Domi31tls
26ac02b0cb
Chantier 1: Intégration BDPM (5737 médicaments officiels) dans medication whitelist Chantier 2: Safe patterns contextuels (dosages mg/mL/cpr, formes pharma, même ligne) Chantier 3: Scores de confiance NER réels (edsnlp 0.20 ner_confidence_score) Chantier 4: GLiNER zero-shot (urchade/gliner_multi_pii-v1) en vote croisé Chantier 5: Scripts export silver annotations + fine-tuning CamemBERT-bio 0 fuite, 0 régression, -18 FP supplémentaires éliminés. Sécurité: GLiNER ne peut rejeter que si confiance NER < 0.70. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
257 lines
8.6 KiB
Python
257 lines
8.6 KiB
Python
#!/usr/bin/env python3
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"""
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Fine-tune CamemBERT-bio pour la désidentification clinique française.
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=====================================================================
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Entraîne almanach/camembert-bio-base sur les annotations silver/gold
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exportées par export_silver_annotations.py.
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Usage:
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python scripts/finetune_camembert_bio.py [--epochs 5] [--batch-size 8] [--lr 2e-5]
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Prérequis: pip install transformers datasets seqeval accelerate
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Export ONNX post-training: python scripts/export_onnx.py
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"""
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import sys
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import argparse
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from pathlib import Path
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from typing import Dict, List
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import numpy as np
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# Vérifier les dépendances
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try:
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from transformers import (
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AutoTokenizer,
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AutoModelForTokenClassification,
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TrainingArguments,
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Trainer,
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DataCollatorForTokenClassification,
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)
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from datasets import Dataset, DatasetDict
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import evaluate
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except ImportError as e:
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print(f"Dépendance manquante: {e}")
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print("Installez: pip install transformers datasets seqeval accelerate")
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sys.exit(1)
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# Labels BIO pour la désidentification
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LABEL_LIST = [
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"O",
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"B-PER", "I-PER",
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"B-TEL", "I-TEL",
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"B-EMAIL", "I-EMAIL",
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"B-NIR", "I-NIR",
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"B-IPP", "I-IPP",
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"B-NDA", "I-NDA",
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"B-RPPS", "I-RPPS",
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"B-DATE_NAISSANCE", "I-DATE_NAISSANCE",
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"B-ADRESSE", "I-ADRESSE",
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"B-ZIP", "I-ZIP",
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"B-VILLE", "I-VILLE",
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"B-HOPITAL", "I-HOPITAL",
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"B-IBAN", "I-IBAN",
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"B-AGE", "I-AGE",
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]
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LABEL2ID = {l: i for i, l in enumerate(LABEL_LIST)}
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ID2LABEL = {i: l for l, i in LABEL2ID.items()}
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MODEL_NAME = "almanach/camembert-bio-base"
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def load_bio_files(data_dir: Path) -> Dict[str, List]:
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"""Charge les fichiers .bio en format HuggingFace datasets."""
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tokens_list: List[List[str]] = []
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labels_list: List[List[int]] = []
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for bio_file in sorted(data_dir.glob("*.bio")):
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text = bio_file.read_text(encoding="utf-8")
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current_tokens: List[str] = []
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current_labels: List[int] = []
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for line in text.splitlines():
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line = line.strip()
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if not line:
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# Fin de phrase
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if current_tokens:
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tokens_list.append(current_tokens)
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labels_list.append(current_labels)
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current_tokens = []
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current_labels = []
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continue
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parts = line.split("\t")
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if len(parts) != 2:
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continue
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token, label = parts
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label_id = LABEL2ID.get(label, LABEL2ID["O"])
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current_tokens.append(token)
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current_labels.append(label_id)
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if current_tokens:
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tokens_list.append(current_tokens)
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labels_list.append(current_labels)
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return {"tokens": tokens_list, "ner_tags": labels_list}
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def tokenize_and_align(examples, tokenizer):
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"""Tokenize et aligne les labels avec les sous-tokens."""
