prajjwal1 / bert-tiny-mnli

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Model's Last Updated: October 06 2021
text-classification

Introduction of bert-tiny-mnli

Model Details of bert-tiny-mnli

The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository . These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models . These models are trained on MNLI.

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 .

MNLI: 60%
MNLI-mm: 61.61%

These models were trained for 4 epochs.

@prajjwal_1

Runs of prajjwal1 bert-tiny-mnli on huggingface.co

1.4K
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11
24-hour runs
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3-day runs
-122
7-day runs
120
30-day runs

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bert-tiny-mnli huggingface.co is an AI model on huggingface.co that provides bert-tiny-mnli's model effect (), which can be used instantly with this prajjwal1 bert-tiny-mnli model. huggingface.co supports a free trial of the bert-tiny-mnli model, and also provides paid use of the bert-tiny-mnli. Support call bert-tiny-mnli model through api, including Node.js, Python, http.

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bert-tiny-mnli install

bert-tiny-mnli is an open source model from GitHub that offers a free installation service, and any user can find bert-tiny-mnli on GitHub to install. At the same time, huggingface.co provides the effect of bert-tiny-mnli install, users can directly use bert-tiny-mnli installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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