diff --git a/CHANGELOG.md b/CHANGELOG.md
index e53ec4663bd..a29098d38d1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -23,6 +23,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `classes` to output from `MAP` metric ([#1419](https://github.com/Lightning-AI/metrics/pull/1419))
 
 
+- Add `ClassificationTask` Enum and use in metrics ([#1479](https://github.com/Lightning-AI/metrics/pull/1479))
+
+
 ### Changed
 
 - Changed `update_count` and `update_called` from private to public methods ([#1370](https://github.com/Lightning-AI/metrics/pull/1370))
@@ -31,6 +34,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Raise exception for invalid kwargs in Metric base class ([#1427](https://github.com/Lightning-AI/metrics/pull/1427))
 
 
+- Extend `EnumStr` raising `ValueError` for invalid value ([#1479](https://github.com/Lightning-AI/metrics/pull/1479))
+
+
 ### Deprecated
 
 -
diff --git a/requirements.txt b/requirements.txt
index a121c783161..4b5ec226720 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy>=1.17.2
 torch>=1.8.1
 typing-extensions; python_version < '3.9'
 packaging  # hotfix for utils, can be dropped with lit-utils >=0.5
-lightning-utilities>=0.4.1
+lightning-utilities>=0.5.0
diff --git a/src/torchmetrics/classification/accuracy.py b/src/torchmetrics/classification/accuracy.py
index 798e8e0eacc..f68a05db6db 100644
--- a/src/torchmetrics/classification/accuracy.py
+++ b/src/torchmetrics/classification/accuracy.py
@@ -18,6 +18,7 @@
 
 from torchmetrics.functional.classification.accuracy import _accuracy_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
 
@@ -490,18 +491,16 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAccuracy(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassAccuracy(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAccuracy(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/auroc.py b/src/torchmetrics/classification/auroc.py
index 85768340a23..14bb0b2e953 100644
--- a/src/torchmetrics/classification/auroc.py
+++ b/src/torchmetrics/classification/auroc.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryAUROC(BinaryPrecisionRecallCurve):
@@ -352,15 +353,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAUROC(max_fpr, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassAUROC(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAUROC(num_labels, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/average_precision.py b/src/torchmetrics/classification/average_precision.py
index 979e11e5db7..c50d4708bf4 100644
--- a/src/torchmetrics/classification/average_precision.py
+++ b/src/torchmetrics/classification/average_precision.py
@@ -30,6 +30,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
@@ -356,15 +357,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAveragePrecision(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassAveragePrecision(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAveragePrecision(num_labels, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/calibration_error.py b/src/torchmetrics/classification/calibration_error.py
index 544fd03debc..3518870d7fa 100644
--- a/src/torchmetrics/classification/calibration_error.py
+++ b/src/torchmetrics/classification/calibration_error.py
@@ -29,6 +29,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 class BinaryCalibrationError(Metric):
@@ -267,12 +268,10 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"n_bins": n_bins, "norm": norm, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTaskNoMultilabel.BINARY:
             return BinaryCalibrationError(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTaskNoMultilabel.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCalibrationError(num_classes, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/cohen_kappa.py b/src/torchmetrics/classification/cohen_kappa.py
index f2c9bcc0b1b..524d8ff5d7d 100644
--- a/src/torchmetrics/classification/cohen_kappa.py
+++ b/src/torchmetrics/classification/cohen_kappa.py
@@ -23,6 +23,7 @@
     _multiclass_cohen_kappa_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 class BinaryCohenKappa(BinaryConfusionMatrix):
@@ -221,12 +222,10 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"weights": weights, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTaskNoMultilabel.BINARY:
             return BinaryCohenKappa(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTaskNoMultilabel.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCohenKappa(num_classes, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/confusion_matrix.py b/src/torchmetrics/classification/confusion_matrix.py
index 7018956ed6a..2f9277d76a1 100644
--- a/src/torchmetrics/classification/confusion_matrix.py
+++ b/src/torchmetrics/classification/confusion_matrix.py
@@ -35,6 +35,7 @@
     _multilabel_confusion_matrix_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _PLOT_OUT_TYPE, plot_confusion_matrix
 
