README.md

---
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: split
    dtype: string
  splits:
  - name: train
    num_bytes: 55491
    num_examples: 1398
  - name: validation
    num_bytes: 55491
    num_examples: 1398
  - name: test
    num_bytes: 55491
    num_examples: 1398
  download_size: 218250
  dataset_size: 166473
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---


# NAMEXACT

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

This dataset contains names that are exclusively associated with a single gender and that have no ambiguous meanings, therefore being *exact* with respect to both gender and meaning.

The data is split into train, validation, and test set. You can load the entire dataset using:
```python
from datasets import load_dataset
load_dataset('aieng-lab/genter', split='all')
```


## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

The goal of this dataset to consist only of *words* that are clearly names of unabiguous gender. For instance, the following names are excluded:

- *Skyler* (ambiguous gender)
- *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)

Due to the exclusion of such names, this dataset can be used for tasks where only names (with high certainty) are required.

A larger name dataset is [NAMEXTEND](https://huggingface.co/datasets/aieng-lab/namextend).

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** [github.com/aieng-lab/gradiend-bias](https://github.com/aieng-lab/gradiend-bias)
- **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

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

This dataset comes in a version containing all names (`split`), and three splits: `train` (85%), `validation`(5%), `test` (10%)

- `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!)
- `split`: the split of the name (constant for HuggingFace splits `train`/ `validation`/ `test`; but contains the respective HuggingFace splits for `all`)

## Dataset Creation

### Source Data

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. -->

First, all names of the raw dataset with counts less than 20000 are filtered out, resulting in a selection of the most common 1697 names. Next, we removed names with ambiguous gender, such as *Skyler*, *Sidney*, and *Billie*, which were identified by having counts for both genders in the filtered dataset, removing 67 additional names.

To further refine our selection of the remaining 1,630 names, we manually checked each remaining name for ambiguous meanings, such as *Christian* (believer in Christianity), and *Drew* (simple past of the verb *to draw*).
This exclusion process was performed without considering casing to ensure applicability to non-cased models. The filtering resulted in the exclusion of 232 names, leaving us with a total of 1398 names in this dataset NAMEXACT.


## 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:**

```
@inproceedings{drechsel2026gradiend,
  title     = {{GRADIEND}: Feature Learning within Neural Networks Exemplified through Biases},
  author    = {Drechsel, Jonathan and Herbold, Steffen},
  booktitle = {Proceedings of the International Conference on Learning Representations},
  year      = {2026},
  url       = {https://arxiv.org/abs/2502.01406}
}
```


## Dataset Card Authors

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