Upload car.py

car.py

@@ -22,7 +22,7 @@ _BASE_FEATURE_NAMES = [
 	"persons",
 	"lug_boot",
 	"safety",
-	"class"
+	"acceptability_level"
 ]
 urls_per_split = {
 	"train": "https://huggingface.co/datasets/mstz/car/raw/main/car.data"
@@ -35,8 +35,8 @@ features_types_per_config = {
 		"persons": datasets.Value("int8"),
 		"lug_boot": datasets.Value("int8"),
 		"safety": datasets.Value("int8"),
-		"class": datasets.ClassLabel(num_classes=4,
-									 names=("unacceptable", "acceptable", "good", "very good"))
+		"acceptability_level": datasets.ClassLabel(num_classes=4,
+									 			   names=("unacceptable", "acceptable", "good", "very good"))
 	},
 	"car_binary": {
 		"buying": datasets.Value("int8"),
@@ -45,8 +45,8 @@ features_types_per_config = {
 		"persons": datasets.Value("int8"),
 		"lug_boot": datasets.Value("int8"),
 		"safety": datasets.Value("int8"),
-		"class": datasets.ClassLabel(num_classes=2,
-									 names=("unacceptable", "acceptable"))
+		"acceptability_level": datasets.ClassLabel(num_classes=2,
+												   names=("unacceptable", "acceptable"))
 	},
 	
 }
@@ -139,7 +139,7 @@ class Car(datasets.GeneratorBasedBuilder):
 			data.loc[:, feature] = data[feature].apply(encoding_function)
 
 		if config == "car_binary":
-			data.loc[:, "class"] = data["class"].apply(lambda x: x if x == 0 else 1)
+			data.loc[:, "acceptability_level"] = data["acceptability_level"].apply(lambda x: x if x == 0 else 1)
 
 
 		return data