doc: fix format (#126)

crowdkit/aggregation/classification/majority_vote.py

@@ -23,12 +23,9 @@ class MajorityVote(BaseClassificationAggregator):
     resulting label will have the largest sum of weights.
 
 
-    {% note info %}
-
-     If two or more labels have the largest number of votes, the resulting
-     label will be the same for all tasks that have the same set of labels with the same number of votes.
-
-     {% endnote %}
+    Note:
+        If two or more labels have the largest number of votes, the resulting
+        label will be the same for all tasks that have the same set of labels with the same number of votes.
 
     Examples:
         Basic Majority Vote:

crowdkit/aggregation/classification/zero_based_skill.py

@@ -20,12 +20,9 @@ class ZeroBasedSkill(BaseClassificationAggregator):
 
     This process is repeated until the labels change or exceed the number of iterations.
 
-    {% note info %}
-
-    It is necessary that all workers in the dataset that is sent to `predict` exist in responses to
-    the dataset that was sent to `fit`.
-
-    {% endnote %}
+    Note:
+        It is necessary that all workers in the dataset that is sent to `predict` exist in responses to
+        the dataset that was sent to `fit`.
 
     Examples:
         >>> from crowdkit.aggregation import ZeroBasedSkill

crowdkit/aggregation/multilabel/binary_relevance.py

@@ -19,13 +19,11 @@ class BinaryRelevance(BaseClassificationAggregator):
     each label is treated as a class in binary classification problem and aggregated separately using
     aggregation algorithms for classification, e.g. Majority Vote or Dawid Skene.
 
-    {% note info %}
+    Note:
+        If this method is used for single-label classification, the output of the BinaryRelevance method may differ
+        from the output of the basic aggregator used for its intended purpose, since each class generates a binary
+        classification task, and therefore it is considered separately. For example, some objects may not have labels.
 
-    If this method is used for single-label classification, the output of the BinaryRelevance method may differ
-    from the output of the basic aggregator used for its intended purpose, since each class generates a binary
-    classification task, and therefore it is considered separately. For example, some objects may not have labels.
-
-    {% endnote %}
 
     Examples:
         >>> import pandas as pd