@@ -397,15 +398,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"normalize": normalize, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryConfusionMatrix(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassConfusionMatrix(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelConfusionMatrix(num_labels, threshold, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/exact_match.py b/src/torchmetrics/classification/exact_match.py
index 5c25e6e9d09..82dfa3e8747 100644
--- a/src/torchmetrics/classification/exact_match.py
+++ b/src/torchmetrics/classification/exact_match.py
@@ -32,6 +32,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTaskNoBinary
 
 
 class MulticlassExactMatch(Metric):
@@ -288,13 +289,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTaskNoBinary.from_str(task)
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "multiclass":
+        if task == ClassificationTaskNoBinary.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassExactMatch(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTaskNoBinary.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelExactMatch(num_labels, threshold, **kwargs)
-        raise ValueError(f"Expected argument `task` to either be `'multiclass'` or `'multilabel'` but got {task}")
diff --git a/src/torchmetrics/classification/f_beta.py b/src/torchmetrics/classification/f_beta.py
index c3dfa4a5b26..4dd02190e27 100644
--- a/src/torchmetrics/classification/f_beta.py
+++ b/src/torchmetrics/classification/f_beta.py
@@ -24,6 +24,7 @@
     _multilabel_fbeta_score_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryFBetaScore(BinaryStatScores):
@@ -729,13 +730,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryFBetaScore(beta, threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassFBetaScore(beta, num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelFBetaScore(beta, num_labels, threshold, average, **kwargs)
         raise ValueError(
@@ -776,19 +777,17 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryF1Score(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassF1Score(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelF1Score(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/hamming.py b/src/torchmetrics/classification/hamming.py
index 636215ae145..ccdd13400f8 100644
--- a/src/torchmetrics/classification/hamming.py
+++ b/src/torchmetrics/classification/hamming.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.hamming import _hamming_distance_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryHammingDistance(BinaryStatScores):
@@ -344,20 +345,17 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
-
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryHammingDistance(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassHammingDistance(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelHammingDistance(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/hinge.py b/src/torchmetrics/classification/hinge.py
index 1d59b60621e..b68584e7f1b 100644
--- a/src/torchmetrics/classification/hinge.py
+++ b/src/torchmetrics/classification/hinge.py
@@ -29,6 +29,7 @@
     _multiclass_hinge_loss_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 class BinaryHingeLoss(Metric):
@@ -252,12 +253,10 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTaskNoMultilabel.BINARY:
             return BinaryHingeLoss(squared, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTaskNoMultilabel.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassHingeLoss(num_classes, squared, multiclass_mode, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/jaccard.py b/src/torchmetrics/classification/jaccard.py
index 2b7e43f3c9c..ae1c37fd93c 100644
--- a/src/torchmetrics/classification/jaccard.py
+++ b/src/torchmetrics/classification/jaccard.py
@@ -23,6 +23,7 @@
     _multilabel_jaccard_index_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryJaccardIndex(BinaryConfusionMatrix):
@@ -295,15 +296,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryJaccardIndex(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassJaccardIndex(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelJaccardIndex(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/matthews_corrcoef.py b/src/torchmetrics/classification/matthews_corrcoef.py
index 39f4d40a169..cceaa4e4b43 100644
--- a/src/torchmetrics/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/classification/matthews_corrcoef.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification import BinaryConfusionMatrix, MulticlassConfusionMatrix, MultilabelConfusionMatrix
 from torchmetrics.functional.classification.matthews_corrcoef import _matthews_corrcoef_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryMatthewsCorrCoef(BinaryConfusionMatrix):
@@ -237,15 +238,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryMatthewsCorrCoef(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassMatthewsCorrCoef(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelMatthewsCorrCoef(num_labels, threshold, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/precision_recall.py b/src/torchmetrics/classification/precision_recall.py
index 4964e013f4a..300dc3364aa 100644
--- a/src/torchmetrics/classification/precision_recall.py
+++ b/src/torchmetrics/classification/precision_recall.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.precision_recall import _precision_recall_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryPrecision(BinaryStatScores):
@@ -620,18 +621,16 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        task = ClassificationTask.from_str(task)
+        if task == ClassificationTask.BINARY:
             return BinaryPrecision(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassPrecision(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecision(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
 
