diff --git a/setup.py b/setup.py
index f83d744..fe1e79d 100644
--- a/setup.py
+++ b/setup.py
@@ -31,7 +31,7 @@ def parse_requirements(filename):
     name=PACKAGE_NAME,
     packages=proj_packages,
     package_dir={PACKAGE_NAME: SOURCE_DIRECTORY},
-    version="0.0.30",
+    version="0.0.31",
     author="Fabian Groeger",
     author_email="fabian.groeger@unibas.ch",
     description="A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors.",
diff --git a/src/cleaner/selfclean_cleaner.py b/src/cleaner/selfclean_cleaner.py
index 828b707..071e7e7 100644
--- a/src/cleaner/selfclean_cleaner.py
+++ b/src/cleaner/selfclean_cleaner.py
@@ -55,7 +55,8 @@ def __init__(
         log_level: str = "INFO",
         **kwargs,
     ):
-        set_log_level(min_log_level=log_level)
+        self.log_level = log_level
+        set_log_level(min_log_level=self.log_level)
         fix_random_seeds(seed=random_seed)
 
         self.memmap = memmap
@@ -119,12 +120,17 @@ def fit(
 
         # create the distance matrix in chunks
         n_chunks = math.ceil(self.N / self.chunk_size)
-        for i in tqdm(
-            range(n_chunks),
-            desc="Creating distance matrix",
-            total=n_chunks,
-            position=0,
-            leave=True,
+        iterator = range(n_chunks)
+        for i in (
+            tqdm(
+                iterator,
+                desc="Creating distance matrix",
+                total=n_chunks,
+                position=0,
+                leave=True,
+            )
+            if self.log_level == "DEBUG"
+            else iterator
         ):
             chunk_slice = slice(i * self.chunk_size, (i + 1) * self.chunk_size, 1)
             X_emb = emb_space[chunk_slice]
@@ -164,7 +170,19 @@ def fit(
             )
             triu_indices = np.triu_indices(self.N, k=1)
         # create the upper triangular matrix of the distance matrix
-        for start_idx in range(0, len(triu_indices[0]), self.chunk_size):
+        n_chunks = math.ceil(len(triu_indices[0]) / self.chunk_size)
+        iterator = range(0, len(triu_indices[0]), self.chunk_size)
+        for start_idx in (
+            tqdm(
+                iterator,
+                desc="Creating upper triangular distance matrix",
+                total=n_chunks,
+                position=0,
+                leave=True,
+            )
+            if self.log_level == "DEBUG"
+            else iterator
+        ):
             end_idx = min(start_idx + self.chunk_size, len(triu_indices[0]))
             self.p_distances[start_idx:end_idx] = self.distance_matrix[
                 triu_indices[0][start_idx:end_idx], triu_indices[1][start_idx:end_idx]