diff --git a/src/MEDS_tabular_automl/evaluation_callback.py b/src/MEDS_tabular_automl/evaluation_callback.py
index d9236f8..0a394c5 100644
--- a/src/MEDS_tabular_automl/evaluation_callback.py
+++ b/src/MEDS_tabular_automl/evaluation_callback.py
@@ -3,13 +3,10 @@
 import polars as pl
 from hydra.experimental.callback import Callback
 from loguru import logger
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig
 
 
 class EvaluationCallback(Callback):
-    def __init__(self, **kwargs):
-        self.kwargs = kwargs
-
     def on_multirun_end(self, config: DictConfig, **kwargs):
         """Find best model based on log files and logger.info its performance and hyperparameters."""
         log_fp = Path(config.model_logging.model_log_dir)
@@ -27,7 +24,6 @@ def on_multirun_end(self, config: DictConfig, **kwargs):
 
         logger.info(f"The best model can be found at {best_model}")
         self.log_performance(perf[0, :])
-        # self.log_hyperparams(log_fp / best_model / f"{config.model_logging.config_log_stem}.log")
         if hasattr(config, "model_saving.delete_below_top_k") and config.delete_below_top_k >= 0:
             self.delete_below_top_k_models(
                 perf, config.model_saving.delete_below_top_k, config.model_saving.model_dir
@@ -43,15 +39,6 @@ def log_performance(self, perf):
             f"\nPerformance of best model:\nTuning AUC: {tuning_auc}\nTest AUC: {test_auc}",
         )
 
-    def log_hyperparams(self, best_params_fp):
-        """logger.info hyperparameters of the best model with nice formatting."""
-        # check if this file exists
-        if not best_params_fp.is_file():
-            raise FileNotFoundError(f"Best hyperparameters file not found at {best_params_fp}")
-        best_params = OmegaConf.load(best_params_fp)
-        # print using OmegaConf.to_yaml
-        logger.info(f"\nHyperparameters of the best model:\n{OmegaConf.to_yaml(best_params)}")
-
     def delete_below_top_k_models(self, perf, k, model_dir):
         """Save only top k models from the model directory and delete all other files."""
         top_k_models = perf.head(k)["model_fp"].values