 
 class Recall:
@@ -672,19 +671,17 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryRecall(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassRecall(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelRecall(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/precision_recall_curve.py b/src/torchmetrics/classification/precision_recall_curve.py
index e26309413d1..dafdea2e6cf 100644
--- a/src/torchmetrics/classification/precision_recall_curve.py
+++ b/src/torchmetrics/classification/precision_recall_curve.py
@@ -37,6 +37,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryPrecisionRecallCurve(Metric):
@@ -466,15 +467,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryPrecisionRecallCurve(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassPrecisionRecallCurve(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecisionRecallCurve(num_labels, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/recall_at_fixed_precision.py b/src/torchmetrics/classification/recall_at_fixed_precision.py
index 4b79171f1b8..188b71ce272 100644
--- a/src/torchmetrics/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/classification/recall_at_fixed_precision.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryRecallAtFixedPrecision(BinaryPrecisionRecallCurve):
@@ -323,18 +324,16 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
-        if task == "binary":
+        task = ClassificationTask.from_str(task)
+        if task == ClassificationTask.BINARY:
             return BinaryRecallAtFixedPrecision(min_precision, thresholds, ignore_index, validate_args, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassRecallAtFixedPrecision(
                 num_classes, min_precision, thresholds, ignore_index, validate_args, **kwargs
             )
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelRecallAtFixedPrecision(
                 num_labels, min_precision, thresholds, ignore_index, validate_args, **kwargs
             )
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/roc.py b/src/torchmetrics/classification/roc.py
index a8358b165a9..aa7d86c1cc0 100644
--- a/src/torchmetrics/classification/roc.py
+++ b/src/torchmetrics/classification/roc.py
@@ -28,6 +28,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryROC(BinaryPrecisionRecallCurve):
@@ -381,15 +382,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryROC(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassROC(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelROC(num_labels, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/specificity.py b/src/torchmetrics/classification/specificity.py
index 3468196812d..0965a99bffc 100644
--- a/src/torchmetrics/classification/specificity.py
+++ b/src/torchmetrics/classification/specificity.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.specificity import _specificity_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinarySpecificity(BinaryStatScores):
@@ -320,19 +321,17 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinarySpecificity(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassSpecificity(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelSpecificity(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/specificity_at_sensitivity.py b/src/torchmetrics/classification/specificity_at_sensitivity.py
index b0fed56213c..05f4aa03c14 100644
--- a/src/torchmetrics/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/classification/specificity_at_sensitivity.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinarySpecificityAtSensitivity(BinaryPrecisionRecallCurve):
@@ -327,18 +328,16 @@ def __new__(  # type: ignore
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
-        if task == "binary":
+        task = ClassificationTask.from_str(task)
+        if task == ClassificationTask.BINARY:
             return BinarySpecificityAtSensitivity(min_sensitivity, thresholds, ignore_index, validate_args, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassSpecificityAtSensitivity(
                 num_classes, min_sensitivity, thresholds, ignore_index, validate_args, **kwargs
             )
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelSpecificityAtSensitivity(
                 num_labels, min_sensitivity, thresholds, ignore_index, validate_args, **kwargs
             )
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/stat_scores.py b/src/torchmetrics/classification/stat_scores.py
index 1c70ec32365..8fd61a10748 100644
--- a/src/torchmetrics/classification/stat_scores.py
+++ b/src/torchmetrics/classification/stat_scores.py
@@ -36,6 +36,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class _AbstractStatScores(Metric):
@@ -495,19 +496,17 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryStatScores(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassStatScores(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelStatScores(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/functional/classification/accuracy.py b/src/torchmetrics/functional/classification/accuracy.py
index 61b6c778054..109ebf35c41 100644
--- a/src/torchmetrics/functional/classification/accuracy.py
+++ b/src/torchmetrics/functional/classification/accuracy.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _accuracy_reduce(
@@ -396,20 +397,19 @@ def accuracy(
         >>> accuracy(preds, target, task="multiclass", num_classes=3, top_k=2)
         tensor(0.6667)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_accuracy(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_accuracy(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_accuracy(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/auroc.py b/src/torchmetrics/functional/classification/auroc.py
index 7565f6f99c3..f5548f8634b 100644
--- a/src/torchmetrics/functional/classification/auroc.py
+++ b/src/torchmetrics/functional/classification/auroc.py
@@ -38,6 +38,7 @@
 )
 from torchmetrics.utilities.compute import _auc_compute_without_check, _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -450,14 +451,12 @@ def auroc(
         >>> auroc(preds, target, task='multiclass', num_classes=3)
         tensor(0.7778)
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_auroc(preds, target, max_fpr, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_auroc(preds, target, num_classes, average, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_auroc(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/average_precision.py b/src/torchmetrics/functional/classification/average_precision.py
index e481ea44ab5..a905f14bc49 100644
--- a/src/torchmetrics/functional/classification/average_precision.py
+++ b/src/torchmetrics/functional/classification/average_precision.py
@@ -36,6 +36,7 @@
 )
 from torchmetrics.utilities.compute import _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -437,16 +438,14 @@ def average_precision(
         >>> average_precision(pred, target, task="multiclass", num_classes=5, average=None)
         tensor([1.0000, 1.0000, 0.2500, 0.2500,    nan])
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_average_precision(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_average_precision(
             preds, target, num_classes, average, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_average_precision(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/calibration_error.py b/src/torchmetrics/functional/classification/calibration_error.py
index 97428675ee3..ef32dd14f14 100644
--- a/src/torchmetrics/functional/classification/calibration_error.py
+++ b/src/torchmetrics/functional/classification/calibration_error.py
@@ -23,6 +23,7 @@
     _multiclass_confusion_matrix_format,
     _multiclass_confusion_matrix_tensor_validation,
 )
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 def _binning_bucketize(
@@ -347,10 +348,11 @@ def calibration_error(
     :func:`binary_calibration_error` and :func:`multiclass_calibration_error` for the specific details of
     each argument influence and examples.
     """
+    task = ClassificationTaskNoMultilabel.from_str(task)
     assert norm is not None
-    if task == "binary":
+    if task == ClassificationTaskNoMultilabel.BINARY:
         return binary_calibration_error(preds, target, n_bins, norm, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTaskNoMultilabel.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_calibration_error(preds, target, num_classes, n_bins, norm, ignore_index, validate_args)
     raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multiclass'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/cohen_kappa.py b/src/torchmetrics/functional/classification/cohen_kappa.py
index 75bf6b53b4f..3a771858ae1 100644
--- a/src/torchmetrics/functional/classification/cohen_kappa.py
+++ b/src/torchmetrics/functional/classification/cohen_kappa.py
@@ -27,6 +27,7 @@
     _multiclass_confusion_matrix_tensor_validation,
     _multiclass_confusion_matrix_update,
 )
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 def _cohen_kappa_reduce(confmat: Tensor, weights: Optional[Literal["linear", "quadratic", "none"]] = None) -> Tensor:
@@ -256,9 +257,10 @@ class labels.
         >>> cohen_kappa(preds, target, task="multiclass", num_classes=2)
         tensor(0.5000)
     """
-    if task == "binary":
+    task = ClassificationTaskNoMultilabel.from_str(task)
+    if task == ClassificationTaskNoMultilabel.BINARY:
         return binary_cohen_kappa(preds, target, threshold, weights, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTaskNoMultilabel.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_cohen_kappa(preds, target, num_classes, weights, ignore_index, validate_args)
-    raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multiclass'` but got {task}")
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/confusion_matrix.py b/src/torchmetrics/functional/classification/confusion_matrix.py
index 562bbdc3042..2a04b14e723 100644
--- a/src/torchmetrics/functional/classification/confusion_matrix.py
+++ b/src/torchmetrics/functional/classification/confusion_matrix.py
@@ -19,6 +19,7 @@
 
