Datasets:
File size: 1,884 Bytes
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---
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) |