Delete xlsum_fa.py

xlsum_fa.py

@@ -1,102 +0,0 @@
-import csv
-
-import datasets
-from datasets.tasks import Summarization
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{hasan-etal-2021-xl,
-    title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
-    author = "Hasan, Tahmid  and
-      Bhattacharjee, Abhik  and
-      Islam, Md. Saiful  and
-      Mubasshir, Kazi  and
-      Li, Yuan-Fang  and
-      Kang, Yong-Bin  and
-      Rahman, M. Sohel  and
-      Shahriyar, Rifat",
-    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
-    month = aug,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.findings-acl.413",
-    pages = "4693--4703",
-}
-"""
-
-_DESCRIPTION = """Persian portion of the XLSum Dataset"""
-
-_DOWNLOAD_URLS = {
-    "train": "https://huggingface.co/datasets/hezarai/xlsum-fa/resolve/main/xlsum-fa_train.csv",
-    "test": "https://huggingface.co/datasets/hezarai/xlsum-fa/resolve/main/xlsum-fa_test.csv",
-}
-
-
-class XLSumFaConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(XLSumFaConfig, self).__init__(**kwargs)
-
-
-class XLSumFa(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        XLSumFaConfig(
-            name="xlsum-fa",
-            version=datasets.Version("1.0.0"),
-            description=_DESCRIPTION,
-        ),
-    ]
-
-    def _info(self):
-        text_column = "text"
-        summary_column = "summary"
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {text_column: datasets.Value("string"),
-                 summary_column: datasets.features.Value("string")}
-            ),
-            homepage="https://huggingface.co/datasets/hezarai/xlsum-fa",
-            citation=_CITATION,
-            task_templates=[Summarization(text_column=text_column, summary_column=summary_column)],
-        )
-
-    def _split_generators(self, dl_manager):
-        """
-        Returns 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}
-            ),
-        ]
-
-    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": ..., "summary": ..., ...})
-        Each call to this method yields an output like below:
-        ```
-        (123, {"text": "...", "summary": "..."})
-        ```
-        """
-        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
-            )
-
-            next(csv_reader, None)
-
-            for id_, row in enumerate(csv_reader):
-                text, label = row
-                yield id_, {"text": text, "summary": label}