Add ThaiCulturaX_loading_script.py

ThaiCulturaX_loading_script.py

@@ -0,0 +1,81 @@
+"""South East Asia mC4 dataset."""
+import gzip
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """
+Thai CulturaX Clean dataset."""
+_CITATION = """EMPTY"""
+_URL = "EMPTY"
+_DATA_URL = "https://huggingface.co/datasets/pythainlp/thai-culturax-clean-dataset/tree/main/data/data.{index:02d}.json"
+_LICENSE="odc-by"
+
+
+class ThaiCulturaXConfig(datasets.BuilderConfig):
+    """BuilderConfig for Dataset."""
+
+    def __init__(self, *args, languages="Thai", **kwargs):
+        """BuilderConfig for Dataset.
+        Args:
+            languages (:obj:`List[str]`): list of languages to load
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(
+            *args,
+            **kwargs,
+        )
+        self.languages = languages
+
+
+class ThaiCulturaX(datasets.GeneratorBasedBuilder):
+    """ThaiCulturaX clean dataset."""
+
+    BUILDER_CONFIGS = [
+        ThaiCulturaXConfig(anguages="Thai", version=datasets.Version("1.0.0"))
+    ]
+    BUILDER_CONFIG_CLASS = ThaiCulturaXConfig
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "meta": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_urls = {}
+        for split in ["train"]:
+            data_urls[split] = [
+                _DATA_URL.format(
+                    index=index,
+                )
+                for index in range(0, 53)
+            ]
+        train_downloaded_files = dl_manager.download(data_urls["train"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepaths": train_downloaded_files},
+            ),
+        ]
+
+    def _generate_examples(self, filepaths):
+        id_ = 0
+        for filepath in filepaths:
+            logger.info("generating examples from = %s", filepath)
+            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for line in f:
+                    if line:
+                        example = json.loads(line)
+                        yield id_, example
+                        id_ += 1