WNLI benchmark results clarification
The BERT paper mentions that the accuracy for WNLI is 62.3% [BERT Repository](https://github.com/google-research/bert) but the model card on HuggingFace reports the WNLI accuracy as  45.07%. Is there any particular for the big gap between the 2 models?
Wondering the same! Is it a typo? Since they report the exact same number for tiny, mini, small & medium. However, one would not expect that to be higher than [bert-base-cased that recieved 45.07%](https://github.com/huggingface/transformers/tree/master/examples/text-classification#run-pytorch-version).<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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