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# 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 os
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.
data_dir = 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": os.path.join(data_dir, paths[self.config.name]["train"]),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, paths[self.config.name]["test"]),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, paths[self.config.name]["valid"]),
},
),
]
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.
if self.config.name != "raw":
with open(filepath, encoding="utf-8") as f:
sentences = []
example_idx = 0
for idx, line in enumerate(f):
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:
with open(filepath, encoding="utf=8") as f:
book_idx = 0
book_sentences = []
for idx, line in enumerate(f):
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
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