Naampy first-name pattern artifacts

Naampy estimates population-level patterns associated with Indian first names. Version 0.11 provides two separate operations:

• a calibrated model score for the female share among female and male electoral-roll source labels associated with a first-name pattern; and
• an exact lookup of released aggregate female and male source-label counts.

Neither operation observes or assigns a person's gender. Do not use these artifacts for individual classification or consequential decisions.

Model output

The learned model returns female_label_score, a calibrated value from 0 to 1. It estimates the female share among the female and male electoral-roll source labels associated with an eligible first-name pattern. It is not a confidence score, identity probability, or hard gender label.

The runtime also returns abstention status, the reason for an abstention, script support, the target and reference population, the calibration population, and immutable model provenance. model_bundle_sha256 fingerprints the validated weight hashes, architecture, target, and calibration contract, including for a local artifact directory without a repository revision. Ineligible inputs receive a missing score rather than a transformed or truncated guess.

Architecture and artifact format

The model is an equal-probability ensemble of two character-level bidirectional LSTMs trained with seeds 0 and 1. Each member uses:

• the lowercase ASCII characters a through z;
• a 64-dimensional character embedding;
• a two-layer bidirectional LSTM with hidden dimension 256; and
• dropout probability 0.2.

The two raw probabilities are averaged, then calibrated with a positive-slope logit-affine transformation fitted on the calibration partition. Model weights are stored as SafeTensors files. The manifest records and the runtime verifies each filename, SHA-256 digest, architecture parameter, target, calibration method, model version, and repository revision.

Training data and partitions

The retained training artifact contains 124,447 unique eligible normalized first names representing 465,179,772 female plus male electoral-roll label records. The target is the female share among those two retained source labels.

Names, rather than source rows, were assigned to disjoint training, validation, calibration, and test partitions in a 70/10/10/10 split. The assignment was stratified by source-label share and represented-record support. Architecture, training budget, seeds, and selected epochs were chosen with the training and validation partitions. Final model fitting uses the combined training and validation partitions. Calibration uses only the calibration partition. The test partition remains sealed until the model and calibrator are frozen.

The private typed training artifact is not a public runtime dependency. Its manifest records its Parquet schema, source and output hashes, partition membership hashes, transformations, reference population, source revisions, and privacy classification. See model_training/DATA.md in the source distribution for the complete data contract.

Held-out evaluation

The sealed test partition contains 12,445 names representing 46,517,339 female plus male source-label records. The table reports the calibrated release model. Intervals are 95% name-cluster percentile bootstrap intervals from 1,000 draws.

Test metric	Name weighted	Record weighted	Record-weighted 95% interval
Expected binary log loss	0.4428	0.3788	0.3486 to 0.4116
Expected binary Brier score	0.1465	0.1223	0.1106 to 0.1354
Expected record accuracy	0.7732	0.8117	0.7855 to 0.8367
Expected female precision	0.7604	0.8051	0.7715 to 0.8377
Expected female recall	0.7677	0.8031	0.7444 to 0.8481
Expected female F1	0.7640	0.8041	0.7676 to 0.8342
10-bin calibration error	0.0171	0.0159	0.0132 to 0.0331

The calibration transform improved record-weighted test log loss from 0.3801 to 0.3788 and Brier score from 0.12239 to 0.12233. It did not improve every metric: 10-bin calibration error increased from 0.0138 to 0.0159, and expected record accuracy decreased from 0.8122 to 0.8117. The transform was selected on the separate calibration partition by weighted log loss; test results did not change the model or calibrator.

Supported input

The model accepts one normalized first name containing 3 to 19 lowercase ASCII letters, with no character repeated three or more times consecutively. The runtime applies Unicode NFC normalization, trims surrounding whitespace, and case-folds. It does not delete punctuation, remove diacritics, transliterate, reorder tokens, or truncate names.

Inputs with missing values, multiple tokens, unsupported scripts or characters, or values outside the frozen training domain produce explicit abstentions.

Exact composition lookup

The repository also contains a separate public Parquet lookup artifact. It has one globally aggregated row per released normalized first name and contains female and male source-label record counts. It does not contain state or birth year fields. The sparse third-gender source-label field is validated in the source but is not aggregated, zero-imputed, or published.

The lookup covers 40,581 names and 475,541,789 represented female plus male label records from Naampy v2_1k. Every released name has at least 1,000 included source-label records. An absent lookup result means only that the name was not released in this table. The lookup never falls back to the learned model.

Intended use

Use Naampy for aggregate research only when a name-pattern estimate or released aggregate composition is an appropriate, validated measurement. Report the artifact version, target, abstention rate, and relevant performance slices.

Do not use Naampy to infer or assert an individual's gender. Do not use it for employment, credit, housing, health care, policing, immigration, voting, advertising, access to services, or any other consequential decision.

Limitations

The source labels come from historical Indian electoral-roll records. They are binary because of the retained modeling target, not because gender identity is binary. Names may be shared across source-label categories. Spelling, romanization, geography, time, coverage, and administrative practices may change both model scores and lookup composition.

The learned model supports only a narrow lowercase ASCII first-name domain. It does not support native-script names, accented Latin characters, punctuation, or full names. Its score is a population-level association within the stated reference data and should not be generalized to a different population without direct validation and recalibration.

Use with Naampy

from naampy import estimate_first_name_pattern, lookup_first_name_composition

estimates = estimate_first_name_pattern(["Priya", "Rahul", "\u0926\u0947\u0935", None])
composition = lookup_first_name_composition(["Priya", "Rahul", "unknown"])

The package downloads every artifact at an immutable Hugging Face commit and verifies the hashes and schemas in its manifests. For controlled or offline deployments, set NAAMPY_MODEL_DIR and NAAMPY_LOOKUP_TABLE_DIR to directories containing complete verified bundles.

Licensing and citation

Naampy source code and learned weights are MIT licensed. The exact lookup is derived from the source dataset and remains CC0 1.0. The source dataset is published at DOI 10.7910/DVN/WZGJBM. Cite the software version, the immutable artifact revision, and the source dataset when reporting results.