47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
# -*- coding: utf-8 -*-
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"""
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@author:XuMing(xuming624@qq.com)
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@description:
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This basic example loads a pre-trained model from the web and uses it to
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compute cosine similarity for a given list of sentences.
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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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from loguru import logger
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logger.remove()
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logger.add(sys.stderr, level="INFO")
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if __name__ == '__main__':
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model = Similarity("shibing624/text2vec-base-chinese")
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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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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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# 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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# 3.Semantic Search
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m = Similarity(sentence_model="shibing624/text2vec-base-chinese", corpus=corpus)
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q = '如何更换花呗绑定银行卡'
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print(m.most_similar(q, topn=5))
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print("query:", q)
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for i in m.most_similar(q, topn=5):
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print('\t', i)
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