SentenceTransformers介绍_paraphrase-minilm-l6-v2

SentenceTransformer使用范例

1使用SentenceTransformers获得句子向量嵌入

from sentence_transformers import SentenceTransformer

#模型下载
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# 编码句子
sentences = ['Python is an interpreted high-level general-purpose programming language.',
    'Python is dynamically-typed and garbage-collected.',
    'The quick brown fox jumps over the lazy dog.']

# 获得句子嵌入向量
embeddings = model.encode(sentences)

# 打印嵌入向量
for sentence, embedding in zip(sentences, embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")
# Sentence: Python is an interpreted high-level general-purpose programming language.
# Embedding: [-1.17965914e-01 -4.57159936e-01 -5.87313235e-01 -2.72477478e-01 ...
# ...
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

2计算句子或者词语的余弦相似度

• 余弦相似度（Cosine Similarity）是n维空间中两个n维向量之间角度的余弦。它等于两个向量的点积（向量积）除以两个向量长度（或大小）的乘积。

取值范围为[-1:1],取值为-1表示完全不相似，取值为1表示完全相似

from sentence_transformers import SentenceTransformer, util

# 加载模型
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# 句子
sentences = ['Python is an interpreted high-level general-purpose programming language.',
    'Python is dynamically-typed and garbage-collected.',
    'The quick brown fox jumps over the lazy dog.']

# 讲语句进行向量编码
embeddings = model.encode(sentences)

# 计算相似度
sim = util.cos_sim(embeddings[0], embeddings[1])
print("{0:.4f}".format(sim.tolist()[0][0])) # 0.6445
sim = util.cos_sim(embeddings[0], embeddings[2])
print("{0:.4f}".format(sim.tolist()[0][0])) # 0.0365
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

3语义搜索

语义搜索通过理解搜索查询的内容来提高搜索的准确性，而不是仅仅依赖于词汇匹配。这是利用嵌入之间的相似性完成的。

语义搜索是将语料库中的所有条目嵌入到向量空间中。在搜索时，查询也会被嵌入到相同的向量空间中，并从语料库中找到最接近的嵌入。

语料库中文档的插入和查询的嵌入

# 下载模型
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# Corpus of documents and their embeddings
corpus = ['Python is an interpreted high-level general-purpose programming language.',
    'Python is dynamically-typed and garbage-collected.',
    'The quick brown fox jumps over the lazy dog.']
corpus_embeddings = model.encode(corpus)

# Queries and their embeddings
queries = ["What is Python?", "What did the fox do?"]
queries_embeddings = model.encode(queries)

# Find the top-2 corpus documents matching each query
hits = util.semantic_search(queries_embeddings, corpus_embeddings, top_k=2)

# Print results of first query
print(f"Query: {queries[0]}")
for hit in hits[0]:
    print(corpus[hit['corpus_id']], "(Score: {:.4f})".format(hit['score']))
# Query: What is Python?
# Python is an interpreted high-level general-purpose programming language. (Score: 0.6759)
# Python is dynamically-typed and garbage-collected. (Score: 0.6219)

# Print results of second query
print(f"Query: {queries[1]}")
for hit in hits[1]:
    print(corpus[hit['corpus_id']], "(Score: {:.4f})".format(hit['score']))
# Query: What did the fox do?
# The quick brown fox jumps over the lazy dog. (Score: 0.3816)
# Python is dynamically-typed and garbage-collected. (Score: 0.0713)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

为了充分利用语义搜索，必须区分对称和非对称语义搜索，因为它会严重影响要使用的模型的选择。

文章参考：
5分钟 NLP系列 — SentenceTransformers 库介绍
Sentence Transformer官网