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -630,14 +631,12 @@ def confusion_matrix(
                 [[1, 0], [1, 0]],
                 [[0, 1], [0, 1]]])
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_confusion_matrix(preds, target, threshold, normalize, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_confusion_matrix(preds, target, num_classes, normalize, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_confusion_matrix(preds, target, num_labels, threshold, normalize, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/exact_match.py b/src/torchmetrics/functional/classification/exact_match.py
index 89e043cbcd7..da41cb8c016 100644
--- a/src/torchmetrics/functional/classification/exact_match.py
+++ b/src/torchmetrics/functional/classification/exact_match.py
@@ -26,6 +26,7 @@
     _multilabel_stat_scores_tensor_validation,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTaskNoBinary
 
 
 def _exact_match_reduce(
@@ -229,12 +230,13 @@ def exact_match(
         >>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='samplewise')
         tensor([1., 0.])
     """
-    if task == "multiclass":
+    task = ClassificationTaskNoBinary.from_str(task)
+    if task == ClassificationTaskNoBinary.MULTICLASS:
         assert num_classes is not None
         return multiclass_exact_match(preds, target, num_classes, multidim_average, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTaskNoBinary.MULTILABEL:
         assert num_labels is not None
         return multilabel_exact_match(
             preds, target, num_labels, threshold, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(f"Expected argument `task` to either be `'multiclass'` or `'multilabel'` but got {task}")
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/f_beta.py b/src/torchmetrics/functional/classification/f_beta.py
index 02200d6c0f0..5a649f70dab 100644
--- a/src/torchmetrics/functional/classification/f_beta.py
+++ b/src/torchmetrics/functional/classification/f_beta.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _fbeta_reduce(
@@ -692,23 +693,21 @@ def fbeta_score(
         >>> fbeta_score(preds, target, task="multiclass", num_classes=3, beta=0.5)
         tensor(0.3333)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_fbeta_score(preds, target, beta, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_fbeta_score(
             preds, target, beta, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_fbeta_score(
             preds, target, beta, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
 
