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GoldenMatch ships five OSS reference-data packs that auto-config picks up when your column names signal a known shape. No external downloads, no API keys, no extra install step — the data files live inside the goldenmatch wheel. The packs add two scorers (name_freq_weighted_jw, given_name_aliased_jw) and three transforms (legal_form_strip, address_normalize, naics_normalize). The auto-config controller swaps them in automatically when a column matches the relevant name pattern AND the profiled data shape agrees.

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The five packs

PackSourceCoverageAdds
SurnamesUS Census 2010Top 10,000 family names with frequency rankname_freq_weighted_jw scorer
Given namesPublic-domain alias corpus~140 alias relationships (William↔Bill, Robert↔Bob, Katherine↔Kate/Kathy)given_name_aliased_jw scorer
BusinessUSPTO + curated legal-form list~30 corporate suffixes across English-speaking jurisdictions (Inc, LLC, Ltd, GmbH, S.A.)legal_form_strip transform
AddressesUSPS Publication 28Street-suffix + secondary-unit abbreviations (Avenue→AVE, Apartment→APT)address_normalize transform
IndustriesUS Census 2022 NAICS2,125 codes across all five hierarchy levels (sector → 6-digit US industry)naics_normalize transform
All packs are loaded lazily on first use. Missing-data fallback is built in — if a wheel build skips a data file, the relevant refinement becomes a no-op and the rest of the pipeline runs normally.

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Auto-config integration

The hook goldenmatch.refdata.autoconfig_hooks.refine_matchkey_field(column_name, scorer, transforms, col_type) fires once per matchkey field during auto_configure_df(). It returns a refined (scorer, transforms) tuple — or the input unchanged if no refdata pack applies. Refinement rules (each gated on the relevant pack’s is_available() AND on the profiled col_type):
Column name patternProfiled col_type must beEffect
last_name, surname, lname, family_name, …name / multi_nameScorer becomes name_freq_weighted_jw
first_name, given_name, fname, forename, …name / multi_nameScorer becomes given_name_aliased_jw
company, business, org, firm, employer, legal_name, entity_namename / multi_name / description / stringlegal_form_strip prepended
address, street, addr_line, mailing_address, line_1, …address / stringaddress_normalize prepended
naics, sic, industry_code, business_type, …identifier / numeric / string / descriptionnaics_normalize prepended
The col_type gate (PR #224) is the critical safety net: a column literally named last_name but holding numeric IDs (a mis-mapped warehouse load, for example) keeps its caller-specified scorer instead of being silently swapped to name_freq_weighted_jw, which would IDF-weight pairs of integers as if they were surnames. Transforms are prepended rather than replaced — the existing lowercase/strip chain still runs after the refdata canonicalization, so blocking-key derivation downstream is unchanged. A column that matches multiple patterns (e.g. company_last_name) gets multiple refinements: scorer swap from the last_name rule, transform prepend from the company rule.

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Scorers

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name_freq_weighted_jw — surname IDF-weighted Jaro-Winkler

Modulates plain Jaro-Winkler by the inverse document frequency of each surname in the US Census table. Common surnames (Smith, Johnson, Williams) get down-weighted in the borderline JW zone; rare surnames keep full credit. 
jw = JaroWinkler.similarity(a, b)
if jw >= 0.95 or jw < 0.70:
    return jw                         # confident — no re-weighting
if either side is OOV in the bundled table:
    return jw                         # can't classify frequency
idf = mean(surname_idf(a), surname_idf(b))
weight = 0.6 + 0.4 * idf
return jw * weight
The borderline zone [0.70, 0.95] is where frequency evidence carries real discrimination. Outside the zone, plain JW is trusted directly so exact matches aren’t degraded. The 0.6 floor ensures matches on SmithSmyth still carry signal — they just don’t score as high as matches on HuXu. Vectorized score_matrix(values) for hot-path NxN scoring uses one rapidfuzz.cdist + numpy mean/where rather than an O(N²) Python double-loop. Quality lift: on the synthetic surname-FP fixture (200 TP pairs, 200 FP-candidate common-surname pairs, 600 distractor singletons), name_freq_weighted_jw lifts F1 from 0.667 (plain JW baseline) to 0.915 — recall stays at 1.0, precision goes 0.50 → 0.84.

