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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). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. |
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## How to use |
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You can use this model to extract sentence embeddings for sentence similarity task. We use cosine distance to calculate the embedding similarity here: |
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```python |
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>>> from sentence_transformers import SentenceTransformer |
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>>> model = SentenceTransformer('uer/sbert-base-chinese-nli') |
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>>> sentences = ['那个人很开心', '那个人非常开心'] |
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>>> sentence_embeddings = model.encode(sentences) |
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>>> from sklearn.metrics.pairwise import paired_cosine_distances |
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>>> cosine_score = 1 - paired_cosine_distances([sentence_embeddings[0]],[sentence_embeddings[1]]) |
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``` |
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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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@article{zhao2023tencentpretrain, |
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title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, |
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author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, |
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journal={ACL 2023}, |
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pages={217}, |
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year={2023} |
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``` |
