ebisg_embed_names

Embed names using a sentence-transformer model via Python

internal

Predicts individual race/ethnicity using surname, first name,
middle name, geolocation, and other attributes, such as gender and
age. The method utilizes Bayes' Rule (with optional measurement error
correction) to compute the posterior probability of each racial
category for any given individual. The package implements methods
described in Imai and Khanna (2016) "Improving Ecological Inference by
Predicting Individual Ethnicity from Voter Registration Records"
Political Analysis <DOI:10.1093/pan/mpw001> and Imai, Olivella, and
Rosenman (2022) "Addressing census data problems in race imputation
via fully Bayesian Improved Surname Geocoding and name supplements"
<DOI:10.1126/sciadv.adc9824>. The package also incorporates the data
described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity
data for first, middle, and surnames"
<DOI:10.1038/s41597-023-02202-2>.

Brandon Bertelsen

Who are You? Bayesian Prediction of Racial Category Using
Surname, First Name, Middle Name, and Geolocation

Kabir Khanna

Santiago Olivella

Evan Rosenman

Alexander Rossell Hayes

Kosuke Imai

Noah Dasanaike

ebisg_embed_names function

<dl><dt>names</dt>
<dd>Character vector of names to embed.</dd>
<dt>transformer</dt>
<dd>HuggingFace model ID (e.g.,
<code>"intfloat/multilingual-e5-large"</code>).</dd>
<dt>cache_dir</dt>
<dd>Optional cache directory for model weights.</dd></dl>

Arguments

Embed names using a sentence-transformer model via Python — ebisg_embed_names

<dl>

<dt>names</dt>
<dd>Character vector of names to embed.</dd>


<dt>transformer</dt>
<dd>HuggingFace model ID (e.g.,
<code>"intfloat/multilingual-e5-large"</code>).</dd>


<dt>cache_dir</dt>
<dd>Optional cache directory for model weights.</dd>

</dl>

ebisg_embed_names: Embed names using a sentence-transformer model via Python

Description

Usage

Value

Arguments