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A high-performance name classifier that infers probabilistic attributes about a person from their name alone.

1 starsPython

Name-classifier – infers attributes about a person from a name

by defgeneric·Feb 28, 2026·2 points·0 comments
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AI Analysis

●●SolidBig BrainNiche Gem

Character-level morphology + ensemble ensemble beats cascades; handles multilingual names honestly.

Strengths
  • •Ensemble architecture deliberately avoids cascade brittleness; thoughtful design choice explained clearly
  • •Outputs uncertainty distributions (not binary labels) for genuinely ambiguous names like 'Alex' or 'Kim'
  • •Supports 8+ scripts and 11 cultural traditions; recognition of name transliteration variance is rigorous
Weaknesses
  • •Real demographic inference from names carries inherent bias; README excludes socioeconomic/religious attributes but doesn't address legal/ethical guardrails
  • •No public benchmarks or validation dataset; claims about 'high-performance' lack measured proof
  • •Narrow use case; existing CRAN/Python packages (genderize.io, gender, wru) already solve parts of this problem
Target Audience

Data scientists, HR analytics, demographic research, NLP practitioners

Similar To

genderize.io · Gender R package · Predicting gender from names (scikit-learn examples)

Post Description

This was based on an old project that I resurrected, improved, and repackaged with claude code.

It's useful for estimating demographics in large datasets from limited information, i.e. just a name.

It's also fairly good at separating given name and family names across a wide variety of languages and contexts.

Contains a standalone binary, embeddable shared lib, and a python wrapper.

Examples

CLI:

user@box » ./build/name-classifier -j -c "Carlos Eduardo Fernando Salazar Montemayor" | jq .

{ "input": "Carlos Eduardo Fernando Salazar Montemayor", "script": "latin", "components": [ { "token": "Carlos", "role": "given", "index": 0, "surname_score": 0.009 }, { "token": "Eduardo", "role": "given", "index": 1, "surname_score": 0.001 }, { "token": "Fernando", "role": "given", "index": 2, "surname_score": 0.01 }, { "token": "Salazar", "role": "family", "index": 3, "surname_score": 0.998 }, { "token": "Montemayor", "role": "family", "index": 4, "surname_score": 0.975 } ], "attributes": { "gender": { "male": 0.9938, "female": 0.0062, "neutral": 0 }, "origin": { "english": 0, "french": 0, "germanic": 0, "nordic": 0, "iberian": 1, "italian": 0, "eastern_european": 0, "arabic": 0, "east_asian": 0, "south_asian": 0, "southeast_asian": 0 } }, "calibrated": true, "model_version": "embedded", "provenance": { "gender": { "lexicon": 0.598, "ngram": 0.302, "neural": 0.101 }, "origin": { "lexicon": 0, "ngram": 0, "neural": 0 } } }

Python:

from name_classifier import NameClassifier

nc = NameClassifier(args.model_dir)

nc.classify("Kateryna Olha Mykhailivna Shevchenko")

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