add modules

bangalore_score.py

@@ -28,29 +28,31 @@ year={2020}
 
 # TODO: Add description of the module here
 _DESCRIPTION = """\
-This new module is designed to solve this great ML task and is crafted with a lot of care.
+Computing metrics on generated tabular data
 """
 
 
 # TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references, using certain scores
+Computing loss and accuracy on generated tabular data
 Args:
     predictions: list of predictions to score. Each predictions
         should be a string with tokens separated by spaces.
     references: list of reference for each prediction. Each
         reference should be a string with tokens separated by spaces.
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    bleu: bleu score (for normalized data)
+    rougeL: rougeL score (for normalized data)
+    levenstein_distance: levenstein distance (for normalized data)
+    format_score: score for correctness of generated output format
 Examples:
     Examples should be written in doctest format, and should illustrate how
     to use the function.
 
-    >>> my_new_module = evaluate.load("my_new_module")
-    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
+    >>> my_new_module = evaluate.load("DoctorSlimm/bangalore_score")
+    >>> results = my_new_module.compute(references=[str, str], predictions=[str, str])
     >>> print(results)
-    {'accuracy': 1.0}
+    {'bleu': 0.5, 'rougeL': 0.5, 'levenstein_distance': 0.5, 'format_score': 0.5}
 """
 
 # TODO: Define external resources urls if needed