genderize cora + ultra: open weights (CC BY-NC 4.0), inference code (MIT), model card

COMMERCIAL_USE.md

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+# Licensing of the genderize weights
+
+**Weights** (`genderize_cora.pt`, `genderize_ultra.pt`, their `.config.json`, `.calibration.json` and `maps.json`)
+are released under **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** — see `LICENSE`.
+
+In plain words:
+
+- You may **download, run, study, modify and redistribute** the weights and derivatives **for non-commercial
+  purposes**, provided you give attribution (*dbtool.it, genderize cora/ultra, 2026*) and indicate changes.
+- Research, teaching, personal projects, non-profit and public-sector use are non-commercial uses.
+- **Commercial use is not granted by this licence.** Commercial use means, without limitation: offering the
+  model or a derivative as a paid or ad-supported service or API, embedding it in a product that is sold,
+  or using it to deliver paid services to third parties.
+- **The only commercial channel is dbtool.it**: the hosted API at https://api.dbtool.it and the `dbtool Local`
+  on-premise licence, both offered by the rights holder. A separate written commercial licence can be
+  requested from the rights holder, Domenico Gigante (d.gigante@tech-time.it).
+
+**Inference code** (`genderize_infer.py`) is released under the **MIT License** (see `LICENSE-CODE`) so that it
+can be freely reused; it carries no model knowledge.
+
+No training data, training scripts or dictionary tables are part of this release.

