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"""Wikipedia snippets in parquet format"""
import math
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 = """\
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."
)
class WikipediaSnippetsStreamed(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"wiki_id": datasets.Value("string"),
"start_paragraph": datasets.Value("int32"),
"start_character": datasets.Value("int32"),
"end_paragraph": datasets.Value("int32"),
"end_character": datasets.Value("int32"),
"article_title": datasets.Value("string"),
"section_title": datasets.Value("string"),
"passage_text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://dumps.wikimedia.org",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
url = "https://storage.googleapis.com/huggingface-nlp/cache/datasets/wiki40b/en/1.1.0/wiki40b-train.parquet"
downloaded_file = dl_manager.download(url)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}),
]
def wiki40b_article_snippets(self, article, passage_len=100, overlap=0):
paragraphs = article["text"].split("\n")
aticle_idx = paragraphs.index("_START_ARTICLE_") + 1
article_title = paragraphs[aticle_idx] if aticle_idx < len(paragraphs) else ""
section_indices = [i + 1 for i, par in enumerate(paragraphs[:-1]) if par == "_START_SECTION_"]
par_tabs = [par.split(" ") for par in paragraphs]
word_map = [
(i, len(" ".join(par[:j])), w)
for i, par in enumerate(par_tabs)
if not par[0].startswith("_START_")
for j, w in enumerate(par)
if i > 0
]
step_size = passage_len - overlap
passages = []
for i in range(math.ceil(len(word_map) / step_size)):
pre_toks = word_map[i * step_size: i * step_size + passage_len]
start_section_id = max([0] + [j for j in section_indices if j <= pre_toks[0][0]])
section_ids = [j for j in section_indices if j >= start_section_id and j <= pre_toks[-1][0]]
section_ids = section_ids if len(section_ids) > 0 else [0]
passage_text = " ".join([w for p_id, s_id, w in pre_toks])
passages += [
{
"article_title": article_title,
"section_title": " & ".join([paragraphs[j] for j in section_ids]),
"wiki_id": article["wikidata_id"],
"start_paragraph": pre_toks[0][0],
"start_character": pre_toks[0][1],
"end_paragraph": pre_toks[-1][0],
"end_character": pre_toks[-1][1] + len(pre_toks[-1][2]) + 1,
"passage_text": passage_text.replace("_NEWLINE_", "\n"),
}
]
return passages
def _generate_examples(self, filepath):
logger.info("generating examples from = %s", filepath)
passage_counter = 0
with open(filepath, "rb") as f:
pf = pq.ParquetFile(f)
for i in range(pf.num_row_groups):
batch_table_dict = pf.read_row_group(i).to_pydict()
for idx, article_text in enumerate(batch_table_dict["text"]):
article = {"text": article_text, "wikidata_id": batch_table_dict["wikidata_id"][idx]}
passages = self.wiki40b_article_snippets(article)
for passage in passages:
yield ++passage_counter, passage
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