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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 | """Baseline : Jean-Baptiste/camembert-ner (generique PER/ORG/LOC/MISC).
Sert de point de comparaison dans la matrice (§5.3) : que vaut un NER FR
generique la ou Anonym-IA V2 est specialise PII ? Torch CPU via transformers,
pas d'export ONNX : c'est un candidat de matrice, pas un module produit.
"""
from functools import lru_cache
LABEL_MAP = {"PER": "PERSON", "ORG": "COMPANY", "LOC": "CITY", "MISC": "MISC"}
@lru_cache(maxsize=1)
def _pipe():
from transformers import pipeline
return pipeline("ner", model="Jean-Baptiste/camembert-ner",
aggregation_strategy="simple", device=-1)
def detect(text: str) -> list[dict]:
spans = []
for e in _pipe()(text):
etype = LABEL_MAP.get(e["entity_group"], e["entity_group"])
start, end = int(e["start"]), int(e["end"])
# recalage frontieres de mots (meme raison que ner_onnx)
while start > 0 and text[start].isalnum() and text[start - 1].isalnum():
start -= 1
while end < len(text) and text[end - 1].isalnum() and text[end].isalnum():
end += 1
spans.append({"start": start, "end": end, "type": etype,
"value": text[start:end]})
return spans
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