from d2l import torch as d2l
#@save
d2l.DATA_HUB['SNLI'] = (
'https://nlp.stanford.edu/projects/snli/snli_1.0.zip',
'9fcde07509c7e87ec61c640c1b2753d9041758e4')
data_dir = d2l.download_extract('SNLI')
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import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import os
import re
import paddle
from paddle import nn
#@save
d2l.DATA_HUB['SNLI'] = (
'https://nlp.stanford.edu/projects/snli/snli_1.0.zip',
'9fcde07509c7e87ec61c640c1b2753d9041758e4')
data_dir = d2l.download_extract('SNLI')
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#@save
def read_snli(data_dir, is_train):
"""将SNLI数据集解析为前提、假设和标签"""
def extract_text(s):
# 删除我们不会使用的信息
s = re.sub('\\(', '', s)
s = re.sub('\\)', '', s)
# 用一个空格替换两个或多个连续的空格
s = re.sub('\\s{2,}', ' ', s)
return s.strip()
label_set = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
file_name = os.path.join(data_dir, 'snli_1.0_train.txt'
if is_train else 'snli_1.0_test.txt')
with open(file_name, 'r') as f:
rows = [row.split('\t') for row in f.readlines()[1:]]
premises = [extract_text(row[1]) for row in rows if row[0] in label_set]
hypotheses = [extract_text(row[2]) for row in rows if row[0] \
in label_set]
labels = [label_set[row[0]] for row in rows if row[0] in label_set]
return premises, hypotheses, labels
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#@save
def read_snli(data_dir, is_train):
"""将SNLI数据集解析为前提、假设和标签"""
def extract_text(s):
# 删除我们不会使用的信息
s = re.sub('\\(', '', s)
s = re.sub('\\)', '', s)
# 用一个空格替换两个或多个连续的空格
s = re.sub('\\s{2,}', ' ', s)
return s.strip()
label_set = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
file_name = os.path.join(data_dir, 'snli_1.0_train.txt'
if is_train else 'snli_1.0_test.txt')
with open(file_name, 'r') as f:
rows = [row.split('\t') for row in f.readlines()[1:]]
premises = [extract_text(row[1]) for row in rows if row[0] in label_set]
hypotheses = [extract_text(row[2]) for row in rows if row[0] \
in label_set]
labels = [label_set[row[0]] for row in rows if row[0] in label_set]
return premises, hypotheses, labels
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#@save
def read_snli(data_dir, is_train):
"""将SNLI数据集解析为前提、假设和标签"""
def extract_text(s):
# 删除我们不会使用的信息
s = re.sub('\\(', '', s)
s = re.sub('\\)', '', s)
# 用一个空格替换两个或多个连续的空格
s = re.sub('\\s{2,}', ' ', s)
return s.strip()
label_set = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
file_name = os.path.join(data_dir, 'snli_1.0_train.txt'
if is_train else 'snli_1.0_test.txt')
with open(file_name, 'r') as f:
rows = [row.split('\t') for row in f.readlines()[1:]]
premises = [extract_text(row[1]) for row in rows if row[0] in label_set]
hypotheses = [extract_text(row[2]) for row in rows if row[0] \
in label_set]
labels = [label_set[row[0]] for row in rows if row[0] in label_set]
return premises, hypotheses, labels
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train_data = read_snli(data_dir, is_train=True)
for x0, x1, y in zip(train_data[0][:3], train_data[1][:3], train_data[2][:3]):
print('前提：', x0)
print('假设：', x1)
print('标签：', y)
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前提： A person on a horse jumps over a broken down airplane .
假设： A person is training his horse for a competition .
标签： 2
前提： A person on a horse jumps over a broken down airplane .
假设： A person is at a diner , ordering an omelette .
标签： 1
前提： A person on a horse jumps over a broken down airplane .
假设： A person is outdoors , on a horse .
标签： 0
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train_data = read_snli(data_dir, is_train=True)
for x0, x1, y in zip(train_data[0][:3], train_data[1][:3], train_data[2][:3]):
print('前提：', x0)
print('假设：', x1)
print('标签：', y)
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:class: output
前提： A person on a horse jumps over a broken down airplane .
假设： A person is training his horse for a competition .
标签： 2
前提： A person on a horse jumps over a broken down airplane .
假设： A person is at a diner , ordering an omelette .
标签： 1
前提： A person on a horse jumps over a broken down airplane .
假设： A person is outdoors , on a horse .
标签： 0
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train_data = read_snli(data_dir, is_train=True)
for x0, x1, y in zip(train_data[0][:3], train_data[1][:3], train_data[2][:3]):
print('前提：', x0)
print('假设：', x1)
print('标签：', y)
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:class: output
前提： A person on a horse jumps over a broken down airplane .
假设： A person is training his horse for a competition .
标签： 2
前提： A person on a horse jumps over a broken down airplane .
假设： A person is at a diner , ordering an omelette .
标签： 1
前提： A person on a horse jumps over a broken down airplane .
假设： A person is outdoors , on a horse .
