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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | """Lance un detecteur sur un gold JSONL et ecrit les predictions + le score.
Usage :
python -m bench.detect.run data/bench_v0.jsonl --out data/pred_regex.jsonl
Detecteurs disponibles : regex (couche A). Les candidats NER/GLiNER viendront
s'ajouter ici en phase 2 (meme interface : detect(text) -> spans).
Le rapport est decoupe clean / bruite OCR : la degradation du regex sur le
bruit est une mesure attendue du benchmark (analyse §7.4).
"""
import argparse
import json
from bench.detect import regex_layer
from bench.harness.score import format_report, load_jsonl, score
def _ner(text):
from bench.detect import ner_onnx
return ner_onnx.detect(text)
def _clip(span, occupied, text):
"""Decoupe un span autour des zones occupees : on garde les morceaux
libres (jeter le span entier ferait perdre "Jean Dupont" quand le NER
sort "Jean Dupont, 87, chemin Roux" et que le regex prend l'adresse)."""
pieces = []
start = None
for i in range(span["start"], span["end"] + 1):
free = i < span["end"] and i not in occupied
if free and start is None:
start = i
elif not free and start is not None:
while start < i and not text[start].isalnum():
start += 1
end = i
while end > start and not text[end - 1].isalnum():
end -= 1
if end - start >= 2:
pieces.append({"start": start, "end": end,
"type": span["type"], "value": text[start:end]})
start = None
return pieces
def _fusion(text):
"""regex + NER : la couche regex (checksums) est prioritaire sur ses
spans ; le NER complete partout ailleurs, decoupe si chevauchement."""
spans = regex_layer.detect(text)
occupied = set()
for s in spans:
occupied.update(range(s["start"], s["end"]))
for s in _ner(text):
for piece in _clip(s, occupied, text):
occupied.update(range(piece["start"], piece["end"]))
spans.append(piece)
return sorted(spans, key=lambda s: s["start"])
def _full(text):
"""regex + NER + propagation document-entier des personnes."""
from bench.detect.propagation import propagate
spans = _fusion(text)
spans.extend(propagate(text, spans))
from bench.detect.propagation import propagate_exact, propagate_enumerations
from bench.detect.tools_filter import filtrer
spans.extend(propagate_exact(text, spans))
spans.extend(propagate_enumerations(text, spans))
spans.extend(propagate_exact(text, spans))
return filtrer(sorted(spans, key=lambda s: s["start"]))
def _hybrid(text):
"""La reco mesuree : regex -> gazetteer communes -> camembert-ner
generique (PERSON/CITY, meilleur rappel) -> Anonym-IA (le reste) ->
propagation."""
from bench.detect import gazetteer, ner_onnx
from bench.detect.propagation import propagate
spans = regex_layer.detect(text)
occupied = set()
for s in spans:
occupied.update(range(s["start"], s["end"]))
for source in (
gazetteer.detect(text),
[s for s in ner_onnx.detect_generic(text) if s["type"] in ("PERSON", "CITY")],
_ner(text),
):
for s in source:
for piece in _clip(s, occupied, text):
occupied.update(range(piece["start"], piece["end"]))
spans.append(piece)
spans.extend(propagate(text, spans))
from bench.detect.propagation import propagate_exact, propagate_enumerations
from bench.detect.tools_filter import filtrer
spans.extend(propagate_exact(text, spans))
spans.extend(propagate_enumerations(text, spans))
spans.extend(propagate_exact(text, spans))
return filtrer(sorted(spans, key=lambda s: s["start"]))
def _camembert_baseline(text):
from bench.detect.ner_baseline import detect
return detect(text)
def _hybrid2(text):
"""hybrid + NOTRE fine-tune en moteur COMPANY/filet metier, insere entre
camembert-ner (qui garde PERSON/CITY) et Anonym-IA (types exotiques)."""
from bench.detect import gazetteer, ner_onnx
from bench.detect.propagation import propagate
spans = regex_layer.detect(text)
occupied = set()
for s in spans:
occupied.update(range(s["start"], s["end"]))
for source in (
gazetteer.detect(text),
[s for s in ner_onnx.detect_generic(text) if s["type"] in ("PERSON", "CITY")],
ner_onnx.detect_noirci(text),
_ner(text),
):
for s in source:
for piece in _clip(s, occupied, text):
occupied.update(range(piece["start"], piece["end"]))
spans.append(piece)
spans.extend(propagate(text, spans))
from bench.detect.propagation import propagate_exact, propagate_enumerations
from bench.detect.tools_filter import filtrer
spans.extend(propagate_exact(text, spans))
spans.extend(propagate_enumerations(text, spans))
spans.extend(propagate_exact(text, spans))
return filtrer(sorted(spans, key=lambda s: s["start"]))
def _lite(text):
"""Candidat executable : regex -> gazetteer -> UN seul modele (notre
fine-tune v2, corpus v4 mixte metier+WikiNER) -> propagation."""
