diff --git a/src/languedoc/train.py b/src/languedoc/train.py
--- a/src/languedoc/train.py
+++ b/src/languedoc/train.py
@@ -72,6 +72,7 @@ def cross_validate(sample_sets: list[Sam
 	score = 0
 	max_score = 0
 
+	print("# Crossvalidation:")
 	for s in sample_sets:
 		for (test_text, partial_model) in s.generate_tests(CROSSVALIDATION_SOURCE_COUNT):
 			real_lang = partial_model.language
@@ -85,7 +86,7 @@ def cross_validate(sample_sets: list[Sam
 					if predicted_lang == real_lang:
 						score += 1
 					else:
-						print(real_lang, predicted_lang, t)
+						print(f"{real_lang} misidentified as {predicted_lang}: {t}")
 					max_score += 1
 
 	return score/max_score, score, max_score
@@ -100,6 +101,7 @@ def train(data_dir: str, model_path: str
 	samples = []
 	lang_dirs = sorted([x.path for x in os.scandir(data_dir)])
 
+	print("# Source texts:")
 	for d in lang_dirs:
 		lang = os.path.basename(d)
 		lang_samples = SampleSet(lang)
@@ -109,7 +111,7 @@ def train(data_dir: str, model_path: str
 			with open(file) as f:
 				text = f.read()
 				text = preprocess(text)
-				print(f"{lang}: {file.name} ({len(text)})")
+				print(f"{lang}: {file.name} ({len(text)} chars)")
 
 				lang_samples.add(text)
 
@@ -117,7 +119,9 @@ def train(data_dir: str, model_path: str
 		s = json.dumps([sample_set.create_model().export() for sample_set in samples], ensure_ascii=False, sort_keys=True)
 		f.write(s.encode("utf-8"))
 
-	print(cross_validate(samples))
+	print()
+	(acc, success, count) = cross_validate(samples)
+	print(f"Accuracy: {acc*100:.4f}%, ({success}/{count} tests during crossvalidation)")
 
 
 DATA_DIR = os.path.join(os.path.dirname(__file__), "../../data")