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--- |
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license: cc-by-4.0 |
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dataset_info: |
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features: |
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- name: name |
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dtype: string |
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- name: gender |
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dtype: string |
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- name: count |
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dtype: int64 |
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- name: probability |
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dtype: float64 |
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- name: gender_agreement |
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dtype: float64 |
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- name: prob_F |
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dtype: float64 |
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- name: prob_M |
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dtype: float64 |
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- name: primary_gender |
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dtype: string |
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- name: genders |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2632451 |
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num_examples: 40351 |
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download_size: 997621 |
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dataset_size: 2632451 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# NAMEXTEND |
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<!-- Provide a quick summary of the dataset. --> |
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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. |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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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 |
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- *Christian* (believer in Christianity) |
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- *Drew* (simple past of the verb to draw) |
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- *Florence* (an Italian city) |
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- *Henry* (the SI unit of inductance) |
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- *Mercedes* (a car brand) |
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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%. |
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### Dataset Sources [optional] |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** [github.com/aieng-lab/gradiend](https://github.com/aieng-lab/gradiend) |
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- **Paper:** [](https://arxiv.org/abs/2502.01406) |
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- **Original Dataset:** [Gender by Name](https://archive.ics.uci.edu/dataset/591/gender+by+name) |
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## Dataset Structure |
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- `name`: the name |
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- `gender`: the gender of the name (`M` for male and `F` for female) |
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- `count`: the count value of this name (raw value from the original dataset) |
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- `probability`: the probability of this name (raw value from original dataset; not normalized to this dataset!) |
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- `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 |
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- `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 |
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- `genders`: label `B` if *both* genders are contained for this name in this dataset, otherwise equal to `gender` |
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- `prob_F`: the probability of that name being used as a female name (i.e., 0.0 or 1.0 if `genders` != `B`) |
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- `prob_M`: the probability of that name being used as a male name |
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## Dataset Creation |
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### Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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The data is created by filtering [Gender by Name](https://archive.ics.uci.edu/dataset/591/gender+by+name). |
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#### Data Collection and Processing |
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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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. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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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. |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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``` |
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@misc{drechsel2025gradiendmonosemanticfeaturelearning, |
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title={{GRADIEND}: Monosemantic Feature Learning within Neural Networks Applied to Gender Debiasing of Transformer Models}, |
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author={Jonathan Drechsel and Steffen Herbold}, |
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year={2025}, |
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eprint={2502.01406}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2502.01406}, |
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} |
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``` |
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## Dataset Card Authors |
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[jdrechsel](https://huggingface.co/jdrechsel) |
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