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given_name_aliased_jw — alias-aware Jaro-Winkler

Same as plain JW, except known alias pairs (William↔Bill, Katherine↔Kate/Kathy, Robert↔Bob) score 1.0 regardless of edit distance. 
if a and b are known aliases of the same canonical name:
    return 1.0
else:
    return JaroWinkler.similarity(a, b)
The scorer never lowers a JW score — it only promotes known aliases. Degrades cleanly to plain JW when the bundled alias table is missing.

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Transforms

Removes corporate legal forms from the trailing position of a business name. Applied before scoring so Acme Inc and Acme LLC collapse to acme and match on the substantive name. 
"Acme Inc"               → "acme"
"Beta Holdings, Ltd."    → "beta holdings"
"Gamma Corp"             → "gamma"
"Delta GmbH"             → "delta"
"Epsilon Pty Ltd"        → "epsilon"
Suffix table covers Inc, LLC, Ltd, Limited, Corp, Corporation, Co, Company, GmbH, AG, S.A., S.A.S., Pty, Pty Ltd, BV, NV, KG, OY, AB, SRL, plus their common abbreviations and punctuation variants. Case-insensitive; preserves casing of the remaining tokens after lowercasing for comparison.

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address_normalize

Canonicalizes street-suffix and unit abbreviations per USPS Publication 28, plus pre-tokenization rewrites for common notation quirks. 
"123 Main Street #5"        → "123 main st apt 5"
"45 Maple Avenue"           → "45 maple ave"
"PO Box 100"                → "po box 100"
"678 Oak Blvd, Suite 200"   → "678 oak blvd ste 200"
Pre-tokenization rewrites handle apartment-hash notation (#5 → apt 5) and PO Box variants (P.O. Box, P O Box) — without these, #5 and Apt 5 would canonicalize to different tokens and fail to match.

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naics_normalize

Canonicalizes US NAICS 2022 industry classifications. Accepts numeric codes, codes with trailing titles, and known industry titles — all map to a single canonical code. 
"111110"                                    → "111110"
"111110 (Soybean Farming)"                  → "111110"
"NAICS 2022 code 511210"                    → "511210"
"Software Publishers"                       → "513210"   (canonical code for the title)
"Information"                               → "51"       (sector code)
"just a random description"                 → "just a random description"  (passthrough)
Numeric input scans every digit-run in the string and walks back through hierarchy prefixes — a vintage-year prefix like 2022 is skipped because no NAICS code resolves at any hierarchy level. Unknown 6-digit codes still normalize to digits-only, so two records sharing the same unknown code still match each other after the transform.

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Plugin enforcement

Both scorers and all three transforms are registered via PluginRegistry on import goldenmatch.refdata. Registration uses runtime isinstance checks against ScorerPlugin / TransformPlugin Protocols, so a duck-typed implementation missing a method fails at registration rather than deep inside a scoring loop. NameFreqWeightedJW additionally satisfies the VectorizedScorerPlugin Protocol — core/scorer._fuzzy_score_matrix detects the vectorized method via getattr and uses it for NxN block scoring instead of falling back to a Python double-loop.

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Disabling

Refdata refinements are not configurable via YAML in v1 — they fire whenever the relevant column name pattern matches AND the profiled col_type agrees. To pin a different scorer or transform explicitly, set it on the matchkey field — refdata only refines auto-generated configs, never user-specified ones. 
# Explicit scorer wins; refdata won't override
matchkeys:
  - name: fuzzy_name
    type: weighted
    threshold: 0.85
    fields:
      - field: last_name
        scorer: jaro_winkler  # stays jaro_winkler, no refdata swap
        weight: 0.5
To verify what auto-config produced, dump the committed config: 
import goldenmatch as gm
config = gm.auto_configure_df(df)
print(config.model_dump_json(indent=2))

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Performance & extension points

  • Each pack lazy-loads on first use. Module-level state is a @dataclass(frozen=True) with explicit fields, swapped atomically under a lock on reload — readers never see half-built state mid-rebuild.
  • All five packs are pure-Python lookups; no native bindings, no network calls. Adding ~5-50ms of one-time load on first refdata-touching column, ~0ms steady-state.
  • Extension hooks for v2:
    • libpostal binding under reference-address-postal — currently the address pack is rule-based; libpostal would handle international addresses.
    • OpenCorporates company variants — full registry-name aliasing, not just legal-form suffix stripping.
    • Per-scorer threshold tuning in Learning Memory — currently refdata scorers use the same 0.85 default as their plain counterparts.

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See also