diff --git a/neurox/data/extraction/transformers_extractor.py b/neurox/data/extraction/transformers_extractor.py
index 4c19272..238c867 100644
--- a/neurox/data/extraction/transformers_extractor.py
+++ b/neurox/data/extraction/transformers_extractor.py
@@ -127,6 +127,7 @@ def extract_sentence_representations(
     dtype="float32",
     include_special_tokens=False,
     tokenization_counts={},
+    verbose=False 
 ):
     """
     Get representations for a single sentence
@@ -245,15 +246,16 @@ def extract_sentence_representations(
             ]
         all_hidden_states = np.array(all_hidden_states, dtype=dtype)
 
-    print('Sentence         : "%s"' % (sentence))
-    print("Original    (%03d): %s" % (len(original_tokens), original_tokens))
-    print(
-        "Tokenized   (%03d): %s"
-        % (
-            len(tokenizer.convert_ids_to_tokens(ids)),
-            tokenizer.convert_ids_to_tokens(ids),
+    if verbose: 
+        print('Sentence         : "%s"' % (sentence))
+        print("Original    (%03d): %s" % (len(original_tokens), original_tokens))
+        print(
+            "Tokenized   (%03d): %s"
+            % (
+                len(tokenizer.convert_ids_to_tokens(ids)),
+                tokenizer.convert_ids_to_tokens(ids),
+            )
         )
-    )
 
     assert all_hidden_states.shape[1] == len(ids)
 
@@ -276,13 +278,14 @@ def extract_sentence_representations(
         special_token_ids = []
 
     assert all_hidden_states.shape[1] == len(filtered_ids)
-    print(
-        "Filtered   (%03d): %s"
-        % (
-            len(tokenizer.convert_ids_to_tokens(filtered_ids)),
-            tokenizer.convert_ids_to_tokens(filtered_ids),
+    if verbose: 
+        print(
+            "Filtered   (%03d): %s"
+            % (
+                len(tokenizer.convert_ids_to_tokens(filtered_ids)),
+                tokenizer.convert_ids_to_tokens(filtered_ids),
+            )
         )
-    )
 
     # Get actual tokens for filtered ids in order to do subword
     #  aggregation
@@ -397,15 +400,17 @@ def extract_sentence_representations(
                 last_special_token_pointer += 1
             counter += 1
 
-    print("Detokenized (%03d): %s" % (len(detokenized), detokenized))
-    print("Counter: %d" % (counter))
+    if verbose: 
+        print("Detokenized (%03d): %s" % (len(detokenized), detokenized))
+        print("Counter: %d" % (counter))
 
     if inputs_truncated:
         print("WARNING: Input truncated because of length, skipping check")
     else:
         assert counter == len(filtered_ids)
         assert len(detokenized) == len(original_tokens) + len(special_token_ids)
-    print("===================================================================")
+    if verbose: 
+        print("===================================================================")
     return final_hidden_states, detokenized
 
 
@@ -422,6 +427,7 @@ def extract_representations(
     filter_layers=None,
     dtype="float32",
     include_special_tokens=False,
+    verbose=False
 ):
     """
     Extract representations for an entire corpus and save them to disk
@@ -502,6 +508,8 @@ def corpus_generator(input_corpus_path):
     print("Extracting representations from model")
     tokenization_counts = {}  # Cache for tokenizer rules
     for sentence_idx, sentence in enumerate(corpus_generator(input_corpus)):
+        if sentence_idx % 100 == 0: 
+            print(f"Reached Sentence {sentence_idx}")
         hidden_states, extracted_words = extract_sentence_representations(
             sentence,
             model,
@@ -512,12 +520,15 @@ def corpus_generator(input_corpus_path):
             dtype=dtype,
             include_special_tokens=include_special_tokens,
             tokenization_counts=tokenization_counts,
+            verbose=verbose
         )
 
-        print("Hidden states: ", hidden_states.shape)
-        print("# Extracted words: ", len(extracted_words))
+        if verbose: 
+            print("Hidden states: ", hidden_states.shape)
+            print("# Extracted words: ", len(extracted_words))
 
         writer.write_activations(sentence_idx, extracted_words, hidden_states)
+    print("[INFO] DONE WITH THE ACTIVATION EXTRACTION...")
 
     writer.close()
 
@@ -558,7 +569,6 @@ def main():
         action="store_true",
         help="Include special tokens like [CLS] and [SEP] in the extracted representations",
     )
-
     ActivationsWriter.add_writer_options(parser)
 
     args = parser.parse_args()