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| # -*- coding: utf-8 -*- | |
| """ | |
| @author:XuMing(xuming624@qq.com) | |
| @description: | |
| """ | |
| import gradio as gr | |
| from text2vec import SBert, cos_sim, semantic_search, Similarity | |
| # 中文句向量模型(CoSENT) | |
| sim_model = Similarity(model_name_or_path='shibing624/text2vec-base-chinese', | |
| similarity_type='cosine', embedding_type='sbert') | |
| sentences1 = ['如何更换花呗绑定银行卡', | |
| 'The cat sits outside', | |
| 'A man is playing guitar', | |
| 'The new movie is awesome'] | |
| sentences2 = ['花呗更改绑定银行卡', | |
| 'The dog plays in the garden', | |
| 'A woman watches TV', | |
| 'The new movie is so great'] | |
| def ai_text(sentence1, sentence2): | |
| score = sim_model.get_score(sentence1, sentence2) | |
| print("{} \t\t {} \t\t Score: {:.4f}".format(sentence1, sentence2, score)) | |
| return score | |
| if __name__ == '__main__': | |
| examples = [ | |
| ['import torch.nn as'], | |
| ['parser.add_argument("--num_train_epochs",'], | |
| ['torch.device('], | |
| ['def set_seed('], | |
| ] | |
| input1 = gr.inputs.Textbox(placeholder="Enter First Sentence") | |
| input2 = gr.inputs.Textbox(placeholder="Enter Second Sentence") | |
| output_text = gr.outputs.Textbox() | |
| gr.Interface(ai_text, | |
| inputs=[input1, input2], | |
| outputs=[output_text], | |
| theme="grass", | |
| title="Chinese Text to Vector Model shibing624/text2vec-base-chinese", | |
| description="Copy or input python code here. Submit and the machine will calculate the cosine score.", | |
| article="Link to <a href='https://github.com/shibing624/text2vec' style='color:blue;' target='_blank\'>Github REPO</a>", | |
| # examples=examples | |
| ).launch() |