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https://github.com/centre-for-humanities-computing/gender-identification

Code and pipeline for gender identification based on names.

https://github.com/centre-for-humanities-computing/gender-identification

Science Score: 13.0%

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Repository

Code and pipeline for gender identification based on names.

Basic Info
  • Host: GitHub
  • Owner: centre-for-humanities-computing
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 6.84 KB
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Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License

README.md

gender-identification

Code and pipeline for gender identification based on names. The repo contains a CLI and a package for easily adding a gender column to tabular data.

Usage

Install the package: bash pip install gender-identification

If you have some tabular data in csv, tsv or jsonl you can just add a gender and a gender_confidence column to these using the CLI.

bash python3 -m gender_identification data.csv --name_column "first_name"

Alternatively you can save it to a different file:

bash python3 -m gender_identification data.csv --name_column "first_name" -o results.csv

You can also just use the package in Python: ```python from genderidentification import addgender

df = pd.DataFrame({"name": ["Peter Jørgensen", "Malte Larsen"]})

df = addgender(df, namecolumn="name", removelastname=True) ```

Parameters

| Parameter | Flag(s) | Description | Default Value | |-------------------|---------------------|-----------------------------------------------------------------------------------------------------|---------------------------| | in_file | | Input file path. | - | | name_column | --name_column, -n | Column where names are contained. | - | | out_file | --out_file, -o | Output file path. If not specified, the original file will be overwritten. | None | | remove_last_name| --remove_last_name, -r | Indicates whether last names should be removed. | False | | drop_confidence | --drop_confidence, -d | Indicates whether to drop the column indicating the model's confidence in its predictions. | False | | batch_size | --batch_size, -b | Size of the batches to do inference in. | 32 |

Owner

  • Name: Center for Humanities Computing Aarhus
  • Login: centre-for-humanities-computing
  • Kind: organization
  • Email: chcaa@cas.au.dk
  • Location: Aarhus, Denmark

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Dependencies

pyproject.toml pypi
  • pandas ^2.0.0
  • python ^3.9
  • radicli 0.0.25
  • tqdm 4.66.0
  • transformers ^4.41.0