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"""Bengali wikipedia from 03/20/2021""" |
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import os |
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import pyarrow.parquet as pq |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@ONLINE {wikidump, |
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author = {Wikimedia Foundation}, |
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title = {Wikimedia Downloads}, |
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url = {https://dumps.wikimedia.org} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Bengali Wikipedia from the dump of 03/20/2021. |
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The data was processed using the huggingface datasets wikipedia script early april 2021. |
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The dataset was built from the Wikipedia dump (https://dumps.wikimedia.org/). |
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Each example contains the content of one full Wikipedia article with cleaning to strip |
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markdown and unwanted sections (references, etc.). |
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""" |
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_LICENSE = ( |
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"This work is licensed under the Creative Commons Attribution-ShareAlike " |
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"3.0 Unported License. To view a copy of this license, visit " |
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"http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to " |
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"Creative Commons, PO Box 1866, Mountain View, CA 94042, USA." |
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) |
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_N_SHARDS = 10 |
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_URLS = { |
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"train": [f"data/20210320/shard-{i:05d}-of-{_N_SHARDS:05d}.parquet" for i in range(_N_SHARDS)], |
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} |
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class WikipediaBn(datasets.ArrowBasedBuilder): |
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"""SQUAD: The Stanford Question Answering Dataset. Version 1.1.""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"title": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://dumps.wikimedia.org", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}), |
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] |
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def _generate_tables(self, filepaths): |
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"""This function returns the examples in the raw (text) form.""" |
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for filepath in filepaths: |
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logger.info("generating examples from = %s", filepath) |
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filepath_id = os.path.basename(filepath) |
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with open(filepath, "rb") as f: |
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pf = pq.ParquetFile(f) |
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for i in range(pf.num_row_groups): |
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id_ = f"{filepath_id}_{i}" |
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yield id_, pf.read_row_group(i) |
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