Datasets:
simple_questions_v2

	# coding=utf-8
	# Copyright 2020 HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	import os

	import datasets


	_DESCRIPTION = """\
	SimpleQuestions is a dataset for simple QA, which consists
	of a total of 108,442 questions written in natural language by human
	English-speaking annotators each paired with a corresponding fact,
	formatted as (subject, relationship, object), that provides the answer
	but also a complete explanation. Fast have been extracted from the
	Knowledge Base Freebase (freebase.com). We randomly shuffle these
	questions and use 70% of them (75910) as training set, 10% as
	validation set (10845), and the remaining 20% as test set.
	"""
	_HOMEPAGE_URL = "https://research.fb.com/downloads/babi/"
	_CITATION = """\
	@misc{bordes2015largescale,
	title={Large-scale Simple Question Answering with Memory Networks},
	author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},
	year={2015},
	eprint={1506.02075},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	"""

	_URL = "https://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1"


	class SimpleQuestionsV2Config(datasets.BuilderConfig):
	def __init__(self, args, data_type=None, *kwargs):
	super().__init__(args, version=datasets.Version("1.0.0", ""), *kwargs)
	self.data_type = data_type


	class SimpleQuestionsV2(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	SimpleQuestionsV2Config(name="annotated", data_type="annotated", description="Annotated dataset"),
	SimpleQuestionsV2Config(name="freebase2m", data_type="freebase2m", description="Freebase subset 2M"),
	SimpleQuestionsV2Config(name="freebase5m", data_type="freebase5m", description="Freebase subset 5M"),
	]
	BUILDER_CONFIG_CLASS = SimpleQuestionsV2Config
	DEFAULT_CONFIG_NAME = "annotated"

	def _info(self):
	if self.config.data_type == "annotated":
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"subject_entity": datasets.Value("string"),
	"relationship": datasets.Value("string"),
	"object_entity": datasets.Value("string"),
	"question": datasets.Value("string"),
	},
	)
	else:
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"subject_entity": datasets.Value("string"),
	"relationship": datasets.Value("string"),
	"object_entities": datasets.Sequence(datasets.Value("string")),
	},
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE_URL,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	path = dl_manager.download_and_extract(_URL)
	if self.config.data_type == "annotated":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
	),
	]
	elif self.config.data_type == "freebase2m":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"datapath": os.path.join(
	path,
	"SimpleQuestions_v2",
	"freebase-subsets",
	"freebase-FB2M.txt",
	)
	},
	)
	]
	elif self.config.data_type == "freebase5m":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"datapath": os.path.join(
	path,
	"SimpleQuestions_v2",
	"freebase-subsets",
	"freebase-FB5M.txt",
	)
	},
	)
	]
	else:
	raise Exception("Unknown data type. Try one of: annotated, freebase2m and freebase5m")

	def _generate_examples(self, datapath):
	if self.config.data_type == "annotated":
	with open(datapath, encoding="utf-8") as f:
	for sentence_counter, row in enumerate(f):
	row = row.split("\t")
	result = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"subject_entity": row[0],
	"relationship": row[1],
	"object_entity": row[2],
	"question": row[3],
	},
	)
	yield result
	else:
	with open(datapath, encoding="utf-8") as f:
	for sentence_counter, row in enumerate(f):
	row = row.split("\t")
	result = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"subject_entity": row[0],
	"relationship": row[1],
	"object_entities": row[2].split(),
	},
	)
	yield result