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.gitattributes

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wikipedia_bn.py

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+"""Bengali wikipedia from 03/20/2021"""
+
+import os
+import pyarrow.parquet as pq
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@ONLINE {wikidump,
+    author = {Wikimedia Foundation},
+    title  = {Wikimedia Downloads},
+    url    = {https://dumps.wikimedia.org}
+}
+"""
+
+_DESCRIPTION = """\
+Bengali Wikipedia from the dump of 03/20/2021.
+The data was processed using the huggingface datasets wikipedia script early april 2021.
+The dataset was built from the Wikipedia dump (https://dumps.wikimedia.org/).
+Each example contains the content of one full Wikipedia article with cleaning to strip
+markdown and unwanted sections (references, etc.).
+"""
+
+_LICENSE = (
+    "This work is licensed under the Creative Commons Attribution-ShareAlike "
+    "3.0 Unported License. To view a copy of this license, visit "
+    "http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to "
+    "Creative Commons, PO Box 1866, Mountain View, CA 94042, USA."
+)
+
+_N_SHARDS = 10
+
+_URLS = {
+    "train": [f"data/20210320/shard-{i:05d}-of-{_N_SHARDS:05d}.parquet" for i in range(_N_SHARDS)],
+}
+
+
+class WikipediaBn(datasets.ArrowBasedBuilder):
+    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://dumps.wikimedia.org",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}),
+        ]
+
+    def _generate_tables(self, filepaths):
+        """This function returns the examples in the raw (text) form."""
+        for filepath in filepaths:
+            logger.info("generating examples from = %s", filepath)
+            filepath_id = os.path.basename(filepath)
+            with open(filepath, "rb") as f:
+                pf = pq.ParquetFile(f)
+                for i in range(pf.num_row_groups):
+                    id_ = f"{filepath_id}_{i}"
+                    yield id_, pf.read_row_group(i)