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            ---
         
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            language: zh
         
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            pipeline_tag: sentence-similarity
         
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            tags:
         
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            - sentence-transformers
         
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            - feature-extraction
         
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            - sentence-similarity
         
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            - transformers
         
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            license: apache-2.0
         
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            widget:
         
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                source_sentence: "那个人很开心"
         
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                sentences:
         
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                    - 那个人非常开心
         
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                    - 那只猫很开心
         
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                    - 那个人在吃东西
         
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            ---
         
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            # Chinese Sentence BERT
         
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            ## Model description
         
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            This is the sentence embedding model pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
         
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            ## Training data
         
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            [ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference/) is used as training data. 
         
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            ## Training procedure
         
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            The model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune five epochs with a sequence length of 128 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved.
         
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            ```
         
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            python3 finetune/run_classifier_siamese.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
         
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                                                       --vocab_path models/google_zh_vocab.txt \
         
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                                                       --config_path models/sbert/base_config.json \
         
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                                                       --train_path datasets/ChineseTextualInference/train.tsv \
         
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                                                       --dev_path datasets/ChineseTextualInference/dev.tsv \
         
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                                                       --learning_rate 5e-5 --epochs_num 5 --batch_size 64
         
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            ```
         
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            Finally, we convert the pre-trained model into Huggingface's format:
         
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            ```
         
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            python3 scripts/convert_sbert_from_uer_to_huggingface.py --input_model_path models/finetuned_model.bin \                                                                
         
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                                                                     --output_model_path pytorch_model.bin \                                                                                            
         
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                                                                     --layers_num 12
         
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            ```
         
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            ### BibTeX entry and citation info
         
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            ```
         
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            @article{reimers2019sentence,
         
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              title={Sentence-bert: Sentence embeddings using siamese bert-networks},
         
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              author={Reimers, Nils and Gurevych, Iryna},
         
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              journal={arXiv preprint arXiv:1908.10084},
         
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              year={2019}
         
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            }
         
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            @article{zhao2019uer,
         
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              title={UER: An Open-Source Toolkit for Pre-training Models},
         
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              author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
         
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              journal={EMNLP-IJCNLP 2019},
         
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              pages={241},
         
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              year={2019}
         
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            }
         
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            ```
         
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