feat: add NER annotation step and integrate into pipeline

ner.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+import argparse
+import logging
+import sys
+from pathlib import Path
+
+from core.config import setup_config
+from processing.ner.ner_data_builder import NERDataBuilder
+from processing.ner.ner_name_model import NERNameModel
+
+
+def train(config_path=None, env="development"):
+    """Train the NER model."""
+    try:
+        config = setup_config(config_path=config_path, env=env)
+        trainer = NERNameModel(config)
+        builder = NERDataBuilder(config)
+
+        data_path = Path(config.paths.data_dir) / config.data.output_files["ner_data"]
+        if not data_path.exists():
+            builder.build()
+
+        trainer.create_blank_model("fr")
+        data = trainer.load_data(str(data_path))
+
+        split_idx = int(len(data) * 0.8)
+        train_data, eval_data = data[:split_idx], data[split_idx:]
+
+        logging.info(f"Training with {len(train_data)} examples, evaluating on {len(eval_data)}")
+        trainer.train(train_data, epochs=1, batch_size=config.processing.batch_size, dropout_rate=0.3)
+        trainer.evaluate(eval_data)
+
+        model_path = trainer.save()
+        logging.info(f"Model saved to: {model_path}")
+        return 0
+
+    except Exception as e:
+        logging.error(f"NER Training failed: {e}", exc_info=True)
+        return 1
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Train NER model for DRC names")
+    parser.add_argument("--config", type=str, help="Path to configuration file")
+    parser.add_argument("--env", type=str, default="development", help="Environment name")
+    args = parser.parse_args()
+
+    sys.exit(train(config_path=args.config, env=args.env))
+
+
+if __name__ == "__main__":
+    main()