LICENSE

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LICENSE-CODE

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+MIT License
+
+Copyright (c) 2026 Domenico Gigante (dbtool.it)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+language:
+- multilingual
+license: cc-by-nc-4.0
+library_name: pytorch
+tags:
+- text-classification
+- gender-detection
+- nationality
+- name-analysis
+- byte-level
+- cpu
+pipeline_tag: text-classification
+---
+
+# genderize — cora / ultra
+
+Two models that take a personal name and return a **gender** (M/F) and a
+**country** (226 ISO-3166 alpha-2 codes), with probabilities. Byte-level,
+CPU-only, no tokenizer and no vocabulary file: you feed them a string.
+
+## Package contents
+
+| File | What it is |
+|---|---|
+| `genderize_cora.pt` | weights, cora (3.1 MB) — **CC BY-NC 4.0** |
+| `genderize_ultra.pt` | weights, ultra (12.9 MB) — **CC BY-NC 4.0** |
+| `genderize_cora.config.json` | `{"ch": 160}` — CC BY-NC 4.0 |
+| `genderize_ultra.config.json` | `{"ch": 384}` — CC BY-NC 4.0 |
+| `genderize_cora.calibration.json` | per-head temperature — CC BY-NC 4.0 |
+| `genderize_ultra.calibration.json` | per-head temperature (the file additionally records a calibration-error metric) — CC BY-NC 4.0 |
+| `maps.json` | class ids: gender `M/F`, 226 countries — CC BY-NC 4.0 |
+| `genderize_infer.py` | standalone inference code (needs `torch` and `numpy`) — **MIT** |
+| `LICENSE` | full text of CC BY-NC 4.0 (weights) |
+| `LICENSE-CODE` | full text of the MIT License (inference code) |
+| `COMMERCIAL_USE.md` | what counts as non-commercial use, and how to obtain a commercial licence |
+
+## What the models do
+
+| Variant | Gender | Country | Size |
+|---|---|---|---|
+| `genderize-cora` | 2 classes (M/F) | 226 classes | ~0.77M parameters, ch=160 |
+| `genderize-ultra` | 2 classes (M/F) | 226 classes | ~3.21M parameters, ch=384 |
+
+`ultra` is the larger, more accurate variant; `cora` is the lighter one. Both run
+on CPU: on an Intel i3-6100T (2 cores, 2 threads used) the network alone processes
+about **850 names/s** (`cora`) and **160 names/s** (`ultra`) in batches of 100.
+
+## How it works
+
+**1. Normalisation.** The input name is normalised before anything else:
+Unicode **NFC**, **lowercase**, **whitespace collapsed** (internal runs and
+leading/trailing spaces become a single space). The normalised form is what the
+weights saw, so this step must not be changed or skipped:
+
+    "  MARÍA   GARCÍA  "  ->  "maría garcía"
+
+**2. Encoding.** The normalised text is encoded as **UTF-8 and truncated to 48
+bytes** (not 48 characters: an accented or non-Latin character takes more than
+one byte, so the effective character budget is smaller). Each byte becomes an
+integer in 0–255; byte value 0 is the padding value. A name shorter than 48 bytes
+is padded with zeros.
+
+**3. Network.** Byte-level, dual-head 1-D convolutional classifier:
+
+- one embedding table, 256 entries (one per byte value) of width 64, padding
+  index 0;
+- a 1×1 convolution projecting 64 → `ch` channels;
+- four residual 1-D convolution blocks, kernel sizes **3, 5, 7, 3**, each with
+  convolution + BatchNorm + GELU around a residual connection, padding to keep
+  the length;
+- the masked positions are pooled twice — mean-pooling and max-pooling — and the
+  two vectors are concatenated;
+- a shared layer of 512 units (GELU, plus a dropout layer that is inactive at
+  inference);
+- two linear heads read out from it: **gender**, 2 classes, and **country**,
+  226 classes.
+
+`cora` uses `ch = 160`, `ultra` `ch = 384`; those are the only differences
+between the two configs.
+
+**4. Probabilities.** Because it is a *convolutional* stack, the model sees all
+byte positions at once: character order matters through the convolution kernels,
+not through a recurrent state. Logits are divided by a **per-head temperature**
+before softmax — `temp_gender` and `temp_country` from the calibration file:
+
+| Variant | temp_gender | temp_country |
+|---|---|---|
+| cora | 1.0096479654312134 | 1.0 |
+| ultra | 1.0460342168807983 | 0.939997673034668 |
+
+**5. Decision rule.** Gender is reported as `male` when P(M) ≥ 0.5, otherwise
+`female`; the reported probability is the probability of the reported class.
+Countries are returned as the **top-k** codes (default 5, `--top`) sorted by
+probability.
+
+**6. Output.** One record per input name:
+
+    {"name": "...", "gender": "male|female", "probability": 0.0-1.0,
+     "countries": [{"code": "IT", "probability": 0.0-1.0}, ...], "model": "cora|ultra"}
+
+## Loading and using the weights
+
+All files sit flat in this repository. Download them (for example with
+`huggingface_hub.snapshot_download("textpie/genderize")`) and run the reference
+driver from that directory:
+
+    pip install torch numpy
+    python genderize_infer.py --variant ultra --top 3 "Andrea Rossi"
+
+In Python:
+
+```python
+from genderize_infer import Genderize
+