 
 def f1_score(
@@ -741,20 +740,18 @@ def f1_score(
         >>> f1_score(preds, target, task="multiclass", num_classes=3)
         tensor(0.3333)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_f1_score(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_f1_score(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_f1_score(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/hamming.py b/src/torchmetrics/functional/classification/hamming.py
index 15b4733376c..e6c75379b5e 100644
--- a/src/torchmetrics/functional/classification/hamming.py
+++ b/src/torchmetrics/functional/classification/hamming.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _hamming_distance_reduce(
@@ -398,20 +399,19 @@ def hamming_distance(
         >>> hamming_distance(preds, target, task="binary")
         tensor(0.2500)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_hamming_distance(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_hamming_distance(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_hamming_distance(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/hinge.py b/src/torchmetrics/functional/classification/hinge.py
index 5621c1441fe..72cbdd85fb2 100644
--- a/src/torchmetrics/functional/classification/hinge.py
+++ b/src/torchmetrics/functional/classification/hinge.py
@@ -24,6 +24,7 @@
     _multiclass_confusion_matrix_tensor_validation,
 )
 from torchmetrics.utilities.data import to_onehot
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 def _hinge_loss_compute(measure: Tensor, total: Tensor) -> Tensor:
@@ -276,9 +277,10 @@ def hinge_loss(
         >>> hinge_loss(preds, target, task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
         tensor([1.3743, 1.1945, 1.2359])
     """
-    if task == "binary":
+    task = ClassificationTaskNoMultilabel.from_str(task)
+    if task == ClassificationTaskNoMultilabel.BINARY:
         return binary_hinge_loss(preds, target, squared, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTaskNoMultilabel.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_hinge_loss(preds, target, num_classes, squared, multiclass_mode, ignore_index, validate_args)
-    raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multilabel'` but got {task}")
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/jaccard.py b/src/torchmetrics/functional/classification/jaccard.py
index 5cc33273a5c..203dccb0b09 100644
--- a/src/torchmetrics/functional/classification/jaccard.py
+++ b/src/torchmetrics/functional/classification/jaccard.py
@@ -32,6 +32,7 @@
     _multilabel_confusion_matrix_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _jaccard_index_reduce(
@@ -321,14 +322,13 @@ def jaccard_index(
         >>> jaccard_index(pred, target, task="multiclass", num_classes=2)
         tensor(0.9660)
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_jaccard_index(preds, target, threshold, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_jaccard_index(preds, target, num_classes, average, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_jaccard_index(preds, target, num_labels, threshold, average, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/matthews_corrcoef.py b/src/torchmetrics/functional/classification/matthews_corrcoef.py
index c43b592dc1b..999746f11a4 100644
--- a/src/torchmetrics/functional/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/functional/classification/matthews_corrcoef.py
@@ -31,6 +31,7 @@
     _multilabel_confusion_matrix_tensor_validation,
     _multilabel_confusion_matrix_update,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _matthews_corrcoef_reduce(confmat: Tensor) -> Tensor:
@@ -213,7 +214,7 @@ def multilabel_matthews_corrcoef(
 def matthews_corrcoef(
     preds: Tensor,
     target: Tensor,
-    task: Literal["binary", "multiclass", "multilabel"] = None,
+    task: Literal["binary", "multiclass", "multilabel"],
     threshold: float = 0.5,
     num_classes: Optional[int] = None,
     num_labels: Optional[int] = None,
@@ -235,14 +236,13 @@ def matthews_corrcoef(
         >>> matthews_corrcoef(preds, target, task="multiclass", num_classes=2)
         tensor(0.5774)
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_matthews_corrcoef(preds, target, threshold, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_matthews_corrcoef(preds, target, num_classes, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_matthews_corrcoef(preds, target, num_labels, threshold, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/precision_recall.py b/src/torchmetrics/functional/classification/precision_recall.py
index f04521b5b94..c131b2169b1 100644
--- a/src/torchmetrics/functional/classification/precision_recall.py
+++ b/src/torchmetrics/functional/classification/precision_recall.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _precision_recall_reduce(
@@ -652,15 +653,15 @@ def precision(
         tensor(0.2500)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_precision(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_precision(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_precision(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
@@ -704,20 +705,19 @@ def recall(
