Datasets:

cam-cst
/

cbt

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""Children's Book Test Dataset."""


	import datasets


	_CITATION = """\
	@misc{hill2016goldilocks,
	title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
	author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston},
	year={2016},
	eprint={1511.02301},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""


	_DESCRIPTION = """\
	The Children’s Book Test (CBT) is designed to measure directly
	how well language models can exploit wider linguistic context.
	The CBT is built from books that are freely available.
	"""

	_HOMEPAGE = "https://research.fb.com/downloads/babi/"

	_LICENSE = """GNU Free Documentation License v1.3"""

	ZIP_URL = "data/CBTest.tgz"
	dir = "CBTest/data/"
	paths = {
	"raw": {"train": dir + "cbt_train.txt", "valid": dir + "cbt_valid.txt", "test": dir + "cbt_test.txt"},
	"V": {
	"train": dir + "cbtest_V_train.txt",
	"valid": dir + "cbtest_V_valid_2000ex.txt",
	"test": dir + "cbtest_V_test_2500ex.txt",
	},
	"P": {
	"train": dir + "cbtest_P_train.txt",
	"valid": dir + "cbtest_P_valid_2000ex.txt",
	"test": dir + "cbtest_P_test_2500ex.txt",
	},
	"NE": {
	"train": dir + "cbtest_NE_train.txt",
	"valid": dir + "cbtest_NE_valid_2000ex.txt",
	"test": dir + "cbtest_NE_test_2500ex.txt",
	},
	"CN": {
	"train": dir + "cbtest_CN_train.txt",
	"valid": dir + "cbtest_CN_valid_2000ex.txt",
	"test": dir + "cbtest_CN_test_2500ex.txt",
	},
	}


	class Cbt(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="raw", version=VERSION, description="This part of my dataset covers the raw CBT books"
	),
	datasets.BuilderConfig(
	name="V", version=VERSION, description="This part of my dataset covers the verb answer CBT dataset"
	),
	datasets.BuilderConfig(
	name="P", version=VERSION, description="This part of my dataset covers the preposition answer CBT dataset"
	),
	datasets.BuilderConfig(
	name="NE",
	version=VERSION,
	description="This part of my dataset covers the named entity answer CBT dataset",
	),
	datasets.BuilderConfig(
	name="CN", version=VERSION, description="This part of my dataset covers the common noun answer CBT dataset"
	),
	]

	def _info(self):
	if self.config.name in ["V", "P", "NE", "CN"]:
	features = datasets.Features(
	{
	"sentences": datasets.Sequence(datasets.Value("string")), # There are 20 sentences
	"question": datasets.Value("string"),
	"answer": datasets.Value("string"),
	"options": datasets.Sequence(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"), "content": 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."""
	my_urls = ZIP_URL # Cannot download just one single type as it is a compressed file.
	archive = dl_manager.download(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": paths[self.config.name]["train"], "files": dl_manager.iter_archive(archive)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": paths[self.config.name]["test"], "files": dl_manager.iter_archive(archive)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": paths[self.config.name]["valid"], "files": dl_manager.iter_archive(archive)},
	),
	]

	def _generate_examples(self, filepath, files):
	"""Yields examples as (key, example) tuples."""
	for path, f in files:
	if path == filepath:
	if self.config.name != "raw":
	sentences = []
	example_idx = 0
	for idx, line in enumerate(f):
	line = line.decode("utf-8")
	if line.strip() == "":
	continue

	elif line.split()[0] == "21":
	splitline = line.split("\t") # question, answer options are tab separated
	question = splitline[0]
	answer = splitline[1]
	options = splitline[-1]
	question = question[2:].strip() # The first two indices contain `21`.
	answer = answer.strip()
	options = options.strip().split("\|")
	yield example_idx, {
	"sentences": sentences,
	"question": question,
	"options": options,
	"answer": answer,
	}

	sentences = []
	example_idx += 1
	else:
	if len(line.split()[0]) == 1:
	sentences.append(line[1:].strip())
	else:
	sentences.append(line[2:].strip())
	# Text might contain double spaces.
	else:
	book_idx = 0
	book_sentences = []
	for idx, line in enumerate(f):
	line = line.decode("utf-8")
	if line[:12] == "_BOOK_TITLE_":
	if idx == 0: # First line:
	title = line.split(":")[1].strip()
	else:
	yield book_idx, {
	"title": title,
	"content": "".join(book_sentences),
	}
	title = line.split(":")[1].strip()
	book_sentences = []
	book_idx += 1
	else:
	book_sentences.append(line)
	else:
	yield book_idx, {
	"title": title,
	"content": "".join(book_sentences),
	}
	book_sentences = []
	book_idx += 1
	break