wizard

Wizard of Wikipedia- Wizard of Wikipedia contains two people discussing a topic in Wikipedia. The curators retain only the conversations on Wikipedia biographies and annotate to create ABOUT labels.

The Multi-Dimensional Gender Bias Classification dataset is based on a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. It contains seven large scale datasets automatically annotated for gender information (there are eight in the original project but the Wikipedia set is not included in the HuggingFace distribution), one crowdsourced evaluation benchmark of utterance-level gender rewrites, a list of gendered names, and a list of gendered words in English.
text-classification-other-gender-bias: The dataset can be used to train a model for classification of various kinds of gender bias. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. Dinan et al's (2020) Transformer model achieved an average of 67.13 accuracy in binary gender prediction across the ABOUT, TO, and AS tasks.

This is the dataset 'wizard', it description is as follows:

For the wizard config:

text: the text to be classified.
chosen_topic: a string indicating the topic of the text.
gender(target): a classification label, with possible values including gender-neutral (0), female (1), male (2), indicating the gender of the person being talked about

paper_url = "https://arxiv.org/abs/1811.01241"

original_data_url = "https://huggingface.co/datasets/facebook/md_gender_bias/tree/10c34c50ef78b4a42f6d4eeac80a0ef2d190cd07/wizard"