Update ultimate_arabic_news.py

ultimate_arabic_news.py

@@ -8,8 +8,8 @@ _HOMEPAGE = "TODO"
 
 _LICENSE = "TODO"
 
-_URL = {"news":"https://huggingface.co/datasets/khalidalt/ultimate_arabic_news/blob/main/UltimateArabic.csv"
-        "news_preproces":"https://huggingface.co/datasets/khalidalt/ultimate_arabic_news/blob/main/UltimateArabicPrePros.csv"}
+_URL = {"UltimateArabic":"https://huggingface.co/datasets/khalidalt/ultimate_arabic_news/blob/main/UltimateArabic.csv"
+        "UltimateArabicPrePros":"https://huggingface.co/datasets/khalidalt/ultimate_arabic_news/blob/main/UltimateArabicPrePros.csv"}
         
           
 class UAN_Config(datasets.BuilderConfig):
@@ -27,7 +27,7 @@ class UAN_Config(datasets.BuilderConfig):
 class Ultimate_Arabic_News(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.1.0")
         BUILDER_CONFIGS = [
-        TydiqaConfig(
+        UAN_Config(
             name="UltimateArabic",
             description=textwrap.dedent(
                 """\
@@ -36,7 +36,7 @@ class Ultimate_Arabic_News(datasets.GeneratorBasedBuilder):
           language processing tasks such as text classification."""
             ),
         ),
-        TydiqaConfig(
+        UAN_Config(
             name="UltimateArabicPrePros",
             description=textwrap.dedent(
                 """UltimateArabicPrePros: It is a file that contains the data mentioned in the first file, but after pre-processing, where 
@@ -66,7 +66,7 @@ class Ultimate_Arabic_News(datasets.GeneratorBasedBuilder):
             # builder.as_dataset.
             supervised_keys=None,
             # Homepage of the dataset for documentation
-            homepage="https://github.com/google-research-datasets/tydiqa",
+            homepage="https://data.mendeley.com/datasets/jz56k5wxz7/1",
             citation=_CITATION,
         )
 
@@ -76,31 +76,30 @@ class Ultimate_Arabic_News(datasets.GeneratorBasedBuilder):
         # TODO(tydiqa): Downloads the data and defines the splits
         # dl_manager is a datasets.download.DownloadManager that can be used to
         # download and extract URLs
-        primary_downloaded = dl_manager.download_and_extract(_PRIMARY_URLS)
-        secondary_downloaded = dl_manager.download_and_extract(_SECONDARY_URLS)
-        if self.config.name == "primary_task":
+        UltAr_downloaded = dl_manager.download_and_extract(_URL['UltimateArabic'])
+        UltArPre_downloaded = dl_manager.download_and_extract(_URL['UltimateArabicPrePros'])
+        if self.config.name == "UltimateArabic":
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
                     # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": primary_downloaded["train"]},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": primary_downloaded["dev"]},
+                    gen_kwargs={"filepath": UltAr_downloaded["train"]},
                 ),
             ]
-        elif self.config.name == "secondary_task":
+        elif self.config.name == "UltimateArabicPrePros":
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
                     # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": secondary_downloaded["train"]},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": secondary_downloaded["dev"]},
+                    gen_kwargs={"filepath": UltArPre_downloaded["train"]},
                 ),
-            ]
+
+            ]
+            
+    def _generate_examples(self, csv_file):
+        with open(csv_file, encoding="utf-8") as f:
+            data =  csv.DictReader(f)
+            for row, item in enumerate(data):
+                yield row, {"text": item['text'],"label": item['label']}
+        
+