diff --git a/soundbay/data.py b/soundbay/data.py
index 342369d..6d47ba6 100644
--- a/soundbay/data.py
+++ b/soundbay/data.py
@@ -2,7 +2,6 @@
 from itertools import starmap, repeat
 from pathlib import Path
 from typing import Union
-from decimal import Decimal
 
 import numpy as np
 import pandas as pd
@@ -71,9 +70,8 @@ def __init__(self, data_path, metadata_path, augmentations, augmentations_p, pre
         self.preprocessor = self.set_preprocessor(preprocessors)
         assert (0 <= margin_ratio) and (1 >= margin_ratio)
         self.margin_ratio = margin_ratio
-        self.items_per_classes = np.unique(self.metadata['label'], return_counts=True)[1]
-        weights = 1 / self.items_per_classes
-        self.samples_weight = np.array([weights[t] for t in self.metadata['label'] ])
+        self.num_classes = self._get_num_classes()
+        self.samples_weight = self._get_samples_weight()
 
     @staticmethod
     def _update_metadata_by_mode(metadata, mode, split_metadata_by_label):
@@ -108,13 +106,12 @@ def _preprocess_metadata(self, slice_flag=False):
         Output:
             ClassifierDataset object with self.metadata dataframe after applying the condition
         """
+        self._preprocess_target()
+        is_noise = self.metadata['label'].apply(self._is_noise)
 
         # All calls are worthy (because we can later create a bigger slice contain them that is still a call in
         # _get_audio) but only long enough background sections will do.
-        self.metadata = self.metadata[
-            ((self.metadata['call_length'] >= self.seq_length) & (self.metadata['label'] == 0)) |
-            (self.metadata['label'] > 0)
-            ]
+        self.metadata = self.metadata[((self.metadata['call_length'] >= self.seq_length) & is_noise) | (~is_noise)]
 
         # sometimes the bbox's end time exceeds the file's length
         for name, sub_df in self.metadata.groupby('filename'):
@@ -128,6 +125,26 @@ def _preprocess_metadata(self, slice_flag=False):
 
         self.metadata.reset_index(drop=True, inplace=True)
 
+    def _preprocess_target(self):
+        """
+        Preprocesses the label column in the metadata. If the label is a string, it is evaluated and converted to an
+        integer or a list of integers.
+        """
+        if pd.api.types.is_string_dtype(self.metadata['label']):
+            assert self.metadata['label'].str.match(r'^(\[|\()?(\d+)(\s*,\s*\d+)*(\]|\))?$').all(), \
+                "label should be a string that could be evaluated as a list of integers or integers."
+            self.metadata['label'] = self.metadata['label'].apply(eval)
+            if self.metadata['label'].apply(lambda x: isinstance(x, (list, tuple))).all():
+                self.metadata['label'] = self.metadata['label'].apply(np.array, dtype=int)
+
+    @staticmethod
+    def _is_noise(value: [int, np.ndarray]) -> bool:
+        """
+        Checks if the value is a noise, i.e., if it is equal to 0.
+        """
+        assert (isinstance(value, (int, np.integer)) | isinstance(value, np.ndarray)), "value should be either int or np.ndarray"
+        return np.sum(value) == 0
+
     def _grab_fields(self, idx):
         """
         grabs fields from metadata according to idx
@@ -159,10 +176,10 @@ def _slice_sequence(self):
         self.metadata sliced according to buffers
         """
         self.metadata = self.metadata.reset_index(drop=True)
-        count_values_before = self.metadata.value_counts('label', sort=False) # for validating that the following code doesn't lose samples
+        count_values_before = self.metadata.astype({'label': str}).value_counts('label', sort=False) # for validating that the following code doesn't lose samples
         sliced_times = list(starmap(np.arange, zip(self.metadata['begin_time'], self.metadata['end_time'], repeat(self.seq_length))))
         # add the last sequence at the end of this list for calls only (only if it does not exceed the file)
-        sliced_times = list([np.append(s, self.metadata.loc[i, 'end_time']) if self.metadata.loc[i, 'label'] != 0
+        sliced_times = list([np.append(s, self.metadata.loc[i, 'end_time']) if (not self._is_noise(self.metadata.loc[i, 'label']))
                              else s for i, s in enumerate(sliced_times)])
         new_begin_time = list(x[:-1] for x in sliced_times)
         duplicate_size_vector = [len(list_elem) for list_elem in new_begin_time] # vector to duplicate original dataframe
@@ -172,8 +189,10 @@ def _slice_sequence(self):
         self.metadata['begin_time'] = new_begin_time
         self.metadata['end_time'] = new_end_time
         self.metadata['call_length'] = np.shape(self.metadata)[0] * [self.seq_length]
-        if not all(self.metadata.value_counts('label', sort=False) >= count_values_before):
-            print(f'Note: seems like _slice_sequence erases data.\nbefore:{count_values_before}\nafter:{self.metadata.value_counts("label", sort=False)}')
+        count_values_after = self.metadata.astype({'label': str}).value_counts('label', sort=False)
+        if not all(count_values_after >= count_values_before):
+            print(f'Note: seems like _slice_sequence erases data.\nbefore:{count_values_before}\n'
+                  f'after:{count_values_after}')
         return
 
