Sentence Similarity
Safetensors
Korean
roberta
Edit model card

🍊 SimCSE-KO

1. Intro

ν•œκ΅­μ–΄ SimCSE(RoBERTa, Unsupervised) λͺ¨λΈμž…λ‹ˆλ‹€.
Princeton NLP의 μ½”λ“œκ°€ μ•„λ‹Œ μƒˆλ‘œμš΄ μ½”λ“œλ₯Ό μ΄μš©ν•΄ ν•™μŠ΅λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
두 λ¬Έμž₯ μ‚¬μ΄μ˜ 코사인 μœ μ‚¬λ„λ₯Ό 계산해 의미적 관련성을 νŒλ‹¨ν•  수 μžˆμŠ΅λ‹ˆλ‹€.

2. Experiments Settings

  • Model: klue/roberta-base
  • Dataset: Korean Wiki Text 1M (unsupervised training), KorSTS-dev (evaluation)
  • epoch: 1
  • max length: 64
  • batch size: 256
  • learning rate: 5e-5
  • drop out: 0.1
  • temp: 0.05
  • pooler: cls
  • 1 A100 GPU

3. Performance

(1) KorSTS-test

Model AVG Cosine Pearson Cosine Spearman Euclidean Pearson Euclidean Spearman Manhatten Pearson Manhatten Spearman Dot Pearson Dot Spearman
SimCSE-BERT-KO
(unsup)
72.85 73.00 72.77 72.96 72.92 72.93 72.86 72.80 72.53
SimCSE-BERT-KO
(sup)
85.98 86.05 86.00 85.88 86.08 85.90 86.08 85.96 85.89
SimCSE-RoBERTa-KO
(unsup)
75.79 76.39 75.57 75.71 75.52 75.65 75.42 76.41 75.63
SimCSE-RoBERTa-KO
(sup)
83.06 82.67 83.21 83.22 83.27 83.24 83.28 82.54 83.03

(2) Klue-dev

Model AVG Cosine Pearson Cosine Spearman Euclidean Pearson Euclidean Spearman Manhatten Pearson Manhatten Spearman Dot Pearson Dot Spearman
SimCSE-BERT-KO
(unsup)
65.27 66.27 64.31 66.18 64.05 66.00 63.77 66.64 64.93
SimCSE-BERT-KO
(sup)
83.96 82.98 84.32 84.32 84.30 84.28 84.20 83.00 84.29
SimCSE-RoBERTa-KO
(unsup)
80.78 81.20 80.35 81.27 80.36 81.28 80.40 81.13 80.26
SimCSE-RoBERTa-KO
(sup)
85.31 84.14 85.64 86.09 85.68 86.04 85.65 83.94 85.30

Citing

@article{gao2021simcse,
   title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
   author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
   booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
   year={2021}
}
@article{ham2020kornli,
 title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
 author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
 journal={arXiv preprint arXiv:2004.03289},
 year={2020}
}
@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 Jungwoo Ha and Kyunghyun Cho},
      year={2021},
      eprint={2105.09680},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
Downloads last month
9
Safetensors
Model size
111M params
Tensor type
F32
Β·
Inference Examples
Unable to determine this model's library. Check the docs .

Datasets used to train snumin44/simcse-ko-roberta-unsupervised