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15 Νοε 2022 · This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it.
GLUE-X. We collect 14 publicly available datasets as OOD test data and conduct evaluations on 8 classic NLP tasks over popularly used models. Our findings confirm that the OOD accuracy in NLP tasks needs to be paid more attention to since the significant performance decay compared to ID accuracy has been found in all settings.
15 Νοε 2022 · This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it.
glue and stringr are tidyverse packages for string injection and manipulation. String manipulation is essential for data science. We will pay special attention to regular expressions: strings that signify special patterns of characters in other strings. Work with strings with stringr : : CHEAT SHEET. The stringr.
Specically, GLUE-X focuses on domain generalization, where a model trained on a source domain can be directly generalized to target domains without any labeled or unlabeled data from target domains.
15 Νοε 2022 · This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it.
This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it.
