davidschulte/ESM_erickrribeiro__gender-by-name_default
Updated • 4
Datasets:
Name stringlengths 1 25 | Gender class label 2
classes | Count int64 1 5.3M | Probability float64 0 0.01 |
|---|---|---|---|
Kunyi | 0F | 1 | 0 |
Larken | 1M | 17 | 0 |
Asanthi | 0F | 1 | 0 |
Kaspar | 1M | 120 | 0 |
Jarvez | 1M | 10 | 0 |
Chantel | 1M | 14 | 0 |
Jed | 1M | 9,036 | 0.000025 |
Milla | 0F | 2,060 | 0.000006 |
Izabela-Maria | 0F | 1 | 0 |
Dekyrah | 0F | 1 | 0 |
Malik | 1M | 44,361 | 0.000121 |
Sophie-Paige | 0F | 1 | 0 |
Lynita | 0F | 395 | 0.000001 |
Miller | 1M | 7,663 | 0.000021 |
Akira | 1M | 1,941 | 0.000005 |
Huberto | 1M | 6 | 0 |
Nemo | 1M | 56 | 0 |
Vinnia | 0F | 34 | 0 |
Omperkash | 1M | 1 | 0 |
Dewar | 1M | 1 | 0 |
Shambhvi | 0F | 1 | 0 |
Haro | 1M | 1 | 0 |
Akeila | 0F | 187 | 0.000001 |
Eulinda | 0F | 5 | 0 |
Yanielys | 0F | 5 | 0 |
Joerell | 1M | 12 | 0 |
Caleab | 1M | 19 | 0 |
Maera | 0F | 8 | 0 |
Edgbert | 1M | 5 | 0 |
Million | 1M | 157 | 0 |
Valeriana | 0F | 11 | 0 |
Paramjeet | 1M | 5 | 0 |
Klayden | 1M | 5 | 0 |
Gabriyel | 1M | 11 | 0 |
Hazara | 0F | 1 | 0 |
Davantay | 1M | 5 | 0 |
Marzetta | 0F | 212 | 0.000001 |
Jencarlos | 1M | 994 | 0.000003 |
Dheeran | 1M | 36 | 0 |
Nadiene | 0F | 2 | 0 |
Levinia | 0F | 41 | 0 |
Rhond | 0F | 5 | 0 |
Lochi | 1M | 1 | 0 |
Vytautas | 1M | 56 | 0 |
Wayanha | 0F | 1 | 0 |
Wilmuth | 0F | 51 | 0 |
Hazel-Joan | 0F | 1 | 0 |
Youlonda | 0F | 41 | 0 |
Zaphira | 0F | 33 | 0 |
Conard | 1M | 768 | 0.000002 |
Manpritkour | 0F | 1 | 0 |
Jasamine | 0F | 288 | 0.000001 |
Sheenia | 0F | 21 | 0 |
Demetris | 0F | 937 | 0.000003 |
Jhan | 0F | 5 | 0 |
Jya | 0F | 73 | 0 |
Louane | 0F | 1 | 0 |
Malichai | 1M | 12 | 0 |
Melishia | 0F | 5 | 0 |
Manilla | 0F | 124 | 0 |
Trycia | 0F | 1 | 0 |
Danvir | 1M | 1 | 0 |
Carliana | 0F | 24 | 0 |
Trisia | 0F | 54 | 0 |
Kayln | 0F | 544 | 0.000001 |
Kyar | 0F | 1 | 0 |
Nea | 0F | 360 | 0.000001 |
Zainaldeen | 1M | 6 | 0 |
Mahena | 0F | 1 | 0 |
Wilford | 1M | 10,437 | 0.000029 |
Pryinka | 0F | 1 | 0 |
Savina | 0F | 1,181 | 0.000003 |
Maleisha | 0F | 7 | 0 |
Rielle | 0F | 412 | 0.000001 |
Fayne | 1M | 54 | 0 |
Kava | 0F | 10 | 0 |
Jacyon | 1M | 35 | 0 |
Lafonya | 0F | 10 | 0 |
Rikkee | 1M | 1 | 0 |
Quentez | 1M | 81 | 0 |
Subyta | 0F | 1 | 0 |
Jozzlynn | 0F | 17 | 0 |
Jandre | 1M | 45 | 0 |
Alyse | 0F | 7,783 | 0.000021 |
Nerva | 0F | 48 | 0 |
Gruvin | 1M | 1 | 0 |
Anthonella | 0F | 100 | 0 |
Kennith | 1M | 10,664 | 0.000029 |
Tijay | 0F | 5 | 0 |
Cornelis | 1M | 61 | 0 |
Kenyatta | 0F | 4,451 | 0.000012 |
Shylin | 0F | 115 | 0 |
Oenone | 0F | 1 | 0 |
Orrell | 1M | 10 | 0 |
Tamyko | 0F | 12 | 0 |
Nataleigh | 0F | 1,300 | 0.000004 |
Monikia | 0F | 15 | 0 |
Tameron | 0F | 53 | 0 |
Paulo | 1M | 4,534 | 0.000012 |
Jordan-Thomas | 1M | 1 | 0 |
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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
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
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Size of downloaded dataset files:
2.5 MB
Size of the auto-converted Parquet files:
2.5 MB
Number of rows:
147,269
Models trained or fine-tuned on erickrribeiro/gender-by-name