         >>> recall(preds, target, task="multiclass", average='micro', num_classes=3)
         tensor(0.2500)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_recall(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_recall(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_recall(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/precision_recall_curve.py b/src/torchmetrics/functional/classification/precision_recall_curve.py
index 99678d075b6..3391e7c1032 100644
--- a/src/torchmetrics/functional/classification/precision_recall_curve.py
+++ b/src/torchmetrics/functional/classification/precision_recall_curve.py
@@ -22,6 +22,7 @@
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.compute import _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _binary_clf_curve(
@@ -815,14 +816,12 @@ def precision_recall_curve(
         >>> thresholds
         [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_precision_recall_curve(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_precision_recall_curve(preds, target, num_classes, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_precision_recall_curve(preds, target, num_labels, thresholds, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/recall_at_fixed_precision.py b/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
index d7d907fa755..8f79370c69a 100644
--- a/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
@@ -34,6 +34,7 @@
     _multilabel_precision_recall_curve_tensor_validation,
     _multilabel_precision_recall_curve_update,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _recall_at_precision(
@@ -384,18 +385,16 @@ def recall_at_fixed_precision(
     :func:`binary_recall_at_fixed_precision`, :func:`multiclass_recall_at_fixed_precision` and
     :func:`multilabel_recall_at_fixed_precision` for the specific details of each argument influence and examples.
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_recall_at_fixed_precision(preds, target, min_precision, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_recall_at_fixed_precision(
             preds, target, num_classes, min_precision, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_recall_at_fixed_precision(
             preds, target, num_labels, min_precision, thresholds, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/roc.py b/src/torchmetrics/functional/classification/roc.py
index 7c0fe8d6a5e..ab77240fd4b 100644
--- a/src/torchmetrics/functional/classification/roc.py
+++ b/src/torchmetrics/functional/classification/roc.py
@@ -34,6 +34,7 @@
 )
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _binary_roc_compute(
@@ -483,14 +484,12 @@ def roc(
          tensor([1.0000, 0.7576, 0.3680, 0.3468, 0.0745]),
          tensor([1.0000, 0.1837, 0.1338, 0.1183, 0.1138])]
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_roc(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_roc(preds, target, num_classes, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_roc(preds, target, num_labels, thresholds, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/specificity.py b/src/torchmetrics/functional/classification/specificity.py
index 64e5f20b218..fa5f2b11567 100644
--- a/src/torchmetrics/functional/classification/specificity.py
+++ b/src/torchmetrics/functional/classification/specificity.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _specificity_reduce(
@@ -369,20 +370,19 @@ def specificity(
         >>> specificity(preds, target, task="multiclass", average='micro', num_classes=3)
         tensor(0.6250)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_specificity(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_specificity(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_specificity(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
index e86a14da6ec..a97fcfa0a56 100644
--- a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
@@ -36,6 +36,7 @@
     _multiclass_roc_compute,
     _multilabel_roc_compute,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _convert_fpr_to_specificity(fpr: Tensor) -> Tensor:
@@ -413,20 +414,19 @@ def specicity_at_sensitivity(
     :func:`binary_specificity_at_sensitivity`, :func:`multiclass_specicity_at_sensitivity` and
     :func:`multilabel_specifity_at_sensitvity` for the specific details of each argument influence and examples.
     """
-    if task == "binary":
+    task = ClassificationTask.from_str(task)
+    if task == ClassificationTask.BINARY:
         return binary_specificity_at_sensitivity(  # type: ignore
             preds, target, min_sensitivity, thresholds, ignore_index, validate_args
         )
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_specificity_at_sensitivity(  # type: ignore
             preds, target, num_classes, min_sensitivity, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_specificity_at_sensitivity(  # type: ignore
             preds, target, num_labels, min_sensitivity, thresholds, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/stat_scores.py b/src/torchmetrics/functional/classification/stat_scores.py
index 1b5aa2282f8..3ca1b5b9447 100644
--- a/src/torchmetrics/functional/classification/stat_scores.py
+++ b/src/torchmetrics/functional/classification/stat_scores.py
@@ -19,7 +19,7 @@
 
