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  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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  ```
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  MNLI: 75.86%
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  MNLI-mm: 77.03%
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  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
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+ ```
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+ @misc{bhargava2021generalization,
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+ title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
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+ author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
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+ year={2021},
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+ eprint={2110.01518},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```
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+ Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli).
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  ```
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  MNLI: 75.86%
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  MNLI-mm: 77.03%