+model = Genderize("ultra")              # or Genderize("cora"); pass models_dir=... if the files live elsewhere
+model.predict(["Andrea Rossi"], top=3)
+```
+
+Output for the five names published with the examples (`--variant ultra`),
+reproduced locally against these exact weight files:
+
+| Input | Gender | P | Country top-3 |
+|---|---|---|---|
+| Andrea Rossi | male | 0.849 | IT 0.84, FR 0.05, US 0.02 |
+| Yuki Tanaka | female | 0.589 | JP 0.98, US 0.01, ID 0.00 |
+| María García | female | 0.995 | ES 0.42, MX 0.13, AR 0.09 |
+| Chen Wei | male | 0.692 | CN 0.56, TW 0.13, SG 0.09 |
+| Fatima Al Sayed | female | 0.996 | AE 0.35, OM 0.19, SA 0.14 |
+
+## Benchmarks
+
+Source: **dbtool.it/benchmark**. These are **not** new measurements taken for
+this card and no re-measurement was performed here.
+
+- **Bench**: 25,000 names never seen in training, scored on the **network alone**.
+- The dictionary layer used by the hosted API does not contribute on unseen
+  names, so these figures describe the released weights.
+
+| Metric | cora | ultra |
+|---|---|---|
+| Gender accuracy | 97.9 % | 98.2 % |
+| Country top-1 accuracy | 82.6 % | 83.7 % |
+
+Per-country figures for the full hosted system are on https://dbtool.it/academic; an
+independent open bench on public WGND 2.0 names (network alone, losses included) is
+on https://dbtool.it/benchmark.
+
+## What this release does NOT include
+
+- **The dictionary layer of the API.** The hosted API combines the network with
+  a proprietary frequency layer (exact-name and per-country M/F frequencies)
+  derived from production data. That layer is **not part of this release**:
+  you get the network alone. Consequence: **the public API can answer
+  differently from the weights you download**, especially on very frequent
+  names. Documented example: `Yuki Tanaka` scores *female 0.59* with the network
+  alone (the value reproduced above), while the API with its dictionary layer
+  answers *male 0.58*.
+- **Everything about how the models were trained**: no recipe, no epochs, no
+  optimiser, no data split, no training hyperparameters, no data sources and no
+  per-country counts. This release ships a usable model, not the process behind
+  it, and no training example.
+
+## Training data — nature only
+
+The models were trained on **name–gender–country pairs covering 226 countries,
+tens of millions of examples**. That is the whole description this release
+provides: the corpus is **not redistributed**, and its sources, composition and
+per-country sizes are not part of the package.
+
+## Limitations
+
+- **East Asian names**: accuracy drops to roughly **82–88 %**; Chinese, Korean
+  and Japanese names are frequently confused with one another.
+- **Names ambiguous across countries**: many names are plausible in several
+  countries, so a top-1 of ~83 % means roughly one name in six gets the wrong
+  country. `Andrea` is the classic case: male in Italy, female elsewhere. Treat
+  the country score as a prior, and never as a single-country verdict.
+- **48-byte truncation**: only the first 48 UTF-8 bytes reach the model. Long
+  names and long compounds are silently cut, and multi-byte characters consume
+  more of the budget than plain ASCII.
+- **Empty or near-empty input**: an empty or fully-trimmed name is encoded as 48
+  padding bytes and still returns a prediction. Validate your input.
+- **Binary gender**: the gender head has two classes; names that do not fit them
+  are forced into the closer one.
+- **Transliteration**: results depend on how the name was romanised upstream;
+  different romanisations of the same name can disagree.
+- **Dictionary gap**: the released weights are the network alone, so for very
+  frequent names they can differ from the hosted API (see above).
+
+## Licence
+
+Two licences, one per artefact:
+
+- **Weights** — `genderize_cora.pt`, `genderize_ultra.pt`, their `.config.json`
+  and `.calibration.json`, and `maps.json` — are released under **Creative
+  Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**, full
+  text in `LICENSE`. You may download, run, study, modify and redistribute them
+  and their derivatives **for non-commercial purposes**, with attribution
+  (*dbtool.it, genderize cora/ultra, 2026*) and an indication of changes.
+- **Inference code** — `genderize_infer.py` — is released under the **MIT License**,
+  full text in `LICENSE-CODE`. It can be reused freely; it carries no model
+  knowledge.
+
+**Commercial use of the weights is not granted by this licence.** The commercial
+channel is **dbtool.it** (the hosted API and the on-premise licence), or a
+separate written licence from the rights holder. What counts as commercial use,
+and how to request a licence, is set out in **`COMMERCIAL_USE.md`**.
+
+The training data is **not** part of this release, and neither is the dictionary
+layer of the hosted API (see above).
+
+## Contact
+
+dbtool — https://dbtool.it — open-weights page: https://dbtool.it/open-models.html
+Domenico Gigante, d.gigante@tech-time.it

