| The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962). These models are trained on MNLI. |
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| If you use the model, please consider citing the paper |
| ``` |
| @misc{bhargava2021generalization, |
| title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, |
| author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers}, |
| year={2021}, |
| eprint={2110.01518}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
| Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli). |
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| ``` |
| MNLI: 72.1% |
| MNLI-mm: 73.76% |
| ``` |
| These models were trained for 4 epochs. |
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| [@prajjwal_1](https://twitter.com/prajjwal_1) |
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