     def _get_audio(self, path_to_file, begin_time, end_time, label, channel=None):
@@ -215,6 +234,34 @@ def set_preprocessor(preprocessors_args):
             preprocessor = torch.nn.Identity()
         return preprocessor
 
+    def _get_num_classes(self) -> int:
+        """
+        Returns the number of classes in the metadata.
+        """
+        if self.metadata['label'].apply(lambda x: isinstance(x, np.ndarray)).all():
+            label_lengths = self.metadata['label'].apply(len)
+            assert label_lengths.nunique() == 1, "All labels should have the same length"
+            return label_lengths.iloc[0]
+        else:
+            return self.metadata['label'].nunique()
+
+    def _get_samples_weight(self) -> np.ndarray:
+        """
+        Returns the weight of each sample in the dataset:
+            - if the label is integer, the weight is the inverse of the class count.
+            - if the label is a list, the weight is the inverse of the minimum class count.
+        """
+        if self.metadata['label'].apply(lambda x: isinstance(x, np.ndarray)).all():
+            noise_counts = self.metadata['label'].apply(self._is_noise).sum()
+            class_counts = np.sum(self.metadata['label'])
+            per_sample_min_class_count = (self.metadata['label'].apply(
+                lambda x: class_counts[x.astype(bool)].min() if not self._is_noise(x) else noise_counts))
+            return (1 / per_sample_min_class_count).values
+        else:
+            weights = 1 / np.unique(self.metadata['label'], return_counts=True)[1]
+            return np.array([weights[t] for t in self.metadata['label']])
+
+
     def __getitem__(self, idx):
         '''
         __getitem__ method loads item according to idx from the metadata
@@ -276,7 +323,7 @@ def _get_audio(self, path_to_file, begin_time, end_time, label, channel=None):
         if self.mode == "train":
             if seg_length >= requested_seq_length:
                 # Only for calls we can safely add sections out of the call and label it as call
-                if (self.margin_ratio != 0) and (label > 0):
+                if (self.margin_ratio != 0) and (not self._is_noise(label)):
                     # self.margin_ratio ranges from 0 to 1 - indicates the relative part from seq_len to exceed call_length
                     margin_len_begin = int(requested_seq_length * self.margin_ratio)
                     margin_len_end = int(requested_seq_length * (1 - self.margin_ratio))
diff --git a/soundbay/train.py b/soundbay/train.py
index de09508..9b0914b 100644
--- a/soundbay/train.py
+++ b/soundbay/train.py
@@ -110,7 +110,7 @@ def modeling(
     model.to(device)
 
     # Assert number of labels in the dataset and the number of labels in the model
-    assert model_args['num_classes'] == len(train_dataset.items_per_classes) == len(val_dataset.items_per_classes), \
+    assert model_args['num_classes'] == train_dataset.num_classes == val_dataset.num_classes, \
     "Num of classes in model and the datasets must be equal, check your configs and your dataset labels!!"
 
     # Add model watch to WANDB