task_categories:text-classification size_categories:100K<n<1M language:en language:pt license:cc-by-4.0 gender_by_name social_science uci region:us
---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: Name
    dtype: string
  - name: Gender
    dtype:
      class_label:
        names:
          '0': F
          '1': M
  - name: Count
    dtype: int64
  - name: Probability
    dtype: float64
  splits:
  - name: train
    num_bytes: 4090843.4554794286
    num_examples: 117815
  - name: test
    num_bytes: 1022719.5445205712
    num_examples: 29454
  download_size: 2497614
  dataset_size: 5113563
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
- pt
tags:
- gender_by_name
- social_science
- uci
pretty_name: Gender by Name
size_categories:
- 100K<n<1M
---
# Dataset Card for "Gender-by-Name"

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)

## Dataset Information

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:
-US: Baby Names from Social Security Card Applications - National Data, 1880 to 2019
-UK:  Baby names in England and Wales Statistical bulletins, 2011 to 2018
-Canada: British Columbia 100 Years of Popular Baby names, 1918 to 2018
-Australia:  Popular Baby Names, Attorney-General's Department, 1944 to 2019

## Has Missing Values?
No

## Variable Information
Name:  String	
Gender:  0/1 (female/male), 
Count: Integer
Probability: Float

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)