Create utcner.py

utcner.py

@@ -0,0 +1,88 @@
+import csv
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """Citation"""
+_DESCRIPTION = """Description"""
+
+_DOWNLOAD_URLS = {
+    "train": "https://huggingface.co/datasets/mahdiyehebrahimi/utcner/raw/main/utc_train_ner.csv",
+    "test": "https://huggingface.co/datasets/mahdiyehebrahimi/utcner/raw/main/utc_test_ner.csv",
+}
+
+
+class DatasetNameConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(DatasetNameConfig, self).__init__(**kwargs)
+
+
+class DatasetName(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        DatasetNameConfig(
+            name="utcner",
+            version=datasets.Version("1.1.1"),
+            description=_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self):
+        text_column = "text"
+        label_column = "label"
+        # TODO PROVIDE THE LABELS HERE
+        label_names = ['UndergraduateRegistrationExceptions',
+                       'CentralAuthentication&Email',
+                       'Senior(Registration,Deletion,Leave)',
+                       'Senior(Professor,Seminar,Proposal,Defense)',
+                       'Admissionwithoutatest', 'Calculateandchargetheinternet',
+                       'OfficeAutomation', 'Ph.D.(Admission,Registration,Removal,Leave)',
+                       'Ph.D.(Comprehensive,Research1and2,Opportunity)', 'Yekta|Nikan']
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {text_column: datasets.Value("string"), label_column: datasets.features.ClassLabel(names=label_names)}
+            ),
+            homepage="https://huggingface.co/datasets/mahdiyehebrahimi/utcner",
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column=text_column, label_column=label_column)],
+        )
+
+    def _split_generators(self, dl_manager):
+        """
+        Return SplitGenerators.
+        """
+        train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
+        test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    # TODO
+    def _generate_examples(self, filepath):
+        """
+        Per each file_path read the csv file and iterate it.
+        For each row yield a tuple of (id, {"text": ..., "label": ..., ...})
+        Each call to this method yields an output like below:
+        ```
+        (123, {"text": "I liked it", "label": "positive"})
+        ```
+        """
+        label2id = self.info.features[self.info.task_templates[0].label_column].str2int
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True)
+
+            # Uncomment below line to skip the first row if your csv file has a header row
+            next(csv_reader, None)
+
+            for id_, row in enumerate(csv_reader):
+                text, label  = row
+                label = label2id(label)
+                # Optional preprocessing here
+                yield id_, {"text": text, "label": label}