例如,句子余弦相似性的预训练结果
======================
Query: milk with chocolate flavor
Top 10 most similar sentences in corpus:
Milka milk chocolate 100 g (Score: 0.8672)
Alpro, Chocolate soy drink 1 ltr (Score: 0.6821)
Danone, HiPRO 25g Protein chocolate flavor 330 ml (Score: 0.6692)
在上面的例子中,我正在搜索牛奶,结果首先应该是与牛奶相关的,但在这里它首先返回巧克力。如何对结果的相似性进行微调
我在谷歌上搜索了一下,但没有找到合适的解决方案,请帮助我
代码:
import scipy
import numpy as np
from sentence_transformers import models, SentenceTransformer
model = SentenceTransformer('distilbert-base-multilingual-cased')
corpus = [
"Alpro, Chocolate soy drink 1 ltr",
"Milka milk chocolate 100 g",
"Danone, HiPRO 25g Protein chocolate flavor 330 ml"
]
corpus_embeddings = model.encode(corpus)
queries = [
'milk with chocolate flavor',
]
query_embeddings = model.encode(queries)
# Calculate Cosine similarity of query against each sentence i
closest_n = 10
for query, query_embedding in zip(queries, query_embeddings):
distances = scipy.spatial.distance.cdist([query_embedding], corpus_embeddings, "cosine")[0]
results = zip(range(len(distances)), distances)
results = sorted(results, key=lambda x: x[1])
print("\n======================\n")
print("Query:", query)
print("\nTop 10 most similar sentences in corpus:")
for idx, distance in results[0:closest_n]:
print(corpus[idx].strip(), "(Score: %.4f)" % (1-distance))
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