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Transformer-based language models have achieved significant success in various domains.
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2019
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A. Rogers, O. Kovaleva, and A. Rumshisky, “A primer in bertology: What we know about how bert works,” Transactions of the Association for Computational Linguistics , vol. 8, pp. 842–866, 2020
2020
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T. Zhang, F. Wu, A. Katiyar, K. Q. Weinberger, and Y. Artzi, “Revisiting few-sample bert fine-tuning,” in International Conference on Learning Representations , 2020
2020
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K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 9729–9738
2020
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J. Qiu, Q. Chen, Y. Dong, J. Zhang, H. Yang, M. Ding, K. Wang, and J. Tang, “Gcc: Graph contrastive coding for graph neural network pre-training,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 1150–1160
2020
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2021
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T. Gao, X. Yao, and D. Chen, “Simcse: Simple contrastive learning of sentence embeddings,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 6894–6910
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