diff --git a/run_evaluation.py b/run_evaluation.py
index d0cec2f..5c6b3db 100644
--- a/run_evaluation.py
+++ b/run_evaluation.py
@@ -17,24 +17,25 @@
 def get_dataset(dataset: str, tokenizer: PreTrainedTokenizerBase) -> (pd.DataFrame, pd.DataFrame, List):
     da = DATASET_NAMES2LOADERS[dataset]()
     # Filter extremely long samples from both train and test samples:
-    _logger.info("filtering test set:")
+    _logger.info(f"Filtering test set for dataset: {dataset}")
     test_df = filter_extremely_long_samples(da.test_df, tokenizer)
-    _logger.info("filtering train set:")
+    _logger.info(f"Filtering train set for dataset: {dataset}")
     train_df = filter_extremely_long_samples(da.train_df, tokenizer)
     return test_df, train_df, da.labels
 
 
 def run_pcw_experiment(dataset: str, model: str, cache_dir: str, subsample_test_set: int, output_dir: str,
                        n_windows: List[int], n_shots_per_window: Optional[int], n_runs: int,
-                       random_seed: int, right_indentation: bool) -> None:
+                       random_seed: int, right_indentation: bool) -> None {
+    _logger.info(f'Starting experiment with dataset: {dataset} and model: {model}')
     pcw_model = load_pcw_wrapper(model, cache_dir, right_indentation, max(n_windows))
 
     test_df, train_df, labels = get_dataset(dataset, pcw_model.tokenizer)
 
-    if n_shots_per_window is None:
-        # default behaviour: we take the maximum number of samples per window
+    if n_shots_per_window is None: 
+        # Default behaviour: we take the maximum number of samples per window 
         n_shots_per_window = get_max_n_shots(train_df, test_df, pcw_model.tokenizer, pcw_model.context_window_size)
-        _logger.info(f"Found max n shot per window = {n_shots_per_window}")
+        _logger.info(f"Found max n shot per window = {n_shots_per_window} for dataset: {dataset}")
 
     n_shots = [i * n_shots_per_window for i in n_windows]
 
@@ -43,6 +44,8 @@ def run_pcw_experiment(dataset: str, model: str, cache_dir: str, subsample_test_
 
     accuracies = em.run_experiment_across_shots(n_shots, n_runs)
     save_results(dataset, n_shots, accuracies, output_dir, model)
+    _logger.info(f'Experiment completed for dataset: {dataset} and model: {model}')
+}
 
 
 if __name__ == '__main__':
@@ -69,3 +72,5 @@ def run_pcw_experiment(dataset: str, model: str, cache_dir: str, subsample_test_
                         action='store_true', default=False)
     args = parser.parse_args()
     run_pcw_experiment(**vars(args))
+}
+