update kobest v1 data loader

dataset_infos.json

@@ -166,7 +166,7 @@
         "num_classes": 4,
         "names": [
           "ending_1",
-          "ending_1",
+          "ending_2",
           "ending_3",
           "ending_4"
         ],

kobest_v1.py

@@ -16,7 +16,8 @@ _DESCRIPTION = """\
     The dataset contains data for KoBEST dataset
 """
 
-_URL = "https://github.com/SKT-LSL/KoBEST_datarepo"
+_URL = "https://github.com/SKT-LSL/KoBEST_datarepo/raw/main"
+
 
 _DATA_URLS = {
     "boolq": {
@@ -46,6 +47,8 @@ _DATA_URLS = {
     },
 }
 
+_LICENSE = "CC-BY-SA-4.0"
+
 
 class KoBESTConfig(datasets.BuilderConfig):
     """Config for building KoBEST"""
@@ -66,6 +69,26 @@ class KoBESTConfig(datasets.BuilderConfig):
         self.url = url
 
 
+# class KoBESTConfig(datasets.BuilderConfig):
+#     """BuilderConfig for KoTEST."""
+#
+#     def __init__(
+#         self,
+#         features,
+#         data_url,
+#         file_map,
+#         url,
+#         **kwargs,
+#     ):
+#         """BuilderConfig for KoTEST."""
+#
+#         super(KoBESTConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+#         self.features = features
+#         self.data_url = data_url
+#         self.file_map = file_map
+#         self.url = url
+
+
 class KoBEST(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
         KoBESTConfig(name=name, description=_DESCRIPTION, data_url=_DATA_URLS[name], citation=_CITATAION, url=_URL)
@@ -76,7 +99,7 @@ class KoBEST(datasets.GeneratorBasedBuilder):
     def _info(self):
         features = {}
         if self.config.name == "boolq":
-            labels = ["True", "False"]
+            labels = ["False", "True"]
             features["paragraph"] = datasets.Value("string")
             features["question"] = datasets.Value("string")
             features["label"] = datasets.features.ClassLabel(names=labels)
@@ -90,7 +113,7 @@ class KoBEST(datasets.GeneratorBasedBuilder):
             features["label"] = datasets.features.ClassLabel(names=labels)
 
         if self.config.name == "wic":
-            labels = ["True", "False"]
+            labels = ["False", "True"]
             features["word"] = datasets.Value("string")
             features["context_1"] = datasets.Value("string")
             features["context_2"] = datasets.Value("string")
@@ -127,76 +150,97 @@ class KoBEST(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
         ]
 
-        # if self.config.name == "boolq":
-        #     train = dl_manager.download_and_extract(self.config.data_url["train"])
-        #     dev = dl_manager.download_and_extract(self.config.data_url["dev"])
-        #     test = dl_manager.download_and_extract(self.config.data_url["test"])
-        #
-        #     return [
-        #         datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
-        #         datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
-        #         datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
-        #     ]
-        #
-
     def _generate_examples(self, filepath, split):
         if self.config.name == "boolq":
             df = pd.read_csv(filepath, sep="\t")
             df = df.dropna()
+            df = df[['Text', 'Question', 'Answer']]
 
-            for id_, row in df.iterrows():
-                yield id_, {
-                    "paragraph": str(row["Text"]),
-                    "question": str(row["Question"]),
-                    "label": str(int(row["Answer"])),
-                }
+            df = df.rename(columns={
+                'Text': 'paragraph',
+                'Question': 'question',
+                'Answer': 'label',
+            })
+            df['label'] = [0 if str(s) == 'False' else 1 for s in df['label'].tolist()]
 
-        if self.config.name == "copa":
+        elif self.config.name == "copa":
             df = pd.read_csv(filepath, sep="\t")
             df = df.dropna()
-
-            for id_, row in df.iterrows():
-                yield id_, {
-                    "premise": str(row["sentence"]),
-                    "question": str(row["question"]),
-                    "alternative_1": str(int(row["1"])),
-                    "alternative_2": str(int(row["2"])),
-                    "label": str(row["Answer"]-1),
-                }
-
-        if self.config.name == "wic":
+            df = df[['sentence', 'question', '1', '2', 'Answer']]
+
+            df = df.rename(columns={
+                'sentence': 'premise',
+                'question': 'question',
+                '1': 'alternative_1',
+                '2': 'alternative_2',
+                'Answer': 'label',
+            })
+            df['label'] = [i-1 for i in df['label'].tolist()]
+
+        elif self.config.name == "wic":
             df = pd.read_csv(filepath, sep="\t")
             df = df.dropna()
+            df = df[['Target', 'SENTENCE1', 'SENTENCE2', 'ANSWER']]
 
-            for id_, row in df.iterrows():
-                yield id_, {
-                    "word": str(row["Target"]),
-                    "context_1": str(row["SENTENCE1"]),
-                    "context_2": str(int(row["SENTENCE2"])),
-                    "label": str(int(row["Answer"])),
-                }
+            df = df.rename(columns={
+                'Target': 'word',
+                'SENTENCE1': 'context_1',
+                'SENTENCE2': 'context_2',
+                'ANSWER': 'label',
+            })
+            df['label'] = [0 if str(s) == 'False' else 1 for s in df['label'].tolist()]
 
-        if self.config.name == "hellaswag":
+        elif self.config.name == "hellaswag":
             df = pd.read_csv(filepath, sep="\t")
             df = df.dropna()
-
-            for id_, row in df.iterrows():
-                yield id_, {
-                    "context": str(row["context"]),
-                    "ending_1": str(row["choice1"]),
-                    "ending_2": str(int(row["choice2"])),
-                    "ending_3": str(int(row["choice3"])),
-                    "ending_4": str(int(row["choice4"])),
-                    "label": str(row["label"]),
-                }
-
-        if self.config.name == "sentineg":
+            df = df[['context', 'choice1', 'choice2', 'choice3', 'choice4', 'label']]
+
+            # for id_, row in df.iterrows():
+            #     yield id_, {
+            #         "context": str(row["context"]),
+            #         "ending_1": str(row["choice1"]),
+            #         "ending_2": str(int(row["choice2"])),
+            #         "ending_3": str(int(row["choice3"])),
+            #         "ending_4": str(int(row["choice4"])),
+            #         "label": str(row["label"]),
+            #     }
+            #
+            df = df.rename(columns={
+                'context': 'context',
+                'choice1': 'ending_1',
+                'choice2': 'ending_2',
+                'choice3': 'ending_3',
+                'choice4': 'ending_4',
+                'label': 'label',
+            })
+
+        elif self.config.name == "sentineg":
             df = pd.read_csv(filepath, sep="\t")
             df = df.dropna()
+            df = df[['Text', 'Label']]
+
+            # for id_, row in df.iterrows():
+            #     yield id_, {
+            #         "sentence": str(row["Text"]),
+            #         "label": str(int(row["Label"])),
+            #     }
+
+            df = df.rename(columns={
+                'Text': 'sentence',
+                'Label': 'label',
+            })
+
+        else:
+            raise NotImplementedError
+
+        for id_, row in df.iterrows():
+            features = {key: row[key] for key in row.keys()}
+            yield id_, features
+
 
-            for id_, row in df.iterrows():
-                yield id_, {
-                    "sentence": str(row["Text"]),
-                    "label": str(int(row["Label"])),
-                }
+if __name__ == "__main__":
+    for task in ['boolq', 'copa', 'wic', 'hellaswag', 'sentineg']:
+        dataset = datasets.load_dataset("kobest_v1.py", task, ignore_verifications=True)
+        print(dataset)
+        print(dataset['train']['label'])