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---
language: Chinese
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
widget:
source_sentence: "那个人很开心"
sentences:
- 那个人非常开心
- 那只猫很开心
- 那个人在吃东西
---
# Chinese Sentence BERT
## Model description
This model is pre-trained by [UER-py](https://arxiv.org/abs/1909.05658).
## Training data
[ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference/) is used as training data.
## Training procedure
This model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune three epochs with a sequence length of 512 on the basis of the Google's pre-trained Chinese BERT model. At the end of each epoch, the model is saved when the best performance on development set is achieved.
```
python3 finetune/run_classifier_siamese.py --pretrained_model_path models/google_zh_model.bin \
--vocab_path models/google_zh_vocab.txt \
--config_path models/sbert/base_config.json \
--train_path datasets/ChineseTextualInference/train.tsv \
--dev_path datasets/ChineseTextualInference/dev.tsv \
--epochs_num 3 --batch_size 32
```
Finally, we convert the pre-trained model into Huggingface's format:
```
python3 scripts/convert_sbert_from_uer_to_huggingface.py --input_model_path cluecorpussmall_bart_base_seq512_model.bin-250000 \
--output_model_path pytorch_model.bin \
--layers_num 12
```
### BibTeX entry and citation info
```
@article{reimers2019sentence,
title={Sentence-bert: Sentence embeddings using siamese bert-networks},
author={Reimers, Nils and Gurevych, Iryna},
journal={arXiv preprint arXiv:1908.10084},
year={2019}
}
@article{zhao2019uer,
title={UER: An Open-Source Toolkit for Pre-training Models},
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},
journal={EMNLP-IJCNLP 2019},
pages={241},
year={2019}
}
``` |