有没有办法检查python中两个完整句子之间的相似性？_Python_Deep Learning_Nlp_Nltk_Sentence Similarity

有没有办法检查python中两个完整句子之间的相似性？

python deep-learning nlp

有没有办法检查python中两个完整句子之间的相似性？,python,deep-learning,nlp,nltk,sentence-similarity,Python,Deep Learning,Nlp,Nltk,Sentence Similarity,我正在做这样一个项目：但我面临着麻烦，因为我需要检查句子之间的相似性，例如：如果用户说：“此人穿红色T恤”而不是“男孩穿红色T恤” 我想要一种方法来检查这两个句子之间的相似性，而不必检查每个单词之间的相似性在python中有没有实现这一点的方法我正试图找到一种方法来检查两个句子之间的相似性。下面的大多数库应该是语义相似性比较的好选择。通过使用这些库中的预训练模型生成单词或句子向量，可以跳过直接的单词比较与Spacy 必须首先加载所需的模型要使用en\u core\u web\u md

我正在做这样一个项目：但我面临着麻烦，因为我需要检查句子之间的相似性，例如：如果用户说：“此人穿红色T恤”而不是“男孩穿红色T恤” 我想要一种方法来检查这两个句子之间的相似性，而不必检查每个单词之间的相似性在python中有没有实现这一点的方法

我正试图找到一种方法来检查两个句子之间的相似性。

下面的大多数库应该是语义相似性比较的好选择。通过使用这些库中的预训练模型生成单词或句子向量，可以跳过直接的单词比较

与

Spacy

必须首先加载所需的模型

要使用

en\u core\u web\u md

请使用

python-mspacy下载en\u core\u web\u md

下载。使用

en\u core\u web\u lg

使用

python-mspacy下载en\u core\u web\u lg

大型模型的写入容量约为830mb，速度相当慢，因此中型模型是一个不错的选择

代码：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

输出：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

与

句子变换器的句子相似性

使用

pip安装-U语句转换器安装

。这一个生成了句子嵌入

代码：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

输出：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

现在嵌入向量可以用来计算各种相似性度量

代码：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

输出：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

与

scipy

和

pytorch

相同

代码：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

输出：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

代码：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

输出：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

与TFHub通用句子编码器的句子相似性

这个型号非常大，大约1GB，看起来比其他型号慢。这也会生成句子的嵌入

代码：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

输出：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

代码：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

输出：

import spacy
nlp = spacy.load("en_core_web_lg")
#nlp = spacy.load("en_core_web_md")


doc1 = nlp(u'the person wear red T-shirt')
doc2 = nlp(u'this person is walking')
doc3 = nlp(u'the boy wear red T-shirt')


print(doc1.similarity(doc2)) 
print(doc1.similarity(doc3))
print(doc2.similarity(doc3))

0.7003971105290047
0.9671912343259517
0.6121211244876517

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

sentences = [
    'the person wear red T-shirt',
    'this person is walking',
    'the boy wear red T-shirt'
    ]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Sentence: the person wear red T-shirt
Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01]

Sentence: this person is walking
Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02]

Sentence: the boy wear red T-shirt
Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]

from sentence_transformers import SentenceTransformer, util
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1]))
print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2]))
print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))

tensor([[0.4644]])
tensor([[0.9070]])
tensor([[0.3276]])

from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2]))
print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))

0.4643629193305969
0.9069876074790955
0.3275738060474396

import torch.nn
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
b = torch.from_numpy(sentence_embeddings)
print(cos(b[0], b[1]))
print(cos(b[0], b[2]))
print(cos(b[1], b[2]))

tensor(0.4644)
tensor(0.9070)
tensor(0.3276)

import tensorflow_hub as hub

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings = embed([
    "the person wear red T-shirt",
    "this person is walking",
    "the boy wear red T-shirt"
    ])

print(embeddings)

tf.Tensor(
[[ 0.063188    0.07063895 -0.05998802 ... -0.01409875  0.01863449
   0.01505797]
 [-0.06786212  0.01993554  0.03236153 ...  0.05772103  0.01787272
   0.01740014]
 [ 0.05379306  0.07613157 -0.05256693 ... -0.01256405  0.0213196
  -0.00262441]], shape=(3, 512), dtype=float32)

from scipy.spatial import distance
print(1 - distance.cosine(embeddings[0], embeddings[1]))
print(1 - distance.cosine(embeddings[0], embeddings[2]))
print(1 - distance.cosine(embeddings[1], embeddings[2]))

0.15320375561714172
0.8592830896377563
0.09080004692077637

其他语句嵌入库

此图显示了该方法

资源

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