 from torchmetrics.utilities.checks import _check_same_shape, _input_format_classification
 from torchmetrics.utilities.data import _bincount, select_topk
-from torchmetrics.utilities.enums import AverageMethod, DataType, MDMCAverageMethod
+from torchmetrics.utilities.enums import AverageMethod, ClassificationTask, DataType, MDMCAverageMethod
 
 
 def _binary_stat_scores_arg_validation(
@@ -1081,20 +1081,18 @@ def stat_scores(
                 [1, 1, 1, 1, 2],
                 [1, 0, 3, 0, 1]])
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_stat_scores(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_stat_scores(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_stat_scores(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/regression/kendall.py b/src/torchmetrics/functional/regression/kendall.py
index 287aa006f69..8308fadbb28 100644
--- a/src/torchmetrics/functional/regression/kendall.py
+++ b/src/torchmetrics/functional/regression/kendall.py
@@ -26,46 +26,26 @@
 class _MetricVariant(EnumStr):
     """Enumerate for metric variants."""
 
+    @staticmethod
+    def _name() -> str:
+        return "variant"
+
     A = "a"
     B = "b"
     C = "c"
 
-    @classmethod
-    def from_str(cls, value: Literal["a", "b", "c"]) -> "_MetricVariant":  # type: ignore[override]
-        """Raises:
-        ValueError:
-        If required metric variant is not among the supported options.
-        """
-        _allowed_variants = [im.lower() for im in _MetricVariant._member_names_]
-
-        enum_key = super().from_str(value)
-        if enum_key is not None and enum_key in _allowed_variants:
-            return enum_key  # type: ignore[return-value]  # use override
-        raise ValueError(f"Invalid metric variant. Expected one of {_allowed_variants}, but got {enum_key}.")
-
 
 class _TestAlternative(EnumStr):
     """Enumerate for test altenative options."""
 
+    @staticmethod
+    def _name() -> str:
+        return "alternative"
+
     TWO_SIDED = "two-sided"
     LESS = "less"
     GREATER = "greater"
 
-    @classmethod
-    def from_str(cls, value: Literal["two-sided", "less", "greater"]) -> "_TestAlternative":  # type: ignore[override]
-        """Load from string.
-
-        Raises:
-            ValueError:
-                If required test alternative is not among the supported options.
-        """
-        _allowed_alternatives = [im.lower().replace("_", "-") for im in _TestAlternative._member_names_]
-
-        enum_key = super().from_str(value.replace("-", "_"))
-        if enum_key is not None and enum_key in _allowed_alternatives:
-            return enum_key  # type: ignore[return-value]  # use override
-        raise ValueError(f"Invalid test alternative. Expected one of {_allowed_alternatives}, but got {enum_key}.")
-
 
 def _sort_on_first_sequence(x: Tensor, y: Tensor) -> Tuple[Tensor, Tensor]:
     """Sort sequences in an ascent order according to the sequence ``x``."""
diff --git a/src/torchmetrics/functional/text/infolm.py b/src/torchmetrics/functional/text/infolm.py
index b65d19fd139..25033d00100 100644
--- a/src/torchmetrics/functional/text/infolm.py
+++ b/src/torchmetrics/functional/text/infolm.py
@@ -54,6 +54,10 @@
 class _IMEnum(EnumStr):
     """A helper Enum class for storing the information measure."""
 
+    @staticmethod
+    def _name() -> str:
+        return "Information measure"
+
     KL_DIVERGENCE = "kl_divergence"
     ALPHA_DIVERGENCE = "alpha_divergence"
     BETA_DIVERGENCE = "beta_divergence"
@@ -64,19 +68,6 @@ class _IMEnum(EnumStr):
     L_INFINITY_DISTANCE = "l_infinity_distance"
     FISHER_RAO_DISTANCE = "fisher_rao_distance"
 
-    @classmethod
-    def from_str(cls, value: str) -> Optional["EnumStr"]:
-        """Raises:
-        ValueError:
-        If required information measure is not among the supported options.
-        """
-        _allowed_im = [im.lower() for im in _IMEnum._member_names_]
-
-        enum_key = super().from_str(value)
-        if enum_key is not None and enum_key in _allowed_im:
-            return enum_key
-        raise ValueError(f"Invalid information measure. Expected one of {_allowed_im}, but got {enum_key}.")
-
 
 class _InformationMeasure:
     """A wrapper class used for the calculation the result of information measure between the discrete reference
diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 75fc6e61ca3..f2327854165 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -11,38 +11,39 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from enum import Enum
-from typing import Optional, Union
+from typing import Optional
 
+from lightning_utilities.core.enums import StrEnum as StrEnum
 
-class EnumStr(str, Enum):
-    """Type of any enumerator with allowed comparison to string invariant to cases.
 
