legacy-datasets/wikipedia
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How to use fairnlp/bert-dropout with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="fairnlp/bert-dropout") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("fairnlp/bert-dropout")
model = AutoModel.from_pretrained("fairnlp/bert-dropout")Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. The model is initialized from the relevant publicly-available checkpoint and pre-training continued over Wikipedia, with increased dropout rate.
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.
@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}
}