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7ba64dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | """Construction du corpus synthetique : templates + PII generees + offsets gold.
Usage :
# jeu d'evaluation (templates du pool EVAL, jamais vus en train)
python -m bench.synth.build --split eval --n 1500 --out data/bench_v1.jsonl
# corpus d'entrainement (hors git, regenerable a l'identique par seed)
python -m bench.synth.build --split train --n 100000 \
--out data/corpus_train_100k.jsonl
Chaque segment est un objet JSONL au format du harness (voir harness/score.py).
~30 % des segments recoivent un bruitage OCR a longueur constante (offsets
inchanges, flag "noise": true), ~8 % sont des negatifs purs sans PII, et les
valeurs longues (IBAN, NIR...) peuvent etre coupees par un saut de ligne
(piege OCR/PDF, longueur conservee).
Limite assumee : synthetique pur. L'eval reelle annotee main (Judilibre,
BODACC, documents fictifs relus) reste indispensable avant toute conclusion —
analyse §4.3 : "jamais du synthetique seul".
"""
import argparse
import json
import random
import re
from pathlib import Path
from bench.pii.generators import Person, PiiFactory
from bench.synth.noise import apply_noise
from bench.synth.templates import NEGATIVES, all_templates
_SLOT_RE = re.compile(r"\{([a-z_]+?)(\d+)(_v)?\}")
# prefixe de slot -> (methode factory, label ; None = distracteur non etiquete)
_SLOT_TYPES = {
"person": "PERSON", "company": "COMPANY", "address": "ADDRESS",
"city": "CITY", "email": "EMAIL", "phone": "PHONE", "date": "DATE",
"date_birth": "DATE_BIRTH", "iban": "IBAN", "nir": "NIR",
"siren": "SIREN", "siret": "SIRET", "tva": "TVA", "card": "CARD",
"plate": "PLATE", "rg": "RG", "cadastre": "CADASTRE", "ip": "IP",
"amount": "AMOUNT", "ref": None,
}
def _maybe_linebreak(surface: str, rng: random.Random) -> str:
"""Piege PDF/OCR : coupe une valeur longue par un saut de ligne (meme
longueur, un espace interieur remplace par \\n)."""
if len(surface) < 14 or " " not in surface[2:-2] or rng.random() > 0.04:
return surface
spaces = [i for i, c in enumerate(surface) if c == " " and 1 < i < len(surface) - 2]
i = rng.choice(spaces)
return surface[:i] + "\n" + surface[i + 1:]
def fill_template(template: str, factory: PiiFactory, rng: random.Random,
ref_label: str | None = None) -> dict:
values: dict[tuple[str, str], object] = {}
text_parts: list[str] = []
entities: list[dict] = []
pos = 0
cursor = 0
for m in _SLOT_RE.finditer(template):
name, idx, variant = m.group(1), m.group(2), m.group(3)
if name not in _SLOT_TYPES:
raise ValueError(f"slot inconnu : {m.group(0)}")
text_parts.append(template[cursor:m.start()])
pos += m.start() - cursor
cursor = m.end()
key = (name, idx)
if key not in values:
values[key] = getattr(factory, name)()
val = values[key]
if isinstance(val, Person):
surface = rng.choice(val.variants()[1:]) if variant else val.full
else:
surface = _maybe_linebreak(str(val), rng)
etype = _SLOT_TYPES[name]
if name == "ref" and ref_label:
etype = ref_label
if etype is not None:
entities.append(
{"start": pos, "end": pos + len(surface), "type": etype, "value": surface}
)
text_parts.append(surface)
pos += len(surface)
text_parts.append(template[cursor:])
return {"text": "".join(text_parts), "entities": entities}
def build_corpus(n: int, seed: int, noise_share: float,
split: str = "all", negative_share: float = 0.08,
values: str = "faker", ref_label: str | None = None) -> list[dict]:
rng = random.Random(seed)
if values == "real":
from bench.pii.real_factory import RealValuesFactory
factory = RealValuesFactory(seed=seed)
else:
factory = PiiFactory(seed=seed)
templates = all_templates(split)
domains = list(templates)
segments = []
for i in range(n):
if rng.random() < negative_share:
domain = "negatif"
seg = fill_template(rng.choice(NEGATIVES), factory, rng, ref_label=ref_label)
else:
domain = domains[i % len(domains)]
seg = fill_template(rng.choice(templates[domain]), factory, rng, ref_label=ref_label)
noised = rng.random() < noise_share
if noised:
seg["text"] = apply_noise(seg["text"], rng)
for e in seg["entities"]:
e["value"] = seg["text"][e["start"]:e["end"]]
seg = {"id": f"{split}-{domain}-{i:06d}", "domain": domain,
"noise": noised, **seg}
segments.append(seg)
return segments
def main():
ap = argparse.ArgumentParser(description="Genere le corpus benchmark synthetique")
ap.add_argument("--n", type=int, default=600)
ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--noise-share", type=float, default=0.3)
ap.add_argument("--negative-share", type=float, default=0.08)
ap.add_argument("--split", choices=["train", "eval", "all"], default="all")
ap.add_argument("--values", choices=["faker", "real"], default="faker",
help="real = pools BODACC (anti-contamination fine-tune)")
ap.add_argument("--label-refs", action="store_true",
help="etiquette les {ref} en REF (convention 2026-07-29)")
ap.add_argument("--out", default="data/bench_v0.jsonl")
args = ap.parse_args()
segments = build_corpus(args.n, args.seed, args.noise_share,
args.split, args.negative_share, args.values,
"REF" if args.label_refs else None)
out = Path(args.out)
out.parent.mkdir(parents=True, exist_ok=True)
with out.open("w", encoding="utf-8") as f:
for seg in segments:
f.write(json.dumps(seg, ensure_ascii=False) + "\n")
n_ent = sum(len(s["entities"]) for s in segments)
n_noise = sum(1 for s in segments if s["noise"])
print(f"{len(segments)} segments ({n_noise} bruites), {n_ent} entites -> {out}")
if __name__ == "__main__":
main()
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