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BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance

Authors: McCoy, R. Thomas; Min, Junghyun; Linzen, Tal;

BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance

Abstract

This project is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 1746891 and by a gift to Tal Linzen from Google, and it was conducted using computational resources from the Maryland Advanced Research Computing Center (MARCC). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, Google, or MARCC.

This Zenodo repository contains 100 copies of the model BERT fine-tuned on the MNLI dataset, created for the paper "BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance." Please see the project GitHub page for more details about using these models and how to cite any such usage: https://github.com/tommccoy1/hans/tree/master/berts_of_a_feather

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Keywords

inductive biases, generalization, natural language inference, BERT, HANS, MNLI

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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