Create ultimate_arabic_news.py

ultimate_arabic_news.py

@@ -0,0 +1,106 @@
+import csv
+import datasets
+import os
+
+_DESCRIPTION = "TODO"
+
+_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"}
+        
+          
+class UAN_Config(datasets.BuilderConfig):
+
+    """BuilderConfig for Ultamte Arabic News"""
+
+    def __init__(self, **kwargs):
+        """
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(UAN_Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        
+            
+class Ultimate_Arabic_News(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+        BUILDER_CONFIGS = [
+        TydiqaConfig(
+            name="UltimateArabic",
+            description=textwrap.dedent(
+                """\
+          UltimateArabic: A file containing more than 193,000 original Arabic news texts, without pre-processing. The texts contain words, 
+          numbers, and symbols that can be removed using pre-processing to increase accuracy when using the dataset in various Arabic natural 
+          language processing tasks such as text classification."""
+            ),
+        ),
+        TydiqaConfig(
+            name="UltimateArabicPrePros",
+            description=textwrap.dedent(
+                """UltimateArabicPrePros: It is a file that contains the data mentioned in the first file, but after pre-processing, where 
+                the number of data became about 188,000 text documents, where stop words, non-Arabic words, symbols and numbers have been 
+                removed so that this file is ready for use directly in the various Arabic natural language processing tasks. Like text 
+                classification.
+          """
+            ),
+        ),
+    ]
+
+    def _info(self):
+        # TODO(tydiqa): Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/google-research-datasets/tydiqa",
+            citation=_CITATION,
+        )
+
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # 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":
+            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"]},
+                ),
+            ]
+        elif self.config.name == "secondary_task":
+            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"]},
+                ),
+            ]