genderize_cora.calibration.json

@@ -0,0 +1 @@
+{"temp_gender": 1.0096479654312134, "temp_country": 1.0}

genderize_cora.config.json

@@ -0,0 +1 @@
+{"ch": 160}

genderize_cora.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0f466b2221f71910b4c83f18a6d207adf837d4cdac2d37da20a5bfdbf068c30
+size 3101302

genderize_infer.py

@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+"""genderize (cora / ultra) — standalone inference for the released weights.
+
+What the model is
+-----------------
+A byte-level dual-head convolutional classifier. A personal name is normalised
+(Unicode NFC, lowercase, whitespace collapsed), UTF-8 encoded and truncated to
+48 bytes. Each byte is embedded (256 x 64), projected to `ch` channels and passed
+through four residual 1-D convolution blocks (kernels 3, 5, 7, 3; BatchNorm +
+GELU). Masked mean-pooling and max-pooling over the byte positions are
+concatenated and fed to a shared 512-unit layer, from which two linear heads
+read out: gender (2 classes, M/F) and country (226 ISO-3166 alpha-2 codes).
+`cora` uses ch=160 (~0.77M parameters), `ultra` uses ch=384 (~3.2M).
+Logits are divided by a per-head temperature (calibration.json) before softmax.
+
+Files expected next to this script (or pass --models-dir):
+  genderize_<variant>.pt                state_dict (PyTorch)
+  genderize_<variant>.config.json       {"ch": 160 | 384}
+  genderize_<variant>.calibration.json  {"temp_gender": t, "temp_country": t}
+  maps.json                             {"gender": {"M":0,"F":1}, "country": {"AD":0, ...}}
+
+Usage
+-----
+  python genderize_infer.py --variant ultra "Andrea Rossi" "Yuki Tanaka" "María García"
+  python genderize_infer.py --variant cora --top 3 --json "Chen Wei"
+
+Requires only torch and numpy. CPU is enough.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+import unicodedata
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+MAXLEN = 48
+
+
+class _GBlock(nn.Module):
+    def __init__(self, ch: int, k: int):
+        super().__init__()
+        self.conv = nn.Conv1d(ch, ch, k, padding=k // 2)
+        self.norm = nn.BatchNorm1d(ch)
+
+    def forward(self, x):
+        return x + F.gelu(self.norm(self.conv(x)))
+
+
+class NameModel(nn.Module):
+    """Byte-level dual-head classifier: gender (2) + country (n_countries)."""
+
+    def __init__(self, n_countries: int, emb: int = 64, ch: int = 160):
+        super().__init__()
+        self.emb = nn.Embedding(256, emb, padding_idx=0)
+        self.proj = nn.Conv1d(emb, ch, 1)
+        self.blocks = nn.Sequential(_GBlock(ch, 3), _GBlock(ch, 5), _GBlock(ch, 7), _GBlock(ch, 3))
+        self.shared = nn.Sequential(nn.Linear(ch * 2, 512), nn.GELU(), nn.Dropout(0.15))
+        self.head_g = nn.Linear(512, 2)
+        self.head_c = nn.Linear(512, n_countries)
+
+    def forward(self, x):
+        mask = (x != 0).float().unsqueeze(1)
+        h = self.proj(self.emb(x.long()).transpose(1, 2))
+        h = self.blocks(h) * mask
+        mean = h.sum(-1) / mask.sum(-1).clamp(min=1)
+        mx = h.masked_fill(mask == 0, -1e9).max(-1).values
+        z = self.shared(torch.cat([mean, mx], -1))
+        return self.head_g(z), self.head_c(z)
+
+
