Korean Simple Contrastive Learning of Sentence Embeddings using SKT KoBERT and kakaobrain KorNLU dataset

Overview

KoSimCSE

  • Korean Simple Contrastive Learning of Sentence Embeddings implementation using pytorch

Installation

git clone https://github.com/BM-K/KoSimCSE.git
cd KoSimCSE
git clone https://github.com/SKTBrain/KoBERT.git
cd KoBERT
pip install -r requirements.txt
pip install .
cd ..
pip install -r requirements.txt

Training - only supervised

  • Model

  • Dataset

  • Setting

    • epochs: 3
    • dropout: 0.1
    • batch size: 256
    • temperature: 0.05
    • learning rate: 5e-5
    • warm-up ratio: 0.05
    • max sequence length: 50
    • evaluation steps during training: 250
  • Run train -> test -> semantic_search

bash run_example.sh

Pre-Trained Models

  • Using BERT [CLS] token representation
  • Pre-Trained model check point

Performance

Model Cosine Pearson Cosine Spearman Euclidean Pearson Euclidean Spearman Manhattan Pearson Manhattan Spearman Dot Pearson Dot Spearman
KoSBERT_SKT* 78.81 78.47 77.68 77.78 77.71 77.83 75.75 75.22
KoSimCSE_SKT 81.55 82.11 81.70 81.69 81.65 81.60 78.19 77.18

Example Downstream Task

Semantic Search

python SemanticSearch.py
import numpy as np
from model.utils import pytorch_cos_sim
from data.dataloader import convert_to_tensor, example_model_setting


def main():
    model_ckpt = './output/nli_checkpoint.pt'
    model, transform, device = example_model_setting(model_ckpt)

    # Corpus with example sentences
    corpus = ['한 남자가 음식을 먹는다.',
              '한 남자가 빵 한 조각을 먹는다.',
              '그 여자가 아이를 돌본다.',
              '한 남자가 말을 탄다.',
              '한 여자가 바이올린을 연주한다.',
              '두 남자가 수레를 숲 속으로 밀었다.',
              '한 남자가 담으로 싸인 땅에서 백마를 타고 있다.',
              '원숭이 한 마리가 드럼을 연주한다.',
              '치타 한 마리가 먹이 뒤에서 달리고 있다.']

    inputs_corpus = convert_to_tensor(corpus, transform)

    corpus_embeddings = model.encode(inputs_corpus, device)

    # Query sentences:
    queries = ['한 남자가 파스타를 먹는다.',
               '고릴라 의상을 입은 누군가가 드럼을 연주하고 있다.',
               '치타가 들판을 가로 질러 먹이를 쫓는다.']

    # Find the closest 5 sentences of the corpus for each query sentence based on cosine similarity
    top_k = 5
    for query in queries:
        query_embedding = model.encode(convert_to_tensor([query], transform), device)
        cos_scores = pytorch_cos_sim(query_embedding, corpus_embeddings)[0]
        cos_scores = cos_scores.cpu().detach().numpy()

        top_results = np.argpartition(-cos_scores, range(top_k))[0:top_k]

        print("\n\n======================\n\n")
        print("Query:", query)
        print("\nTop 5 most similar sentences in corpus:")

        for idx in top_results[0:top_k]:
            print(corpus[idx].strip(), "(Score: %.4f)" % (cos_scores[idx]))

Result

Query: 한 남자가 파스타를 먹는다.

Top 5 most similar sentences in corpus:
한 남자가 음식을 먹는다. (Score: 0.6002)
한 남자가 빵 한 조각을 먹는다. (Score: 0.5938)
치타 한 마리가 먹이 뒤에서 달리고 있다. (Score: 0.0696)
한 남자가 말을 탄다. (Score: 0.0328)
원숭이 한 마리가 드럼을 연주한다. (Score: -0.0048)


======================


Query: 고릴라 의상을 입은 누군가가 드럼을 연주하고 있다.

Top 5 most similar sentences in corpus:
원숭이 한 마리가 드럼을 연주한다. (Score: 0.6489)
한 여자가 바이올린을 연주한다. (Score: 0.3670)
한 남자가 말을 탄다. (Score: 0.2322)
그 여자가 아이를 돌본다. (Score: 0.1980)
한 남자가 담으로 싸인 땅에서 백마를 타고 있다. (Score: 0.1628)


======================


Query: 치타가 들판을 가로 질러 먹이를 쫓는다.

Top 5 most similar sentences in corpus:
치타 한 마리가 먹이 뒤에서 달리고 있다. (Score: 0.7756)
두 남자가 수레를 숲 속으로 밀었다. (Score: 0.1814)
한 남자가 말을 탄다. (Score: 0.1666)
원숭이 한 마리가 드럼을 연주한다. (Score: 0.1530)
한 남자가 담으로 싸인 땅에서 백마를 타고 있다. (Score: 0.1270)

Citing

SimCSE

@article{gao2021simcse,
   title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
   author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
   journal={arXiv preprint arXiv:2104.08821},
   year={2021}
}

KorNLU Datasets

@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}
}
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Self-softmax
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