fix doc linking

src/torchmetrics/classification/accuracy.py

@@ -350,8 +350,6 @@ class Accuracy(StatScores):
     changed to subset accuracy (which requires all labels or sub-samples in the sample to
     be correctly predicted) by setting ``subset_accuracy=True``.
 
-    Accepts all input types listed in :ref:`pages/classification:input types`.
-
     Args:
         num_classes:
             Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods.
@@ -387,11 +385,10 @@ class Accuracy(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -409,9 +406,7 @@ class Accuracy(StatScores):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         subset_accuracy:
             Whether to compute subset accuracy for multi-label and multi-dimensional
@@ -557,9 +552,7 @@ def __init__(
             self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
-        """Update state with predictions and targets. See
-        :ref:`pages/classification:input types` for more information on input
-        types.
+        """Update state with predictions and targets.
 
         Args:
             preds: Predictions from model (logits, probabilities, or labels)

src/torchmetrics/classification/dice.py

@@ -33,7 +33,7 @@ class Dice(StatScores):
 
     The reduction method (how the precision scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`pages/classification:input types`.
+    multi-dimensional multi-class case.
 
     Args:
         num_classes:
@@ -69,11 +69,11 @@ class Dice(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`) are flattened into a new ``N_X`` sample axis, i.e.
+              are flattened into a new ``N_X`` sample axis, i.e.
               the inputs are treated as if they were ``(N_X, C)``.
               From here on the ``average`` parameter applies as usual.
 
@@ -90,9 +90,7 @@ class Dice(StatScores):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 

src/torchmetrics/classification/f_beta.py

@@ -773,11 +773,10 @@ class FBetaScore(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -795,9 +794,7 @@ class FBetaScore(StatScores):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -958,11 +955,10 @@ class F1Score(FBetaScore):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -979,9 +975,7 @@ class F1Score(FBetaScore):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 

src/torchmetrics/classification/hamming.py

@@ -340,8 +340,6 @@ class HammingDistance(Metric):
     treats each possible label separately - meaning that, for example, multi-class data is
     treated as if it were multi-label.
 
-    Accepts all input types listed in :ref:`pages/classification:input types`.
-
     Args:
         threshold:
             Threshold for transforming probability or logit predictions to binary ``(0,1)`` predictions, in the case
@@ -423,8 +421,6 @@ def __init__(
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         """Update state with predictions and targets.
 
-        See :ref:`pages/classification:input types` for more information on input types.
-
         Args:
             preds: Predictions from model (probabilities, logits or labels)
             target: Ground truth labels

src/torchmetrics/classification/precision_recall.py

@@ -623,7 +623,7 @@ class Precision(StatScores):
 
     The reduction method (how the precision scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`pages/classification:input types`.
+    multi-dimensional multi-class case.
 
     Args:
         num_classes:
@@ -657,11 +657,11 @@ class Precision(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`) are flattened into a new ``N_X`` sample axis, i.e.
+              are flattened into a new ``N_X`` sample axis, i.e.
               the inputs are treated as if they were ``(N_X, C)``.
               From here on the ``average`` parameter applies as usual.
 
@@ -678,9 +678,7 @@ class Precision(StatScores):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -813,7 +811,7 @@ class Recall(StatScores):
 
     The reduction method (how the recall scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`pages/classification:input types`.
+    multi-dimensional multi-class case.
 
     Args:
         num_classes:
@@ -846,11 +844,10 @@ class Recall(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -868,9 +865,7 @@ class Recall(StatScores):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 

src/torchmetrics/classification/specificity.py

@@ -314,7 +314,7 @@ class Specificity(StatScores):
 
     The reduction method (how the specificity scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`pages/classification:input types`.
+    multi-dimensional multi-class case.
 
     Args:
         num_classes:
@@ -348,11 +348,10 @@ class Specificity(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -371,9 +370,7 @@ class Specificity(StatScores):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 

src/torchmetrics/classification/stat_scores.py

@@ -503,7 +503,7 @@ class StatScores(Metric):
     ``reduce`` parameter, and additionally by the ``mdmc_reduce`` parameter in the
     multi-dimensional multi-class case.
 
-    Accepts all inputs listed in :ref:`pages/classification:input types`.
+
 
     Args:
         threshold:
@@ -539,7 +539,7 @@ class StatScores(Metric):
         mdmc_reduce: Defines how the multi-dimensional multi-class inputs are handeled. Should be one of the following:
 
             - ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
-              multi-class (see :ref:`pages/classification:input types` for the definition of input types).
+              multi-class.
 
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then the outputs are concatenated together. In each
@@ -553,9 +553,7 @@ class StatScores(Metric):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -690,8 +688,6 @@ def __init__(
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         """Update state with predictions and targets.
 
-        See :ref:`pages/classification:input types` for more information on input types.
-
         Args:
             preds: Predictions from model (probabilities, logits or labels)
             target: Ground truth values

src/torchmetrics/functional/classification/accuracy.py

@@ -660,8 +660,6 @@ def accuracy(
     changed to subset accuracy (which requires all labels or sub-samples in the sample to
     be correctly predicted) by setting ``subset_accuracy=True``.
 
-    Accepts all input types listed in :ref:`pages/classification:input types`.
-
     Args:
         preds: Predictions from model (probabilities, logits or labels)
         target: Ground truth labels
@@ -693,11 +691,11 @@ def accuracy(
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`)
+
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -715,9 +713,7 @@ def accuracy(
             Should be left at default (``None``) for all other types of inputs.
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
         ignore_index:
             Integer specifying a target class to ignore. If given, this class index does not contribute
             to the returned score, regardless of reduction method. If an index is ignored, and ``average=None``

src/torchmetrics/functional/classification/dice.py

@@ -178,7 +178,7 @@ def dice(
 
     The reduction method (how the recall scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`pages/classification:input types`.
+    multi-dimensional multi-class case.
 
     Args:
         preds: Predictions from model (probabilities, logits or labels)
@@ -213,11 +213,10 @@ def dice(
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -240,9 +239,7 @@ def dice(
             Should be left at default (``None``) for all other types of inputs.
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
     Return:
         The shape of the returned tensor depends on the ``average`` parameter