标签： 0
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test_data = read_snli(data_dir, is_train=False)
for data in [train_data, test_data]:
print([[row for row in data[2]].count(i) for i in range(3)])
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[183416, 183187, 182764]
[3368, 3237, 3219]
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test_data = read_snli(data_dir, is_train=False)
for data in [train_data, test_data]:
print([[row for row in data[2]].count(i) for i in range(3)])
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[183416, 183187, 182764]
[3368, 3237, 3219]
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test_data = read_snli(data_dir, is_train=False)
for data in [train_data, test_data]:
print([[row for row in data[2]].count(i) for i in range(3)])
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[183416, 183187, 182764]
[3368, 3237, 3219]
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#@save
class SNLIDataset(gluon.data.Dataset):
"""用于加载SNLI数据集的自定义数据集"""
def __init__(self, dataset, num_steps, vocab=None):
self.num_steps = num_steps
all_premise_tokens = d2l.tokenize(dataset[0])
all_hypothesis_tokens = d2l.tokenize(dataset[1])
if vocab is None:
self.vocab = d2l.Vocab(all_premise_tokens + \
all_hypothesis_tokens, min_freq=5, reserved_tokens=['
'])
else:
self.vocab = vocab
self.premises = self._pad(all_premise_tokens)
self.hypotheses = self._pad(all_hypothesis_tokens)
self.labels = np.array(dataset[2])
print('read ' + str(len(self.premises)) + ' examples')
def _pad(self, lines):
return np.array([d2l.truncate_pad(
self.vocab[line], self.num_steps, self.vocab[''])
for line in lines])
def __getitem__(self, idx):
return (self.premises[idx], self.hypotheses[idx]), self.labels[idx]
def __len__(self):
return len(self.premises)
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#@save
class SNLIDataset(torch.utils.data.Dataset):
"""用于加载SNLI数据集的自定义数据集"""
def __init__(self, dataset, num_steps, vocab=None):
self.num_steps = num_steps
all_premise_tokens = d2l.tokenize(dataset[0])
all_hypothesis_tokens = d2l.tokenize(dataset[1])
if vocab is None:
self.vocab = d2l.Vocab(all_premise_tokens + \
all_hypothesis_tokens, min_freq=5, reserved_tokens=['
'])
else:
self.vocab = vocab
self.premises = self._pad(all_premise_tokens)
self.hypotheses = self._pad(all_hypothesis_tokens)
self.labels = torch.tensor(dataset[2])
print('read ' + str(len(self.premises)) + ' examples')
def _pad(self, lines):
return torch.tensor([d2l.truncate_pad(
self.vocab[line], self.num_steps, self.vocab[''])
for line in lines])
def __getitem__(self, idx):
return (self.premises[idx], self.hypotheses[idx]), self.labels[idx]
def __len__(self):
return len(self.premises)
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#@save
class SNLIDataset(paddle.io.Dataset):
"""用于加载SNLI数据集的自定义数据集"""
def __init__(self, dataset, num_steps, vocab=None):
self.num_steps = num_steps
all_premise_tokens = d2l.tokenize(dataset[0])
all_hypothesis_tokens = d2l.tokenize(dataset[1])
if vocab is None:
self.vocab = d2l.Vocab(all_premise_tokens + \
all_hypothesis_tokens, min_freq=5, reserved_tokens=['
'])
else:
self.vocab = vocab
self.premises = self._pad(all_premise_tokens)
self.hypotheses = self._pad(all_hypothesis_tokens)
self.labels = paddle.to_tensor(dataset[2])
print('read ' + str(len(self.premises)) + ' examples')
def _pad(self, lines):
return paddle.to_tensor([d2l.truncate_pad(
self.vocab[line], self.num_steps, self.vocab[''])
for line in lines])
def __getitem__(self, idx):
return (self.premises[idx], self.hypotheses[idx]), self.labels[idx]
def __len__(self):
return len(self.premises)
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#@save
def load_data_snli(batch_size, num_steps=50):
"""下载SNLI数据集并返回数据迭代器和词表"""
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_data = read_snli(data_dir, True)
test_data = read_snli(data_dir, False)
train_set = SNLIDataset(train_data, num_steps)
test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
num_workers=num_workers)
test_iter = gluon.data.DataLoader(test_set, batch_size, shuffle=False,
num_workers=num_workers)
return train_iter, test_iter, train_set.vocab
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#@save
def load_data_snli(batch_size, num_steps=50):
"""下载SNLI数据集并返回数据迭代器和词表"""
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_data = read_snli(data_dir, True)
test_data = read_snli(data_dir, False)
train_set = SNLIDataset(train_data, num_steps)
test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
train_iter = torch.utils.data.DataLoader(train_set, batch_size,
shuffle=True,
num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(test_set, batch_size,
shuffle=False,
num_workers=num_workers)
return train_iter, test_iter, train_set.vocab
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#@save
def load_data_snli(batch_size, num_steps=50):
"""下载SNLI数据集并返回数据迭代器和词表"""
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_data = read_snli(data_dir, True)
test_data = read_snli(data_dir, False)
train_set = SNLIDataset(train_data, num_steps)
test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
train_iter = paddle.io.DataLoader(train_set,batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
return_list=True)
test_iter = paddle.io.DataLoader(test_set, batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
return_list=True)
return train_iter, test_iter, train_set.vocab
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train_iter, test_iter, vocab = load_data_snli(128, 50)
len(vocab)
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[07:14:14] ../src/storage/storage.cc:196: Using Pooled (Naive) StorageManager for CPU
read 549367 examples
read 9824 examples
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18678
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train_iter, test_iter, vocab = load_data_snli(128, 50)
len(vocab)
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read 549367 examples
read 9824 examples
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:class: output
18678
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train_iter, test_iter, vocab = load_data_snli(128, 50)
len(vocab)
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read 549367 examples
read 9824 examples
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:class: output
18678
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for X, Y in train_iter:
print(X[0].shape)
print(X[1].shape)
print(Y.shape)
break
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:class: output
(128, 50)
(128, 50)
(128,)
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for X, Y in train_iter:
print(X[0].shape)
print(X[1].shape)
print(Y.shape)
break
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torch.Size([128, 50])
torch.Size([128, 50])
torch.Size([128])
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for X, Y in train_iter:
print(X[0].shape)
print(X[1].shape)
print(Y.shape)
break
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[128, 50]
[128, 50]
[128, 1]
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`Discussions `__
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`Discussions `__
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`Discussions `__
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