docstring

parapluie.py

@@ -52,31 +52,21 @@ It has shown the highest correlation with human judgement on paraphrase classifi
 """
 
 
-# TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references, using certain scores
 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.
+    sources (`list` of `string`): Source sentences.
+    hypotheses (`list` of `string`): Hypothetical paraphrases.
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    score (`float`): ParaPLUIE score. Minimum possible value is -inf. Maximum possible value is +inf. A score greater than 0 mean that sentences are paraphrases. A score lower than 0 mean the opposite.
 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])
-    >>> print(results)
-    {'accuracy': 1.0}
+    Example 1-A simple example
+        >>> accuracy_metric = evaluate.load("accuracy")
+        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
+        >>> print(results)
+        {'accuracy': 0.5}
 """
 
 
-# BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
-
-
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
 class Parapluie(evaluate.Metric):
     """TODO: Short description of my evaluation module."""