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