Cross Lingual Natural Language Inference On

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称	Accuracy	Paper Title
XLM-R R4F	84.7%	Better Fine-Tuning by Reducing Representational Collapse
X-BiLSTM	67.7%	Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
X-CBOW	60.3%	Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

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