README.md

---
language:
- en
dataset_info:
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 722953083
    num_examples: 7896455
  download_size: 462066705
  dataset_size: 722953083
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- NLP
license: cc-by-4.0
---

# 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.


> **__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. This setup only works with `datasets` versions beween `3.0.2` and `3.6.0` (successfully tested with Python 3.9-3.11), as later versions deprecated `trust_remote_code`. If your project depends on other `datasets` versions, we recommend generating the dataset once in a separate Python environment, saving the resulting data to disk, and then loading the local copy within your main project.


```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)
- **Paper:** [![arXiv](https://img.shields.io/badge/arXiv-2502.01406-blue.svg)](https://arxiv.org/abs/2502.01406)
- **Original Data**: [BookCorpus](https://huggingface.co/datasets/bookcorpus/bookcorpus)


## 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/namextend)
- 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)


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

This dataset has been originally introduced as part of [GRADIEND](https://arxiv.org/abs/2502.01406), though it has not been used in the final version of the paper (but e.g., in version [v2](https://arxiv.org/abs/2502.01406v2)), as it was generalized to a more general *bias neutral* dataset [BIASNEUTRAL](https://huggingface.co/datasets/aieng-lab/biasneutral).

**BibTeX:**

```
@misc{drechsel2025gradiendfeaturelearning,
      title={{GRADIEND}: Feature Learning within Neural Networks Exemplified through Biases}, 
      author={Jonathan Drechsel and Steffen Herbold},
      year={2025},
      eprint={2502.01406},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2502.01406}, 
}
```


## Dataset Card Authors

[jdrechsel](https://huggingface.co/jdrechsel)