diff --git a/pytorch_lightning/metrics/classification/f_beta.py b/pytorch_lightning/metrics/classification/f_beta.py
index d6147b00463b3..85a0d3f736bac 100755
--- a/pytorch_lightning/metrics/classification/f_beta.py
+++ b/pytorch_lightning/metrics/classification/f_beta.py
@@ -52,11 +52,11 @@ class FBeta(Metric):
             Threshold value for binary or multi-label logits. default: 0.5
 
         average:
-            * `'micro'` computes metric globally
-            * `'macro'` computes metric for each class and uniformly averages them
-            * `'weighted'` computes metric for each class and does a weighted-average,
-                where each class is weighted by their support (accounts for class imbalance)
-            * `None` computes and returns the metric per class
+            - ``'micro'`` computes metric globally
+            - ``'macro'`` computes metric for each class and uniformly averages them
+            - ``'weighted'`` computes metric for each class and does a weighted-average,
+              where each class is weighted by their support (accounts for class imbalance)
+            - ``'none'`` computes and returns the metric per class
 
         multilabel: If predictions are from multilabel classification.
         compute_on_step:
@@ -185,11 +185,11 @@ class F1(FBeta):
             Threshold value for binary or multi-label logits. default: 0.5
 
         average:
-            * `'micro'` computes metric globally
-            * `'macro'` computes metric for each class and uniformly averages them
-            * `'weighted'` computes metric for each class and does a weighted-average,
-                where each class is weighted by their support (accounts for class imbalance)
-            * `None` computes and returns the metric per class
+            - ``'micro'`` computes metric globally
+            - ``'macro'`` computes metric for each class and uniformly averages them
+            - ``'weighted'`` computes metric for each class and does a weighted-average,
+              where each class is weighted by their support (accounts for class imbalance)
+            - ``'none'`` computes and returns the metric per class
 
         multilabel: If predictions are from multilabel classification.
         compute_on_step:
diff --git a/pytorch_lightning/metrics/functional/f_beta.py b/pytorch_lightning/metrics/functional/f_beta.py
index 3f0a7a0449325..5f6962d2b3964 100755
--- a/pytorch_lightning/metrics/functional/f_beta.py
+++ b/pytorch_lightning/metrics/functional/f_beta.py
@@ -83,11 +83,11 @@ def fbeta(
             Threshold value for binary or multi-label logits. default: 0.5
 
         average:
-            * `'micro'` computes metric globally
-            * `'macro'` computes metric for each class and uniformly averages them
-            * `'weighted'` computes metric for each class and does a weighted-average,
-                where each class is weighted by their support (accounts for class imbalance)
-            * `None` computes and returns the metric per class
+            - ``'micro'`` computes metric globally
+            - ``'macro'`` computes metric for each class and uniformly averages them
+            - ``'weighted'`` computes metric for each class and does a weighted-average,
+              where each class is weighted by their support (accounts for class imbalance)
+            - ``'none'`` computes and returns the metric per class
 
         multilabel: If predictions are from multilabel classification.
 
@@ -136,11 +136,11 @@ def f1(
             Threshold value for binary or multi-label logits. default: 0.5
 
         average:
-            * `'micro'` computes metric globally
-            * `'macro'` computes metric for each class and uniformly averages them
-            * `'weighted'` computes metric for each class and does a weighted-average,
-                where each class is weighted by their support (accounts for class imbalance)
-            * `None` computes and returns the metric per class
+            - ``'micro'`` computes metric globally
+            - ``'macro'`` computes metric for each class and uniformly averages them
+            - ``'weighted'`` computes metric for each class and does a weighted-average,
+              where each class is weighted by their support (accounts for class imbalance)
+            - ``'none'`` computes and returns the metric per class
 
         multilabel: If predictions are from multilabel classification.