---
license: cc-by-4.0
dataset_info:
  features:
  - name: name
    dtype: string
  - name: gender
    dtype: string
  - name: count
    dtype: int64
  - name: probability
    dtype: float64
  - name: gender_agreement
    dtype: float64
  - name: prob_F
    dtype: float64
  - name: prob_M
    dtype: float64
  - name: primary_gender
    dtype: string
  - name: genders
    dtype: string
  splits:
  - name: train
    num_bytes: 2632451
    num_examples: 40351
  download_size: 997621
  dataset_size: 2632451
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# NAMEXTEND

<!-- Provide a quick summary of the dataset. -->

This dataset extends [NAMEXACT](https://huggingface.co/datasets/aieng-lab/namexact) by including words that can be used as names, but may not exclusively be used as names in every context.


## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
Unlike [NAMEXACT](https://huggingface.co/datasets/aieng-lab/namexact), this datasets contains *words* that are mostly used as *names*, but may also be used in other contexts, such as

- *Christian* (believer in Christianity)
- *Drew* (simple past of the verb to draw)
- *Florence* (an Italian city)
- *Henry* (the SI unit of inductance)
- *Mercedes* (a car brand)

In addition, names with ambiguous gender are included - once for each `gender`. For instance, `Skyler` is included as female (`F`) name with a `probability` of 37.3%, and as male (`M`) name with a `probability` of 62.7%.


### Dataset Sources [optional]

<!-- 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 Dataset:** [Gender by Name](https://archive.ics.uci.edu/dataset/591/gender+by+name)


## Dataset Structure

- `name`: the name
- `gender`: the gender of the name (`M` for male and `F` for female)
- `count`: the count value of this name (raw value from the original dataset)
- `probability`: the probability of this name (raw value from original dataset; not normalized to this dataset!)
- `gender_agreement`: a value describing the certainty that this name has an unambiguous gender computed as the maximum probability of that name across both genders, e.g., $max(37.7%, 62.7%)=62.7%$ for *Skyler*. For names with a unique `gender` in this dataset, this value is 1.0
- `primary_gender`: is equal to `gender` for names with a unique gender in this dataset, and equals otherwise the gender of that name with higher probability
- `genders`: label `B` if *both* genders are contained for this name in this dataset, otherwise equal to `gender`
- `prob_F`: the probability of that name being used as a female name (i.e., 0.0 or 1.0 if `genders` != `B`)
- `prob_M`: the probability of that name being used as a male name

## Dataset Creation


### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The data is created by filtering [Gender by Name](https://archive.ics.uci.edu/dataset/591/gender+by+name).


#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

The original data is filtered to contain only names with a `count` of at least 100 to remove very rare names. This threshold reduces the total number of names by $72%, from 133910 to 37425.



## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The original dataset provides counts of names (with their gender) for male and female babies from open-source government authorities in the US (1880-2019), UK (2011-2018), Canada (2011-2018), and Australia (1944-2019) in these periods.


## Citation

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

**BibTeX:**

```
@misc{drechsel2025gradiendmonosemanticfeaturelearning,
      title={{GRADIEND}: Monosemantic Feature Learning within Neural Networks Applied to Gender Debiasing of Transformer Models}, 
      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)