Upload wikicat_en.py

wikicat_en.py

@@ -0,0 +1,88 @@
+# Loading script for the WikiCAT dataset.
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+
+            """
+
+_DESCRIPTION = """
+                   WikiCAT: Text Classification English dataset from the Viquipedia
+
+               """
+
+_HOMEPAGE = """   """
+
+# TODO: upload datasets to github
+_URL = "https://huggingface.co/datasets/crodri/wikicat_en/resolve/main/"
+_TRAINING_FILE = "hftrain_en.json"
+_DEV_FILE = "hfeval_en.json"
+#_TEST_FILE = "test.json"
+
+
+class wikicat_enConfig(datasets.BuilderConfig):
+    """ Builder config for the Topicat dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for wikicat_en.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(teclaConfig, self).__init__(**kwargs)
+
+
+class wikicat_en(datasets.GeneratorBasedBuilder):
+    """ wikicat_en Dataset """
+
+    BUILDER_CONFIGS = [
+        wikicat_enConfig(
+            name="wikicat_en",
+            version=datasets.Version("1.1.0"),
+            description="wikicat_en",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel
+                        (names= ['Health', 'Law', 'Entertainment', 'Religion', 'Business', 'Science', 'Engineering', 'Nature', 'Philosophy', 'Economy', 'Sports', 'Technology', 'Government', 'Mathematics', 'Military', 'Humanities', 'Music', 'Politics', 'History']
+                    ),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+#            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+#            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            wikicat_en = json.load(f)
+            for id_, article in enumerate(wikicat_en["data"]):
+                text = article["sentence"]
+                label = article["label"]
+                yield id_, {
+                    "text": text,
+                    "label": label,
+                }