src/fmeval/eval_algorithms/classification_accuracy.py [254:264]:
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            valid_labels = (
                self.valid_labels
                if self.valid_labels
                else dataset.unique(column=DatasetColumns.TARGET_OUTPUT.value.name)
            )
            row_count = dataset.count()
            if len(valid_labels) / (row_count + 1) < UNIQUENESS_FACTOR:  # pragma: no cover
                logger.warning(
                    f"The number of classes: {len(valid_labels)} in the dataset is too large "
                    f"for the number of rows in the dataset: {row_count}",
                )
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src/fmeval/eval_algorithms/classification_accuracy_semantic_robustness.py [234:244]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            valid_labels = (
                self.valid_labels
                if self.valid_labels
                else dataset.unique(column=DatasetColumns.TARGET_OUTPUT.value.name)
            )
            row_count = dataset.count()
            if len(valid_labels) / (row_count + 1) < UNIQUENESS_FACTOR:  # pragma: no cover
                logger.warning(
                    f"The number of classes: {len(valid_labels)} in the dataset is too large "
                    f"for the number of rows in the dataset: {row_count}",
                )
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