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''' |
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Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved. |
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Licensed under the Apache License, Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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@File : generate.py |
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@Time : 2022/11/04 19:17 |
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@Author : Liang Yuxin |
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@Version : 1.0 |
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@Contact : [email protected] |
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@License : (C)Copyright 2022-2023, CCNL-IDEA |
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''' |
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import torch |
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from fengshen.models.DAVAE.DAVAEModel import DAVAEModel |
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from transformers import BertTokenizer,T5Tokenizer |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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encoder_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Randeng-DAVAE-1.2B-General-Chinese") |
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decoder_tokenizer = T5Tokenizer.from_pretrained("IDEA-CCNL/Randeng-DAVAE-1.2B-General-Chinese", eos_token = '<|endoftext|>', pad_token = '<pad>',extra_ids=0) |
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decoder_tokenizer.add_special_tokens({'bos_token':'<bos>'}) |
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vae_model = DAVAEModel.from_pretrained("IDEA-CCNL/Randeng-DAVAE-1.2B-General-Chinese").to(device) |
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input_texts = [ |
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"针对电力系统中的混沌振荡对整个互联电网的危害问题,提出了一种基于非线性光滑函数的滑模控制方法.", |
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"超市面积不算大.挺方便附近的居民购买的. 生活用品也比较齐全.价格适用中.", |
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] |
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output_texts = vae_model.simulate_batch(encoder_tokenizer,decoder_tokenizer,input_texts) |
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print(output_texts) |
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