Datasets:
Sub-tasks:
multiple-choice-qa
Languages:
English
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
Tags:
License:
# 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 = "http://www.thespermwhale.com/jaseweston/babi/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 | |