Create xlsum_fa.py

xlsum_fa.py

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+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}