+def normalise(name: str) -> str:
+    """NFC + lowercase + collapse whitespace. Must match what the weights saw."""
+    return " ".join(unicodedata.normalize("NFC", (name or "").lower()).split())
+
+
+def encode(texts: list[str], maxlen: int = MAXLEN) -> torch.Tensor:
+    X = np.zeros((len(texts), maxlen), dtype=np.uint8)
+    for i, t in enumerate(texts):
+        b = t.encode("utf-8")[:maxlen]
+        X[i, : len(b)] = np.frombuffer(b, dtype=np.uint8)
+    return torch.from_numpy(X)
+
+
+class Genderize:
+    def __init__(self, variant: str = "ultra", models_dir: str | Path | None = None):
+        md = Path(models_dir) if models_dir else Path(__file__).resolve().parent
+        maps = json.loads((md / "maps.json").read_text())
+        self.inv_country = {v: k for k, v in maps["country"].items()}
+        self.idx_m = maps["gender"].get("M", 0)
+        cfg = json.loads((md / f"genderize_{variant}.config.json").read_text())
+        cal_path = md / f"genderize_{variant}.calibration.json"
+        self.cal = json.loads(cal_path.read_text()) if cal_path.exists() else {}
+        self.model = NameModel(len(maps["country"]), ch=int(cfg.get("ch", 160)))
+        self.model.load_state_dict(torch.load(md / f"genderize_{variant}.pt", map_location="cpu"))
+        self.model.eval()
+        self.variant = variant
+
+    @torch.no_grad()
+    def predict(self, names: list[str], top: int = 5) -> list[dict]:
+        texts = [normalise(n) for n in names]
+        lg, lc = self.model(encode(texts))
+        pg = F.softmax(lg / self.cal.get("temp_gender", 1.0), dim=-1)
+        pc = F.softmax(lc / self.cal.get("temp_country", 1.0), dim=-1)
+        vals, idx = pc.topk(min(top, pc.shape[-1]), dim=-1)
+        out = []
+        for i, name in enumerate(names):
+            pm = float(pg[i, self.idx_m])
+            gender = "male" if pm >= 0.5 else "female"
+            out.append({
+                "name": name,
+                "gender": gender,
+                "probability": round(pm if gender == "male" else 1.0 - pm, 4),
+                "countries": [{"code": self.inv_country.get(int(j), "??"), "probability": round(float(v), 4)}
+                              for v, j in zip(vals[i].tolist(), idx[i].tolist())],
+                "model": self.variant,
+            })
+        return out
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="genderize cora/ultra — gender + nationality from a name")
+    ap.add_argument("names", nargs="+")
+    ap.add_argument("--variant", choices=["cora", "ultra"], default="ultra")
+    ap.add_argument("--models-dir", default=None)
+    ap.add_argument("--top", type=int, default=5)
+    ap.add_argument("--json", action="store_true", help="print JSON instead of a table")
+    a = ap.parse_args()
+    g = Genderize(a.variant, a.models_dir)
+    rows = g.predict(a.names, top=a.top)
+    if a.json:
+        print(json.dumps(rows, ensure_ascii=False, indent=1))
+    else:
+        for r in rows:
+            cs = ", ".join(f"{c['code']} {c['probability']:.2f}" for c in r["countries"])
+            print(f"{r['name']:<28} {r['gender']:<7} {r['probability']:.3f}   {cs}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

