Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 title: nDCG
 emoji: 👁
-colorFrom: orange
+colorFrom: yellow
 colorTo: red
 sdk: gradio
 sdk_version: 3.9.1
@@ -20,7 +20,7 @@ description: >-
                 2. Documents/Labels are relevant to different degrees
         It is defined as the Sum over all relevances of the retrieved documents reduced logarithmically proportional to 
         the position in which they were retrieved.
-        The Normalized DCG (nDCG) divides the resulting value by the optimal value, that can be achieved, to get a value between 
+        The Normalized DCG (nDCG) divides the resulting value by the best possible value to get a value between 
         0 and 1 s.t. a perfect retrieval achieves a nDCG of 1.
 ---
 
@@ -50,25 +50,26 @@ print(results)
 ```
 
 ### Inputs:
-**references** ('list' of 'float'): True relevance 
+**references** (`list` of `float`): True relevance 
 
-**predictions** ('list' of 'float'): Either predicted relevance, probability estimates or confidence values 
+**predictions** (`list` of `float`): Either predicted relevance, probability estimates or confidence values 
 
-**k** (int): If set to a value only the k highest scores in the ranking will be considered, else considers all outputs.
+**k** (`int`): If set to a value only the k highest scores in the ranking will be considered, else considers all outputs.
         Defaults to None.
 
 **sample_weight** (`list` of `float`): Sample weights Defaults to None.
 
-**ignore_ties** ('boolean'): If set to true, assumes that there are no ties (this is likely if predictions are continuous)
+**ignore_ties** (`boolean`): If set to true, assumes that there are no ties (this is likely if predictions are continuous)
         for efficiency gains. Defaults to False.
 
 ### Output:
-**normalized_discounted_cumulative_gain** ('float'): The averaged nDCG scores for all samples. 
+**normalized_discounted_cumulative_gain** (`float`): The averaged nDCG scores for all samples. 
         Minimum possible value is 0.0 Maximum possible value is 1.0
 
 Output Example(s):
 ```python
 {'nDCG@5': 1.0}
+{'nDCG': 0.876}
 ```
 This metric outputs a dictionary, containing the nDCG score