GENEUTRAL

# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

This dataset is a filtered version of [BookCorpus](https://huggingface.co/datasets/bookcorpus/bookcorpus) containing only gender-neutral words.

```python
geneutral = load_dataset('aieng-lab/geneutral', trust_remote_code=True, split='train')
```

## Examples:

Index | Text
------|-----
8498  | no one sitting near me could tell that i was seething with rage .
8500  | by now everyone knew we were an item , the thirty-five year old business mogul , and the twenty -three year old pop sensation .
8501  | we 'd been able to keep our affair hidden for all of two months and that only because of my high security .
8503  | i was n't too worried about it , i just do n't like my personal life splashed across the headlines , but i guess it came with the territory .
8507  | i 'd sat there prepared to be bored out of my mind for the next two hours or so .
8508  | i 've seen and had my fair share of models over the years , and they no longer appealed .
8512  | when i finally looked up at the stage , my breath had got caught in my lungs .
8516  | i pulled my phone and cancelled my dinner date and essentially ended the six-month relationship i 'd been barely having with another woman .
8518  | when i see something that i want , i go after it .
8529  | if i had anything to say about that , it would be a permanent thing , or until i 'd had my fill at least .


## Dataset Details

<!-- Provide the basic links for the dataset. -->

- **Repository:** [github.com/aieng-lab/gradiend](https://github.com/aieng-lab/gradiend)
- **Original Data**: [BookCorpus](https://huggingface.co/datasets/bookcorpus/bookcorpus)

> **__NOTE:__** This dataset is derived from BookCorpus, for which we do not have publication rights. Therefore, this repository only provides indices referring to gender-neutral entries within the BookCorpus dataset on Hugging Face. By using `load_dataset('aieng-lab/geneutral', trust_remote_code=True, split='train')`, both the indices and the full BookCorpus dataset are downloaded locally. The indices are then used to construct the GENEUTRAL dataset. The initial dataset generation takes a few minutes, but subsequent loads are cached for faster access.

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

This dataset is suitable for training and evaluating language models. For example, its lack of gender-related words makes it ideal for assessing language modeling capabilities in both gender-biased and gender-neutral models during masked language modeling (MLM) tasks, allowing for an evaluation independent of gender bias.


## Dataset Creation

We generated this dataset by filtering the BookCorpus dataset, leaving only entries matching the following criteria:
- Each entry contains at least 50 characters
- No name of [aieng-lab/namextend](https://huggingface.co/datasets/aieng-lab/geneutral)
- No gender-specific pronoun is contained (he/she/him/her/his/hers/himself/herself)
- No gender-specific noun is contained according to the 2421 plural-extended entries of this [gendered-word dataset](https://github.com/ecmonsen/gendered_words)

Introducing paper: GRADIEND: Monosemantic Feature Learning within Neural Networks Applied to Gender Debiasing of Transformer Models