Upload fair-rationales.py

fair-rationales.py

@@ -58,15 +58,12 @@ class FairRationalesConfig(datasets.BuilderConfig):
 
     def __init__(
         self,
-        dataname,
+        name,
         url,
         data_url,
-        citation,
-        # data_file,
-        # attentioncheck,
-        # group_id,
-        # originaldata_split,
         attributes,
+        citation,
+        description,
         label_classes=None,
         label_classes_original=None,
         **kwargs,
@@ -87,16 +84,13 @@ class FairRationalesConfig(datasets.BuilderConfig):
           **kwargs: keyword arguments forwarded to super.
         """
         super(FairRationalesConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.dataname = dataname
+        self.name = name
         self.label_classes = label_classes
         self.label_classes_original = label_classes_original
-        # self.attentioncheck = attentioncheck
-        # self.group_id = group_id
-        # self.originaldata_split = originaldata_split
         self.attributes = attributes
-        self.data_url = data_url
-        # self.data_file = data_file
         self.url = url
+        self.data_url = data_url
+        self.description = description
         self.citation = citation
 
 
@@ -105,7 +99,7 @@ class FairRationales(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         FairRationalesConfig(
-            dataname="sst2",
+            name="sst2",
             description=textwrap.dedent(
                 """\
                 The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.
@@ -152,7 +146,7 @@ class FairRationales(datasets.GeneratorBasedBuilder):
             ),
         ),
         FairRationalesConfig(
-            dataname="dynasent",
+            name="dynasent",
             description=textwrap.dedent(
                 """\
                 DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis.
@@ -185,7 +179,7 @@ class FairRationales(datasets.GeneratorBasedBuilder):
             ),
         ),
         FairRationalesConfig(
-            dataname="cose",
+            name="cose",
             description=textwrap.dedent(
                 """\
                 Common Sense Explanations (CoS-E) allows for training language models to automatically 
@@ -238,7 +232,7 @@ class FairRationales(datasets.GeneratorBasedBuilder):
                     "originaldata_id": datasets.Value("string"),
                     "annotator_ID": datasets.Value("int64")
                     }
-        if self.config.dataname == "cose":
+        if self.config.name == "cose":
             features["label"] = datasets.Value("string")
         else:
             features["label"] = datasets.ClassLabel(names=self.config.label_classes)
@@ -290,7 +284,7 @@ class FairRationales(datasets.GeneratorBasedBuilder):
                 }
                 for attribute_name, _ in self.config.attributes:
                     example[attribute_name] = data[attribute_name]
-                if self.config.dataname == "sst2":
+                if self.config.name == "sst2":
                     example["sst2_id"] = data["sst2_id"]
                     example["sst2_split"] = data["sst2_split"]
                 yield id_, example