diff --git a/moderation_classifier/train_BERT.py b/moderation_classifier/train_BERT.py
index eca1f859b5d11d1182657e0b014cf9f97889bad4..1fd2ee1596b6d9cb70d0d2815e4a47a65339c5f2 100644
--- a/moderation_classifier/train_BERT.py
+++ b/moderation_classifier/train_BERT.py
@@ -32,6 +32,7 @@ def save_logs(
     :param text_preprocessing: Boolean flag whether preprocessing was used or not
     """
     logs = dict()
+    logs["path_repo"] = path_repo
     logs["path_model"] = path
     logs["input_data"] = input_data
     logs["text_preprocessing"] = text_preprocessing
diff --git a/moderation_classifier/train_MNB.py b/moderation_classifier/train_MNB.py
index 7d818130a767d77dca87e122ed7365e0adbc4763..5b1a5e31545d5d02147ace854474193a9dee6fb8 100644
--- a/moderation_classifier/train_MNB.py
+++ b/moderation_classifier/train_MNB.py
@@ -32,7 +32,7 @@ def create_pipeline():
     )
 
     # define model
-    mnb = MultinomialNB(alpha=0.01)
+    mnb = MultinomialNB(alpha=0.1)
 
     # set pipeline
     pipe = Pipeline([("processor", tp), ("vectorizer", vectorizer), ("mnb", mnb)])
@@ -102,16 +102,16 @@ def main(input_data: Union[str, os.PathLike]):
 
     # Load data and extract only text from tagesanzeiger
     print("Load and preprocess text")
-    remove_duplicates = False
+    remove_duplicates = True
     min_num_words = 3
     tl = TextLoader(input_data)
     df_de = tl.load_text_csv(
         newspaper="tagesanzeiger",
+        lang='de',
         load_subset=False,
         remove_duplicates=remove_duplicates,
         min_num_words=min_num_words,
     )
-    df_de = df_de.sample(50)
 
     # Prepare data for modeling
     text = df_de.text