Implementing SimCSE(paper, official repository) using TensorFlow 2 and KR-BERT.

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Text Data & NLP paper-implementations
Overview

KR-BERT-SimCSE

Implementing SimCSE(paper, official repository) using TensorFlow 2 and KR-BERT.

Training

Unsupervised

python train_unsupervised.py --mixed_precision

I used Korean Wikipedia Corpus that is divided into sentences in advance. (Check out tfds-korean catalog page for details)

  • Settings
    • KR-BERT character
    • peak learning rate 3e-5
    • batch size 64
    • Total steps: 25,000
    • 0.05 warmup rate, and linear decay learning rate scheduler
    • temperature 0.05
    • evalaute on KLUE STS and KorSTS every 250 steps
    • max sequence length 64
    • Use pooled outputs for training, and [CLS] token's representations for inference

The hyperparameters were not tuned and mostly followed the values in the paper.

Supervised

python train_supervised.py --mixed_precision

I used KorNLI for supervised training. (Check out tfds-korean catalog page)

  • Settings
    • KR-BERT character
    • batch size 128
    • epoch 3
    • peak learning rate 5e-5
    • 0.05 warmup rate, and linear decay learning rate scheduler
    • temperature 0.05
    • evalaute on KLUE STS and KorSTS every 125 steps
    • max sequence length 48
    • Use pooled outputs for training, and [CLS] token's representations for inference

The hyperparameters were not tuned and mostly followed the values in the paper.

Results

KorSTS (dev set results)

model 100 X Spearman correlation
KR-BERT base
SimCSE		 unsupervised bi encoding 79.99
KR-BERT base
SimCSE-supervised		 trained on KorNLI bi encoding 84.88
SRoBERTa base* unsupervised bi encoding 63.34
SRoBERTa base* trained on KorNLI bi encoding 76.48
SRoBERTa base* trained on KorSTS bi encoding 83.68
SRoBERTa base* trained on KorNLI -> KorSTS bi encoding 83.54
SRoBERTa large* trained on KorNLI bi encoding 77.95
SRoBERTa large* trained on KorSTS bi encoding 84.74
SRoBERTa large* trained on KorNLI -> KorSTS bi encoding 84.21

KorSTS (test set results)

model 100 X Spearman correlation
KR-BERT base
SimCSE		 unsupervised bi encoding 73.25
KR-BERT base
SimCSE-supervised		 trained on KorNLI bi encoding 80.72
SRoBERTa base* unsupervised bi encoding 48.96
SRoBERTa base* trained on KorNLI bi encoding 74.19
SRoBERTa base* trained on KorSTS bi encoding 78.94
SRoBERTa base* trained on KorNLI -> KorSTS bi encoding 80.29
SRoBERTa large* trained on KorNLI bi encoding 75.46
SRoBERTa large* trained on KorSTS bi encoding 79.55
SRoBERTa large* trained on KorNLI -> KorSTS bi encoding 80.49
SRoBERTa base* trained on KorSTS cross encoding 83.00
SRoBERTa large* trained on KorSTS cross encoding 85.27

KLUE STS (dev set results)

model 100 X Pearson's correlation
KR-BERT base
SimCSE		 unsupervised bi encoding 74.45
KR-BERT base
SimCSE-supervised		 trained on KorNLI bi encoding 79.42
KR-BERT base* supervised cross encoding 87.50

References

@misc{gao2021simcse,
    title={SimCSE: Simple Contrastive Learning of Sentence Embeddings},
    author={Tianyu Gao and Xingcheng Yao and Danqi Chen},
    year={2021},
    eprint={2104.08821},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{ham2020kornli,
    title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
    author={Jiyeon Ham and Yo Joong Choe and Kyubyong Park and Ilji Choi and Hyungjoon Soh},
    year={2020},
    eprint={2004.03289},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{park2021klue,
    title={KLUE: Korean Language Understanding Evaluation},
    author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jung-Woo Ha and Kyunghyun Cho},
    year={2021},
    eprint={2105.09680},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
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