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EnglishNLPDataset.py

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+"""English review multi-classification dataset."""
+
+
+import csv
+import datasets
+from datasets.tasks import TextClassification
+
+_CITATION = """\
+----EnglishNLPDataset----
+"""
+
+_DESCRIPTION = """\
+The dataset, prepared in English, includes 10.000 tests, 10.000 validations and 80000 train data.
+The data is composed of customer comments and created from e-commerce sites.
+"""
+
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/BihterDass/EnglishTextClassificationDataset/main/train.csv"
+_VALIDATION_DOWNLOAD_URL ="https://raw.githubusercontent.com/BihterDass/EnglishTextClassificationDataset/main/dev.csv"
+_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/BihterDass/EnglishTextClassificationDataset/main/test.csv"
+
+class EnglishNLPDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for EnglishNLPDataset Config"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for EnglishNLPDatasetConfig
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(EnglishNLPDatasetConfig, self).__init__(**kwargs)
+
+class EnglishNLPDataset(datasets.GeneratorBasedBuilder):
+    """EnglishNLPDataset Classification dataset."""
+    BUILDER_CONFIGS = [
+       EnglishNLPDatasetConfig(
+            name="EnglishData",
+            version=datasets.Version("1.0.0"),
+            description="EnglishNLPDataset: It is a classification study that will contribute to natural language processing operations.",
+        ),
+    ]
+    def _info(self):
+      
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["neg", "nor","pos"]),
+                }
+            ),
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/BihterDass/EnglishTextClassificationDataset",
+            citation=_CITATION,
+	    task_templates=[TextClassification(text_column="text", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        validation_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
+        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file,
+                delimiter=",",
+                quoting=csv.QUOTE_ALL,
+                skipinitialspace=True,
+            )
+            for id_, row in enumerate(csv_reader):
+                (
+                    text,
+                    label,
+                ) = row
+
+                yield id_, {
+                    "text": text,
+                    "label": int(label),
+                }