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tokenized = tokenizer(
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examples["tokens"],
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truncation=True,
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is_split_into_words=True,
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max_length=512,
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padding=False,
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)
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all_labels = []
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for i, labels in enumerate(examples["ner_tags"]):
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word_ids = tokenized.word_ids(batch_index=i)
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label_ids = []
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prev_word_id = None
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for word_id in word_ids:
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if word_id is None:
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label_ids.append(-100)
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elif word_id != prev_word_id:
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label_ids.append(labels[word_id])
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else:
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# Sous-token : I- si le premier est B-, sinon même label
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orig = labels[word_id]
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if orig > 0 and LABEL_LIST[orig].startswith("B-"):
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# Convertir B- en I-
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i_label = LABEL_LIST[orig].replace("B-", "I-")
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label_ids.append(LABEL2ID.get(i_label, orig))
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else:
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label_ids.append(orig)
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prev_word_id = word_id
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all_labels.append(label_ids)
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tokenized["labels"] = all_labels
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return tokenized
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def main():
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parser = argparse.ArgumentParser(description="Fine-tune CamemBERT-bio pour désidentification")
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parser.add_argument("--data-dir", type=Path,
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default=Path(__file__).parent.parent / "data" / "silver_annotations",
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help="Répertoire des fichiers .bio")
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parser.add_argument("--output-dir", type=Path,
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default=Path(__file__).parent.parent / "models" / "camembert-bio-deid",
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help="Répertoire de sortie du modèle")
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parser.add_argument("--epochs", type=int, default=5)
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parser.add_argument("--batch-size", type=int, default=8)
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parser.add_argument("--lr", type=float, default=2e-5)
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parser.add_argument("--val-split", type=float, default=0.15, help="Fraction pour validation")
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args = parser.parse_args()
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# Charger les données
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print(f"Chargement des données depuis {args.data_dir}...")
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raw_data = load_bio_files(args.data_dir)
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n_sentences = len(raw_data["tokens"])
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n_entities = sum(1 for labels in raw_data["ner_tags"] for l in labels if l != 0)
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print(f" {n_sentences} phrases, {n_entities} entités annotées")
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if n_sentences < 10:
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print("ERREUR: pas assez de données. Lancez d'abord export_silver_annotations.py")
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sys.exit(1)
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# Split train/val
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dataset = Dataset.from_dict(raw_data)
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split = dataset.train_test_split(test_size=args.val_split, seed=42)
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datasets = DatasetDict({"train": split["train"], "validation": split["test"]})
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print(f" Train: {len(datasets['train'])}, Validation: {len(datasets['validation'])}")
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# Tokenizer + modèle
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print(f"\nChargement du modèle {MODEL_NAME}...")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForTokenClassification.from_pretrained(
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MODEL_NAME,
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num_labels=len(LABEL_LIST),
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id2label=ID2LABEL,
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label2id=LABEL2ID,
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)
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# Tokenization
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tokenized = datasets.map(
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lambda ex: tokenize_and_align(ex, tokenizer),
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batched=True,
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remove_columns=datasets["train"].column_names,
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)
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# Métriques
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seqeval = evaluate.load("seqeval")
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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predictions = np.argmax(logits, axis=-1)
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true_labels = []
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true_preds = []
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for pred_seq, label_seq in zip(predictions, labels):
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t_labels = []
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t_preds = []
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for p, l in zip(pred_seq, label_seq):
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if l != -100:
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t_labels.append(LABEL_LIST[l])
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t_preds.append(LABEL_LIST[p])
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true_labels.append(t_labels)
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true_preds.append(t_preds)
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results = seqeval.compute(predictions=true_preds, references=true_labels)
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return {
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"precision": results["overall_precision"],
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"recall": results["overall_recall"],
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"f1": results["overall_f1"],
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}
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# Training
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args.output_dir.mkdir(parents=True, exist_ok=True)
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training_args = TrainingArguments(
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output_dir=str(args.output_dir),
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num_train_epochs=args.epochs,
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per_device_train_batch_size=args.batch_size,
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per_device_eval_batch_size=args.batch_size * 2,
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learning_rate=args.lr,
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weight_decay=0.01,
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warmup_ratio=0.1,
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eval_strategy="epoch",
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save_strategy="epoch",
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load_best_model_at_end=True,
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metric_for_best_model="f1",
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logging_steps=50,
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fp16=False, # CPU training
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report_to="none",
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save_total_limit=2,
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)
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data_collator = DataCollatorForTokenClassification(tokenizer)
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=tokenized["train"],
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eval_dataset=tokenized["validation"],
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data_collator=data_collator,
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compute_metrics=compute_metrics,
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tokenizer=tokenizer,
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)
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print(f"\nDémarrage du fine-tuning ({args.epochs} epochs, batch={args.batch_size}, lr={args.lr})...")
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trainer.train()
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# Sauvegarder
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trainer.save_model(str(args.output_dir / "best"))
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tokenizer.save_pretrained(str(args.output_dir / "best"))
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print(f"\nModèle sauvegardé: {args.output_dir / 'best'}")
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# Évaluation finale
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results = trainer.evaluate()
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print(f"\nRésultats finaux:")
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print(f" Precision: {results['eval_precision']:.4f}")
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print(f" Recall: {results['eval_recall']:.4f}")
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print(f" F1: {results['eval_f1']:.4f}")
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print(f"\nPour exporter en ONNX:")
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print(f" python -m optimum.exporters.onnx --model {args.output_dir / 'best'} {args.output_dir / 'onnx'}")
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if __name__ == "__main__":
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main()
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