genderize_ultra.calibration.json

@@ -0,0 +1 @@
+{"temp_gender": 1.0460342168807983, "temp_country": 0.939997673034668, "ece_gender_pre": 0.0012399105666638377, "ece_gender_post": 0.0007778512481309008}

genderize_ultra.config.json

@@ -0,0 +1 @@
+{"ch": 384}

genderize_ultra.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0b61d9a547aa1b11acad39af573ad63897578e35aa609c26b3de33156303b83
+size 12868705

maps.json

@@ -0,0 +1 @@
+{"gender": {"M": 0, "F": 1}, "country": {"AD": 0, "AE": 1, "AF": 2, "AG": 3, "AI": 4, "AL": 5, "AM": 6, "AO": 7, "AR": 8, "AT": 9, "AU": 10, "AX": 11, "AZ": 12, "BA": 13, "BB": 14, "BD": 15, "BE": 16, "BF": 17, "BG": 18, "BH": 19, "BI": 20, "BJ": 21, "BM": 22, "BN": 23, "BO": 24, "BR": 25, "BS": 26, "BT": 27, "BW": 28, "BY": 29, "BZ": 30, "CA": 31, "CC": 32, "CD": 33, "CF": 34, "CG": 35, "CH": 36, "CI": 37, "CK": 38, "CL": 39, "CM": 40, "CN": 41, "CO": 42, "CR": 43, "CU": 44, "CV": 45, "CY": 46, "CZ": 47, "DE": 48, "DJ": 49, "DK": 50, "DM": 51, "DO": 52, "DZ": 53, "EC": 54, "EE": 55, "EG": 56, "EH": 57, "ER": 58, "ES": 59, "ET": 60, "FI": 61, "FJ": 62, "FK": 63, "FM": 64, "FR": 65, "GA": 66, "GB": 67, "GD": 68, "GE": 69, "GG": 70, "GH": 71, "GI": 72, "GM": 73, "GN": 74, "GP": 75, "GQ": 76, "GR": 77, "GT": 78, "GW": 79, "GY": 80, "HK": 81, "HN": 82, "HR": 83, "HT": 84, "HU": 85, "ID": 86, "IE": 87, "IL": 88, "IM": 89, "IN": 90, "IO": 91, "IQ": 92, "IR": 93, "IS": 94, "IT": 95, "JE": 96, "JM": 97, "JO": 98, "JP": 99, "KE": 100, "KG": 101, "KH": 102, "KI": 103, "KM": 104, "KN": 105, "KP": 106, "KR": 107, "KW": 108, "KY": 109, "KZ": 110, "LA": 111, "LB": 112, "LC": 113, "LI": 114, "LK": 115, "LR": 116, "LS": 117, "LT": 118, "LU": 119, "LV": 120, "LY": 121, "MA": 122, "MC": 123, "MD": 124, "ME": 125, "MF": 126, "MG": 127, "MH": 128, "MK": 129, "ML": 130, "MM": 131, "MN": 132, "MO": 133, "MQ": 134, "MR": 135, "MS": 136, "MT": 137, "MU": 138, "MV": 139, "MW": 140, "MX": 141, "MY": 142, "MZ": 143, "NA": 144, "NC": 145, "NE": 146, "NF": 147, "NG": 148, "NI": 149, "NL": 150, "NO": 151, "NP": 152, "NR": 153, "NU": 154, "NZ": 155, "OM": 156, "PA": 157, "PE": 158, "PG": 159, "PH": 160, "PK": 161, "PL": 162, "PN": 163, "PR": 164, "PS": 165, "PT": 166, "PW": 167, "PY": 168, "QA": 169, "RO": 170, "RS": 171, "RU": 172, "RW": 173, "SA": 174, "SB": 175, "SC": 176, "SD": 177, "SE": 178, "SG": 179, "SI": 180, "SK": 181, "SL": 182, "SM": 183, "SN": 184, "SO": 185, "SR": 186, "SS": 187, "ST": 188, "SV": 189, "SX": 190, "SY": 191, "SZ": 192, "TC": 193, "TD": 194, "TG": 195, "TH": 196, "TJ": 197, "TK": 198, "TL": 199, "TM": 200, "TN": 201, "TO": 202, "TR": 203, "TT": 204, "TV": 205, "TW": 206, "TZ": 207, "UA": 208, "UG": 209, "US": 210, "UY": 211, "UZ": 212, "VA": 213, "VC": 214, "VE": 215, "VG": 216, "VN": 217, "VU": 218, "WF": 219, "WS": 220, "XK": 221, "YE": 222, "ZA": 223, "ZM": 224, "ZW": 225}}