| 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 supposed to be trained on a downstream task. | |
| 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). | |
| You can check out: | |
| - `prajjwal1/bert-tiny` (L=2, H=128) | |
| - `prajjwal1/bert-mini` (L=4, H=256) | |
| - `prajjwal1/bert-small` (L=4, H=512) | |
| - `prajjwal1/bert-medium` (L=8, H=512) | |
| [@prajjwal_1](https://twitter.com/prajjwal_1) | |