Upload README.md

README.md

@@ -28,9 +28,9 @@ Add *predictions*, *references* and *prediction_strategies* as follows:
 ```
 
 The minimum fields required by this metric for the test datasets are the following (not necessarily with these names):
-	- *title* containing the first sentence to be compared with different queries representing each class. 
-	- *label_ids* containing the *id* of the class the sample refers to. Including samples of all the classes is advised.
-	- *nli_label* which is '0' if the sample represents a True Positive or '2' if the sample represents a False Positive, meaning that the *label_ids* is incorrectly assigned to the *title*. Including both True Positive and False Positive samples for all classes is advised.
+- *title* containing the first sentence to be compared with different queries representing each class. 
+- *label_ids* containing the *id* of the class the sample refers to. Including samples of all the classes is advised.
+- *nli_label* which is '0' if the sample represents a True Positive or '2' if the sample represents a False Positive, meaning that the *label_ids* is incorrectly assigned to the *title*. Including both True Positive and False Positive samples for all classes is advised.
 		
 	Example:
 	|title                                                                              |label_ids  |nli_label   |
@@ -40,14 +40,15 @@ The minimum fields required by this metric for the test datasets are the followi
 
 ### Inputs
 
-	- *predictions*, *(numpy.array(float32)[sentences to classify,number of classes])*: numpy array with the softmax logits values of the entailment dimension of the NLI inference on the sentences to be classified for each class.
-	- *references* , *(numpy.array(int32)[sentences to classify,2]: numpy array with the reference *label_ids* and *nli_label* of the sentences to be classified, given in the *test_dataset*.
-	- *kwarg* named *prediction_strategies = list(list(str, int(optional)))*, each *list(list(str, int(optional)))* describing a desired prediction strategy. The *prediction_strategies* implemented in this metric are:
-		- *argmax*, which takes the highest value of the softmax inference logits to select the prediction. Syntax: *["argmax_max"]*
-		- *threshold*, which takes all softmax inference logits above a certain value to select the predictions. Syntax: *["threshold", desired value]*
-		- *topk*, which takes the highest *k* softmax inference logits to select the predictions. Syntax: *["topk", desired value]*
+- *predictions*, *(numpy.array(float32)[sentences to classify,number of classes])*: numpy array with the softmax logits values of the entailment dimension of the NLI inference on the sentences to be classified for each class.
+- *references* , *(numpy.array(int32)[sentences to classify,2]: numpy array with the reference *label_ids* and *nli_label* of the sentences to be classified, given in the *test_dataset*.
+- *kwarg* named *prediction_strategies = list(list(str, int(optional)))*, each *list(list(str, int(optional)))* describing a desired prediction strategy. The *prediction_strategies* implemented in this metric are:
+	- *argmax*, which takes the highest value of the softmax inference logits to select the prediction. Syntax: *["argmax_max"]*
+	- *threshold*, which takes all softmax inference logits above a certain value to select the predictions. Syntax: *["threshold", desired value]*
+	- *topk*, which takes the highest *k* softmax inference logits to select the predictions. Syntax: *["topk", desired value]*
+
+	Example:
 
-		Example:
 ```
 	prediction_strategies = [['argmax_max'],['threshold', 0.5],['topk,3']] 
 ```