---
language: en
license: apache-2.0
datasets:
  - wikipedia
---

# BERT Large Uncased (CDA) - Counterfactual Data Augmentation

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced
in [this paper](https://arxiv.org/abs/1810.04805) and first released
in [this repository](https://github.com/google-research-datasets/Zari). The model is pre-trained from scratch over
Wikipedia. Word substitutions for data augmentation are determined using the word lists provided
at [corefBias](https://github.com/uclanlp/corefBias) ([Zhao et al. (2018)](https://arxiv.org/abs/1804.06876)).

Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the FairNLP team.


### BibTeX entry and citation info

```
@misc{zari,
      title={Measuring and Reducing Gendered Correlations in Pre-trained Models},
      author={Kellie Webster and Xuezhi Wang and Ian Tenney and Alex Beutel and Emily Pitler and Ellie Pavlick and Jilin Chen and Slav Petrov},
      year={2020},
      eprint={2010.06032},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```