Fixed some typos

nDCG.py

@@ -13,7 +13,7 @@ score (Ideal DCG, obtained for a perfect ranking) to obtain a score between
 This ranking metric returns a high value if true labels are ranked high by
 ``predictions``.
 
-If a value for k is given to the metric it will only consider the k highest
+If a value for k is given to the metric, it will only consider the k highest
 scores in the ranking 
 
 References
@@ -41,12 +41,12 @@ Args:
 
     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.
 
 Returns:
@@ -64,7 +64,7 @@ Examples:
         >>> results = nDCG_metric.compute(references=[[10, 0, 0, 1, 5]], predictions=[[.1, .2, .3, 4, 70]], k=3)
         >>> print(results)
         {'nDCG@3': 0.4123818817534531}
-    Example 3-There is only one relevant label but there is a tie and the model can't decide which one is the one.
+    Example 3-There is only one relevant label, but there is a tie and the model can't decide which one is the one.
         >>> accuracy_metric = evaluate.load("accuracy")
         >>> results = nDCG_metric.compute(references=[[1, 0, 0, 0, 0]], predictions=[[1, 1, 0, 0, 0]], k=1)
         >>> print(results)