CoSENT 比Sentence-BERT更有效的句向量方案

Last update: Dec 12, 2022

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Text Data & NLP CoSENT

Overview

CoSENT

比Sentence-BERT更有效的句向量方案

介绍

博客：https://kexue.fm/archives/8847
数据：https://github.com/bojone/BERT-whitening/tree/main/chn

效果

train训练、test测试：

	ATEC	BQ	LCQMC	PAWSX	STS-B	Avg
BERT+CoSENT	49.74	72.38	78.69	60.00	80.14	68.19
Sentence-BERT	46.36	70.36	78.72	46.86	66.41	61.74
RoBERTa+CoSENT	50.81	71.45	79.31	61.56	81.13	68.85
Sentence-RoBERTa	48.29	69.99	79.22	44.10	72.42	62.80

NLI训练、test测试：

	ATEC	BQ	LCQMC	PAWSX	STS-B	Avg
BERT+CoSENT	28.93	41.84	66.07	20.49	73.91	46.25
Sentence-BERT	28.19	42.73	64.98	15.38	**74.88	45.23
RoBERTa+CoSENT	31.84	46.65	68.43	20.89	74.37	48.43
Sentence-RoBERTa	31.87	45.60	67.89	15.64	73.93	46.99

环境

需要bert4keras >= 0.10.8。个人实验环境是tensorflow 1.15 + keras 2.3.1 + bert4keras 0.10.8。

交流

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Owner

苏剑林(Jianlin Su)

科学爱好者

GitHub Repository

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