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| # -*- coding: utf-8 -*- | |
| """ | |
| @author:XuMing(xuming624@qq.com) | |
| @description: text similarity example, fine-tuned by CoSENT model | |
| """ | |
| import gradio as gr | |
| from similarities import Similarity | |
| # 中文句向量模型 | |
| sim_model = Similarity(model_name_or_path='shibing624/text2vec-base-chinese') | |
| def load_file(path): | |
| with open(path, 'r', encoding='utf-8') as f: | |
| return f.read().split('\n') | |
| sim_model.add_corpus(load_file('corpus.txt')) | |
| def ai_text(query): | |
| res = sim_model.most_similar(queries=query, topn=5) | |
| print(res) | |
| for q_id, c in res.items(): | |
| print('query:', query) | |
| print("search top 5:") | |
| for corpus_id, s in c.items(): | |
| print(f'\t{sim_model.corpus[corpus_id]}: {s:.4f}') | |
| res_show = '\n'.join(['search top5：'] + [f'text: {sim_model.corpus[corpus_id]} score: {s:.4f}' for corpus_id, s in | |
| list(res.values())[0].items()]) | |
| return res_show | |
| if __name__ == '__main__': | |
| examples = [ | |
| ['星巴克被嘲笑了'], | |
| ['西班牙失业率超过50%'], | |
| ['她在看书'], | |
| ['一个人弹琴'], | |
| ] | |
| input = gr.inputs.Textbox(lines=2, placeholder="Enter Query") | |
| output_text = gr.outputs.Textbox() | |
| gr.Interface(ai_text, | |
| inputs=[input], | |
| outputs=[output_text], | |
| theme="grass", | |
| title="Chinese Text Semantic Search Model", | |
| description="Copy or input Chinese text here. Submit and the machine will find the most similarity texts.", | |
| article="Link to <a href='https://github.com/shibing624/similarities' style='color:blue;' target='_blank\'>Github REPO</a>", | |
| examples=examples | |
| ).launch() | |