diff --git a/lerobot/configs/policy/hilserl_classifier.yaml b/lerobot/configs/policy/hilserl_classifier.yaml
index 149eeab2..9ab181d5 100644
--- a/lerobot/configs/policy/hilserl_classifier.yaml
+++ b/lerobot/configs/policy/hilserl_classifier.yaml
@@ -3,6 +3,14 @@
 defaults:
   - _self_
 
+hydra:
+  run:
+    # Set `dir` to where you would like to save all of the run outputs. If you run another training session
+    # with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
+    dir: outputs/train_hilserl_classifier/${now:%Y-%m-%d}/${now:%H-%M-%S}_${env.name}_${hydra.job.name}
+  job:
+    name: default
+
 seed: 13
 dataset_repo_id: aractingi/push_cube_square_light_reward_cropped_resized
 # aractingi/push_cube_square_reward_1_cropped_resized
diff --git a/lerobot/scripts/train_hilserl_classifier.py b/lerobot/scripts/train_hilserl_classifier.py
index 6044b038..1cae0183 100644
--- a/lerobot/scripts/train_hilserl_classifier.py
+++ b/lerobot/scripts/train_hilserl_classifier.py
@@ -42,6 +42,7 @@ from lerobot.common.utils.utils import (
     format_big_number,
     get_safe_torch_device,
     init_hydra_config,
+    init_logging,
     set_global_seed,
 )
 from lerobot.scripts.server.buffer import random_shift
@@ -60,9 +61,24 @@ def get_model(cfg, logger):  # noqa I001
 
 
 def create_balanced_sampler(dataset, cfg):
-    # Creates a weighted sampler to handle class imbalance
+    # Get underlying dataset if using Subset
+    original_dataset = (
+        dataset.dataset if isinstance(dataset, torch.utils.data.Subset) else dataset
+    )
 
-    labels = torch.tensor([item[cfg.training.label_key] for item in dataset])
+    # Get indices if using Subset (for slicing)
+    indices = dataset.indices if isinstance(dataset, torch.utils.data.Subset) else None
+
+    # Get labels from Hugging Face dataset
+    if indices is not None:
+        # Get subset of labels using Hugging Face's select()
+        hf_subset = original_dataset.hf_dataset.select(indices)
+        labels = hf_subset[cfg.training.label_key]
+    else:
+        # Get all labels directly
+        labels = original_dataset.hf_dataset[cfg.training.label_key]
+
+    labels = torch.stack(labels)
     _, counts = torch.unique(labels, return_counts=True)
     class_weights = 1.0 / counts.float()
     sample_weights = class_weights[labels]
@@ -298,22 +314,24 @@ def benchmark_inference_time(model, dataset, logger, cfg, device, step):
     return avg, median, std
 
 
-@hydra.main(
-    version_base="1.2",
-    config_path="../configs/policy",
-    config_name="hilserl_classifier",
-)
-def train(cfg: DictConfig) -> None:
+def train(
+    cfg: DictConfig, out_dir: str | None = None, job_name: str | None = None
+) -> None:
+    if out_dir is None:
+        raise NotImplementedError()
+    if job_name is None:
+        raise NotImplementedError()
+
     # Main training pipeline with support for resuming training
+    init_logging()
     logging.info(OmegaConf.to_yaml(cfg))
 
+    logger = Logger(cfg, out_dir, wandb_job_name=job_name)
+
     # Initialize training environment
     device = get_safe_torch_device(cfg.device, log=True)
     set_global_seed(cfg.seed)
 
-    out_dir = hydra.core.hydra_config.HydraConfig.get().run.dir + "frozen_resnet10_2"
-    logger = Logger(cfg, out_dir, cfg.wandb.job_name if cfg.wandb.enable else None)
-
     # Setup dataset and dataloaders
     dataset = LeRobotDataset(
         cfg.dataset_repo_id,
@@ -462,5 +480,32 @@ def train(cfg: DictConfig) -> None:
     logging.info("Training completed")
 
 
+@hydra.main(
+    version_base="1.2",
+    config_name="hilserl_classifier",
+    config_path="../configs/policy",
+)
+def train_cli(cfg: dict):
+    train(
+        cfg,
+        out_dir=hydra.core.hydra_config.HydraConfig.get().run.dir,
+        job_name=hydra.core.hydra_config.HydraConfig.get().job.name,
+    )
+
+
+def train_notebook(
+    out_dir=None,
+    job_name=None,
+    config_name="hilserl_classifier",
+    config_path="../configs/policy",
+):
+    from hydra import compose, initialize
+
+    hydra.core.global_hydra.GlobalHydra.instance().clear()
+    initialize(config_path=config_path)
+    cfg = compose(config_name=config_name)
+    train(cfg, out_dir=out_dir, job_name=job_name)
+
+
 if __name__ == "__main__":
-    train()
+    train_cli()