update

ctc_eval.py

@@ -19,16 +19,19 @@ import datasets
 
 # TODO: Add BibTeX citation
 _CITATION = """\
-@InProceedings{huggingface:module,
-title = {A great new module},
-authors={huggingface, Inc.},
-year={2020}
+@inproceedings{deng2021compression,
+  title={Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation},
+  author={Deng, Mingkai and Tan, Bowen and Liu, Zhengzhong and Xing, Eric and Hu, Zhiting},
+  booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
+  pages={7580--7605},
+  year={2021}
 }
 """
 
 # 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.
+This repo contains code of an automatic evaluation metric described in the paper
+Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation
 """
 
 
@@ -36,21 +39,15 @@ This new module is designed to solve this great ML task and is crafted with a lo
 _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.
+    predictions: List of texts (Hypothesis) to score. The list now only supports one piece of text
+    references: List of texts (Premise) to score. The list now only supports one piece of text
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    ctc_score: The CTC score
 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])
+    >>> ctc_score = evaluate.load("yzha/ctc_eval")
+    >>> results = ctc_score.compute(references=['hello world'], predictions='hi world')
     >>> print(results)
-    {'accuracy': 1.0}
+    {'ctc_score': 0.5211202502250671}
 """
 
 # TODO: Define external resources urls if needed
@@ -75,10 +72,10 @@ class CTC_Eval(evaluate.EvaluationModule):
                 'references': datasets.Value('large_string'),
             }),
             # Homepage of the module for documentation
-            homepage="http://module.homepage",
+            homepage="https://github.com/tanyuqian/ctc-gen-eval",
             # Additional links to the codebase or references
-            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
-            reference_urls=["http://path.to.reference.url/new_module"]
+            codebase_urls=["https://github.com/tanyuqian/ctc-gen-eval"],
+            reference_urls=["https://github.com/tanyuqian/ctc-gen-eval"]
         )
 
     def _download_and_prepare(self, dl_manager):