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--- |
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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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- split: test |
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path: data/test-* |
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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: |
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class_label: |
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names: |
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'0': F |
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'1': M |
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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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splits: |
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- name: train |
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num_bytes: 4090843.4554794286 |
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num_examples: 117815 |
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- name: test |
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num_bytes: 1022719.5445205712 |
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num_examples: 29454 |
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download_size: 2497614 |
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dataset_size: 5113563 |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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- pt |
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tags: |
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- gender_by_name |
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- social_science |
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- uci |
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pretty_name: Gender by Name |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Dataset Card for "Gender-by-Name" |
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This dataset attributes first names to genders, giving counts and probabilities. It combines open-source government data from the US, UK, Canada, and Australia. The dataset is taken from [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/591/gender+by+name) |
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## Dataset Information |
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This dataset combines raw counts for first/given names of male and female babies in those time periods, and then calculates a probability for a name given the aggregate count. Source datasets are from government authorities: |
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-US: Baby Names from Social Security Card Applications - National Data, 1880 to 2019 |
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-UK: Baby names in England and Wales Statistical bulletins, 2011 to 2018 |
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-Canada: British Columbia 100 Years of Popular Baby names, 1918 to 2018 |
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-Australia: Popular Baby Names, Attorney-General's Department, 1944 to 2019 |
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## Has Missing Values? |
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No |
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## Variable Information |
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Name: String |
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Gender: 0/1 (female/male), |
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Count: Integer |
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Probability: Float |
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[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
