Datasets:
wiki_snippets

	# WARNING: Please, do not use the code in this script as a template to create another script:
	# - It is a bad practice to use `datasets.load_dataset` inside a loading script. Please, avoid doing it.

	import json
	import math

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@ONLINE {wikidump,
	author = {Wikimedia Foundation},
	title = {Wikimedia Downloads},
	url = {https://dumps.wikimedia.org}
	}
	"""

	_DESCRIPTION = """\
	Wikipedia version split into plain text snippets for dense semantic indexing.
	"""

	_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."
	)


	def wiki40b_article_snippets(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 wikipedia_article_snippets(article, passage_len=100, overlap=0):
	paragraphs = [par for par in article["text"].split("\n") if not par.startswith("Category:")]
	if "References" in paragraphs:
	paragraphs = paragraphs[: paragraphs.index("References")]
	article_title = article["title"]
	section_indices = [
	i + 1
	for i, par in enumerate(paragraphs[:-2])
	if paragraphs[i] == "" and paragraphs[i + 1] != "" and paragraphs[i + 2] != ""
	]
	par_tabs = [par.split(" ") for par in paragraphs]
	word_map = [(i, len(" ".join(par[:j])), w) for i, par in enumerate(par_tabs) for j, w in enumerate(par)]
	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 start_section_id <= j <= pre_toks[-1][0]]
	section_ids = section_ids if len(section_ids) > 0 else [-1]
	passage_text = " ".join([w for p_id, s_id, w in pre_toks])
	passages += [
	{
	"article_title": article_title,
	"section_title": " & ".join(["Start" if j == -1 else paragraphs[j].strip() for j in section_ids]),
	"wiki_id": article_title.replace(" ", "_"),
	"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,
	}
	]
	return passages


	_SPLIT_FUNCTION_MAP = {
	"wikipedia": wikipedia_article_snippets,
	"wiki40b": wiki40b_article_snippets,
	}


	def generate_snippets(wikipedia, split_function, passage_len=100, overlap=0):
	for i, article in enumerate(wikipedia):
	for doc in split_function(article, passage_len, overlap):
	part_id = json.dumps(
	{
	"datasets_id": i,
	"wiki_id": doc["wiki_id"],
	"sp": doc["start_paragraph"],
	"sc": doc["start_character"],
	"ep": doc["end_paragraph"],
	"ec": doc["end_character"],
	}
	)
	doc["_id"] = part_id
	doc["datasets_id"] = i
	yield doc


	class WikiSnippetsConfig(datasets.BuilderConfig):
	"""BuilderConfig for WikiSnippets."""

	def __init__(
	self, wikipedia_name="wiki40b", wikipedia_version_name="en", snippets_length=100, snippets_overlap=0, **kwargs
	):
	"""BuilderConfig for WikiSnippets.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(WikiSnippetsConfig, self).__init__(**kwargs)
	self.wikipedia_name = wikipedia_name
	self.wikipedia_version_name = wikipedia_version_name
	self.snippets_length = snippets_length
	self.snippets_overlap = snippets_overlap


	class WikiSnippets(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIG_CLASS = WikiSnippetsConfig
	BUILDER_CONFIGS = [
	WikiSnippetsConfig(
	name="wiki40b_en_100_0",
	version=datasets.Version("1.0.0"),
	wikipedia_name="wiki40b",
	wikipedia_version_name="en",
	snippets_length=100,
	snippets_overlap=0,
	),
	WikiSnippetsConfig(
	name="wikipedia_en_100_0",
	version=datasets.Version("2.0.0"),
	wikipedia_name="wikipedia",
	wikipedia_version_name="20220301.en",
	snippets_length=100,
	snippets_overlap=0,
	),
	]

	test_dummy_data = False

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"_id": datasets.Value("string"),
	"datasets_id": datasets.Value("int32"),
	"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):
	# WARNING: It is a bad practice to use `datasets.load_dataset` inside a loading script. Please, avoid doing it.
	wikipedia = datasets.load_dataset(
	path=self.config.wikipedia_name,
	name=self.config.wikipedia_version_name,
	)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"wikipedia": wikipedia}),
	]

	def _generate_examples(self, wikipedia):
	logger.info(f"generating examples from = {self.config.wikipedia_name} {self.config.wikipedia_version_name}")
	for split in wikipedia:
	dset = wikipedia[split]
	split_function = _SPLIT_FUNCTION_MAP[self.config.wikipedia_name]
	for doc in generate_snippets(
	dset, split_function, passage_len=self.config.snippets_length, overlap=self.config.snippets_overlap
	):
	id_ = doc["_id"]
	yield id_, doc