45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
# -*- coding: utf-8 -*-
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"""
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@author:XuMing(xuming624@qq.com)
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@description: 文本语义相似度计算和文本匹配搜索
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"""
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import sys
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sys.path.append('..')
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from similarities import Similarity
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# 1.Compute cosine similarity between two sentences.
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sentences = ['如何更换花呗绑定银行卡',
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'花呗更改绑定银行卡']
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corpus = [
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'花呗更改绑定银行卡',
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'我什么时候开通了花呗',
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'俄罗斯警告乌克兰反对欧盟协议',
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'暴风雨掩埋了东北部；新泽西16英寸的降雪',
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'中央情报局局长访问以色列叙利亚会谈',
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'人在巴基斯坦基地的炸弹袭击中丧生',
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]
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model = Similarity(model_name_or_path="shibing624/text2vec-base-chinese")
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print(model)
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similarity_score = model.similarity(sentences[0], sentences[1])
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print(f"{sentences[0]} vs {sentences[1]}, score: {float(similarity_score):.4f}")
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print('-' * 50 + '\n')
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# 2.Compute similarity between two list
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similarity_scores = model.similarity(sentences, corpus)
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print(similarity_scores.numpy())
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for i in range(len(sentences)):
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for j in range(len(corpus)):
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print(f"{sentences[i]} vs {corpus[j]}, score: {similarity_scores.numpy()[i][j]:.4f}")
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print('-' * 50 + '\n')
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# 3.Semantic Search
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model.add_corpus(corpus)
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res = model.most_similar(queries=sentences, topn=3)
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print(res)
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for q_id, c in res.items():
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print('query:', sentences[q_id])
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print("search top 3:")
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for corpus_id, s in c.items():
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print(f'\t{model.corpus[corpus_id]}: {s:.4f}')
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