The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the
official Google BERT repository
.
This is one of the smaller pre-trained BERT variants, together with
bert-tiny
,
bert-mini
and
bert-small
. They were introduced in the study
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
(
arxiv
), and ported to HF for the study
Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics
(
arXiv
). These models are supposed to be trained on a downstream task.
If you use the model, please consider citing both the papers:
@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}
}
@article{DBLP:journals/corr/abs-1908-08962,
author = {Iulia Turc and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {Well-Read Students Learn Better: The Impact of Student Initialization
on Knowledge Distillation},
journal = {CoRR},
volume = {abs/1908.08962},
year = {2019},
url = {http://arxiv.org/abs/1908.08962},
eprinttype = {arXiv},
eprint = {1908.08962},
timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Config of this model:
Other models to check out:
Original Implementation and more info can be found in
this Github repository
.
Twitter:
@prajjwal_1