Add some code instructions

sescore.py

@@ -55,38 +55,35 @@ class robertaEncoder(BERTEncoder):
 
 # TODO: Add BibTeX citation
 _CITATION = """\
-@InProceedings{huggingface:module,
-title = {A great new module},
-authors={huggingface, Inc.},
-year={2020}
+@inproceedings{xu-etal-2022-not,
+  title={Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis},
+  author={Xu, Wenda and Tuan, Yi-lin and Lu, Yujie and Saxon, Michael and Li, Lei and Wang, William Yang},
+  booktitle ={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
+  month={dec},
+  year={2022},
+  url={https://arxiv.org/abs/2210.05035}
 }
 """
 
-# TODO: Add description of the module here
 _DESCRIPTION = """\
 SEScore is an evaluation metric that trys to compute an overall score to measure text generation quality.
 """
 
-
-# TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references, using certain scores
+Calculates how good are predictions given some references
 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.
+    predictions: list of candidate outputs
+    references: list of references 
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    {"mean_score": mean_score, "scores": scores}
+    
 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}
+    >>> import evaluate
+    >>> sescore = evaluate.load("xu1998hz/sescore")
+    >>> score = sescore.compute(
+            references=['sescore is a simple but effective next-generation text evaluation metric'],
+            predictions=['sescore is simple effective text evaluation metric for next generation']
+        )
 """
 
 # TODO: Define external resources urls if needed
@@ -95,7 +92,7 @@ BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
 class SEScore(evaluate.Metric):
-    """TODO: Short description of my evaluation module."""
+    """SEScore"""
 
     def _info(self):
         # TODO: Specifies the evaluate.EvaluationModuleInfo object