from bench.detect import gazetteer, ner_onnx
from bench.detect.propagation import propagate
from bench.detect import vocabulaire
spans = regex_layer.detect(text)
occupied = set()
for s in spans:
occupied.update(range(s["start"], s["end"]))
from bench.detect import gazetteer_company
# le vocabulaire local passe avant les modeles : c'est une connaissance
# certaine, elle doit ancrer la propagation plutot que la subir
for source in (vocabulaire.detect(text), gazetteer.detect(text),
ner_onnx.detect_noirci(text),
gazetteer_company.detect(text)):
for s in source:
for piece in _clip(s, occupied, text):
occupied.update(range(piece["start"], piece["end"]))
spans.append(piece)
spans.extend(propagate(text, spans))
from bench.detect.propagation import propagate_exact, propagate_enumerations
from bench.detect.tools_filter import filtrer
spans.extend(propagate_exact(text, spans))
spans.extend(propagate_enumerations(text, spans))
spans.extend(propagate_exact(text, spans))
return filtrer(sorted(spans, key=lambda s: s["start"]))
def _lite2(text):
"""lite + seconde passe en majuscules ciblees (bench/detect/casse.py).
Une inference de plus par document, contre un tiers de fuite COMPANY
en moins. Voir la mesure du 31/07 dans doc/memory/matrice_comparaison.md."""
from bench.detect.casse import seconde_passe
from bench.detect.propagation import propagate_exact
from bench.detect.tools_filter import filtrer
spans = list(_lite(text))
spans.extend(seconde_passe(text, spans, _lite))
spans.extend(propagate_exact(text, spans))
return filtrer(sorted(spans, key=lambda s: s["start"]))
def _hybrid3(text):
"""hybrid2 SANS Anonym-IA : ablation anti sur-masquage (2026-07-29).
regex -> gazetteer -> generique (PERSON/CITY) -> noirci v2 -> propagation."""
from bench.detect import gazetteer, ner_onnx
from bench.detect.propagation import propagate
spans = regex_layer.detect(text)
occupied = set()
for s in spans:
occupied.update(range(s["start"], s["end"]))
for source in (
gazetteer.detect(text),
[s for s in ner_onnx.detect_generic(text) if s["type"] in ("PERSON", "CITY")],
ner_onnx.detect_noirci(text),
):
for s in source:
for piece in _clip(s, occupied, text):
occupied.update(range(piece["start"], piece["end"]))
spans.append(piece)
spans.extend(propagate(text, spans))
from bench.detect.propagation import propagate_exact, propagate_enumerations
from bench.detect.tools_filter import filtrer
spans.extend(propagate_exact(text, spans))
spans.extend(propagate_enumerations(text, spans))
spans.extend(propagate_exact(text, spans))
return filtrer(sorted(spans, key=lambda s: s["start"]))
DETECTORS = {
"regex": regex_layer.detect,
"lite": _lite,
"lite2": _lite2,
"hybrid3": _hybrid3,
"ner": _ner,
"regex+ner": _fusion,
"full": _full,
"hybrid": _hybrid,
"hybrid2": _hybrid2,
"camembert-ner": _camembert_baseline,
}
def main():
ap = argparse.ArgumentParser(description="Detection + scoring sur un gold JSONL")
ap.add_argument("gold")
ap.add_argument("--detector", choices=DETECTORS, default="regex")
ap.add_argument("--out", help="fichier predictions JSONL (optionnel)")
args = ap.parse_args()
detector = DETECTORS[args.detector]
gold = load_jsonl(args.gold)
preds = {
doc_id: {"id": doc_id, "entities": detector(doc["text"])}
for doc_id, doc in gold.items()
}
if args.out:
with open(args.out, "w", encoding="utf-8") as f:
for p in preds.values():
f.write(json.dumps(p, ensure_ascii=False) + "\n")
for label, keep in [
("TOUT", lambda d: True),
("CLEAN", lambda d: not d.get("noise")),
("BRUITE OCR", lambda d: d.get("noise")),
]:
subset = {i: d for i, d in gold.items() if keep(d)}
if not subset:
continue
print(f"\n=== {args.detector} / {label} ({len(subset)} segments) ===")
print(format_report(score(subset, preds)))
if __name__ == "__main__":
main()
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