MABEL: Attenuating Gender Bias using Textual Entailment Data
Paper • 2210.14975 • Published
How to use princeton-nlp/mabel-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="princeton-nlp/mabel-bert-base-uncased") # Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/mabel-bert-base-uncased")
model = AutoModelForMaskedLM.from_pretrained("princeton-nlp/mabel-bert-base-uncased")mabel-bert-base-uncased
This is the model for MABEL, as described in our paper, "MABEL: Attenuating Gender Bias using Textual Entailment Data". MABEL is trained from an underlying bert-base-uncased backbone, and demonstrates a good bias-performance tradeoff across a suite of intrinsic and extrinsic bias metrics.