-    Example:
-        >>> class MyEnum(EnumStr):
-        ...     ABC = 'abc'
-        >>> MyEnum.from_str('Abc')
-        <MyEnum.ABC: 'abc'>
-        >>> {MyEnum.ABC: 123}
-        {<MyEnum.ABC: 'abc'>: 123}
-    """
+class EnumStr(StrEnum):
+    @staticmethod
+    def _name() -> str:
+        return "Task"
 
     @classmethod
-    def from_str(cls, value: str) -> Optional["EnumStr"]:
-        statuses = [status for status in dir(cls) if not status.startswith("_")]
-        for st in statuses:
-            if st.lower() == value.lower():
-                return getattr(cls, st)
-        return None
+    def from_str(cls, value: str) -> "EnumStr":
+        """Load from string.
 
-    def __eq__(self, other: Union[str, "EnumStr", None]) -> bool:  # type: ignore
-        other = other.value if isinstance(other, Enum) else str(other)
-        return self.value.lower() == other.lower()
+        Raises:
+            ValueError:
+                If required value is not among the supported options.
 
-    def __hash__(self) -> int:
-        # re-enable hashtable so it can be used as a dict key or in a set
-        # example: set(EnumStr)
-        return hash(self.name)
+        >>> class MyEnum(EnumStr):
+        ...     a = "aaa"
+        ...     b = "bbb"
+        >>> MyEnum.from_str("a")
+        <MyEnum.a: 'aaa'>
+        >>> MyEnum.from_str("c")
+        Traceback (most recent call last):
+          ...
+        ValueError: Invalid Task: expected one of ['a', 'b'], but got c.
+        """
+        enum_key = super().from_str(value.replace("-", "_"))
+        if enum_key is not None:
+            return enum_key
+        _allowed_im = [m.lower() for m in cls._member_names_]
+        raise ValueError(f"Invalid {cls._name()}: expected one of {_allowed_im}, but got {value}.")
 
 
 class DataType(EnumStr):
@@ -52,6 +53,10 @@ class DataType(EnumStr):
     True
     """
 
+    @staticmethod
+    def _name() -> str:
+        return "Data type"
+
     BINARY = "binary"
     MULTILABEL = "multi-label"
     MULTICLASS = "multi-class"
@@ -69,6 +74,10 @@ class AverageMethod(EnumStr):
     True
     """
 
+    @staticmethod
+    def _name() -> str:
+        return "Average method"
+
     MICRO = "micro"
     MACRO = "macro"
     WEIGHTED = "weighted"
@@ -79,5 +88,55 @@ class AverageMethod(EnumStr):
 class MDMCAverageMethod(EnumStr):
     """Enum to represent multi-dim multi-class average method."""
 
+    @staticmethod
+    def _name() -> str:
+        return "MDMC Average method"
+
     GLOBAL = "global"
     SAMPLEWISE = "samplewise"
+
+
+class ClassificationTask(EnumStr):
+    """Enum to represent the different tasks in classification metrics.
+
+    >>> "binary" in list(ClassificationTask)
+    True
+    """
+
+    @staticmethod
+    def _name() -> str:
+        return "Classification"
+
+    BINARY = "binary"
+    MULTICLASS = "multiclass"
+    MULTILABEL = "multilabel"
+
+
+class ClassificationTaskNoBinary(EnumStr):
+    """Enum to represent the different tasks in classification metrics.
+
+    >>> "binary" in list(ClassificationTaskNoBinary)
+    False
+    """
+
+    @staticmethod
+    def _name() -> str:
+        return "Classification"
+
+    MULTILABEL = "multilabel"
+    MULTICLASS = "multiclass"
+
+
+class ClassificationTaskNoMultilabel(EnumStr):
+    """Enum to represent the different tasks in classification metrics.
+
+    >>> "multilabel" in list(ClassificationTaskNoMultilabel)
+    False
+    """
+
+    @staticmethod
+    def _name() -> str:
+        return "Classification"
+
+    BINARY = "binary"
+    MULTICLASS = "multiclass"