Datasets:

flax-sentence-embeddings
/

stackexchange_math_jsonl

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the Flax Sentence Embeddings team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""The dataset is a collection of Question and Answer automatically extracted from Stack Exchange community network."""


	import csv
	import json
	import os

	import datasets


	_CITATION = """\
	@misc{StackExchangeDataset,
	author = {Flax Sentence Embeddings Team},
	title = {Stack Exchange question pairs},
	year = {2021},
	howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://huggingface.co/datasets/flax-sentence-embeddings/"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "https://archive.org/details/stackexchange"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLs = {
	'titlebody_upvoted_downvoted_answer': "titlebody_upvoted_downvoted_answer.jsonl.gz",
	'title_answer': "title_answer.jsonl.gz",
	'titlebody_answer': "titlebody_answer.jsonl.gz",
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class StackExchangeMatch(datasets.GeneratorBasedBuilder):
	"""The dataset is a collection of Question and Answer automatically extracted from match Stack Exchange community."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="titlebody_upvoted_downvoted_answer", version=VERSION,
	description="Includes title and body from the question as well as most upvoted and downvoted answer."),
	datasets.BuilderConfig(name="title_answer", version=VERSION,
	description="Includes title from the question as well as most upvoted answer."),
	datasets.BuilderConfig(name="titlebody_answer", version=VERSION,
	description="Includes title and body from the question as well as most upvoted answer.")
	]

	DEFAULT_CONFIG_NAME = "title_answer" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	if self.config.name == "titlebody_upvoted_downvoted_answer": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"title_body": datasets.Value("string"),
	"upvoted_answer": datasets.Value("string"),
	"downvoted_answer": datasets.Value("string")
	}
	)
	elif self.config.name == "titlebody_answer": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"title_body": datasets.Value("string"),
	"upvoted_answer": datasets.Value("string"),
	}
	)
	else: # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"title": datasets.Value("string"),
	"upvoted_answer": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	my_urls = _URLs[self.config.name]
	data_file = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_file,
	},
	)
	]

	def _generate_examples(
	self, filepath # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	""" Yields examples as (key, example) tuples. """
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	if self.config.name == "titlebody_upvoted_downvoted_answer":
	yield id_, {
	"title_body": data[0],
	"upvoted_answer": data[1],
	"downvoted_answer": data[2],
	}
	elif self.config.name == "titlebody_answer":
	yield id_, {
	"title_body": data[0],
	"upvoted_answer": data[1],
	}
	else:
	yield id_, {
	"title": data[0],
	"upvoted_answer": data[1],
	}