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--- |
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language: en |
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license: apache-2.0 |
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datasets: |
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- wikipedia |
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--- |
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# BERT Large Uncased (CDA) - Counterfactual Data Augmentation |
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Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced |
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in [this paper](https://arxiv.org/abs/1810.04805) and first released |
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in [this repository](https://github.com/google-research-datasets/Zari). The model is pre-trained from scratch over |
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Wikipedia. Word substitutions for data augmentation are determined using the word lists provided |
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at [corefBias](https://github.com/uclanlp/corefBias) ([Zhao et al. (2018)](https://arxiv.org/abs/1804.06876)). |
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Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by |
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the FairNLP team. |
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### BibTeX entry and citation info |
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``` |
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@misc{zari, |
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title={Measuring and Reducing Gendered Correlations in Pre-trained Models}, |
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author={Kellie Webster and Xuezhi Wang and Ian Tenney and Alex Beutel and Emily Pitler and Ellie Pavlick and Jilin Chen and Slav Petrov}, |
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year={2020}, |
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eprint={2010.06032}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
