exams / exams.py
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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.
"""EXAMS: a benchmark dataset for multilingual and cross-lingual question answering"""
import json
import datasets
_CITATION = """\
@article{hardalov2020exams,
title={EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering},
author={Hardalov, Momchil and Mihaylov, Todor and Dimitrina Zlatkova and Yoan Dinkov and Ivan Koychev and Preslav Nvakov},
journal={arXiv preprint arXiv:2011.03080},
year={2020}
}
"""
_DESCRIPTION = """\
EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations.
It consists of more than 24,000 high-quality high school exam questions in 16 languages,
covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.
"""
_HOMEPAGE = "https://github.com/mhardalov/exams-qa"
_LICENSE = "CC-BY-SA-4.0"
_URLS_LIST = [
("alignments", "https://github.com/mhardalov/exams-qa/raw/main/data/exams/parallel_questions.jsonl"),
]
_URLS_LIST += [
(
"multilingual_train",
"https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/train.jsonl.tar.gz",
),
("multilingual_dev", "https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/dev.jsonl.tar.gz"),
("multilingual_test", "https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/test.jsonl.tar.gz"),
(
"multilingual_with_para_train",
"https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz",
),
(
"multilingual_with_para_dev",
"https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz",
),
(
"multilingual_with_para_test",
"https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz",
),
]
_CROSS_LANGUAGES = ["bg", "hr", "hu", "it", "mk", "pl", "pt", "sq", "sr", "tr", "vi"]
_URLS_LIST += [
("crosslingual_test", "https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/test.jsonl.tar.gz"),
(
"crosslingual_with_para_test",
"https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz",
),
]
for ln in _CROSS_LANGUAGES:
_URLS_LIST += [
(
f"crosslingual_{ln}_train",
f"https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/train_{ln}.jsonl.tar.gz",
),
(
f"crosslingual_with_para_{ln}_train",
f"https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_{ln}_with_para.jsonl.tar.gz",
),
(
f"crosslingual_{ln}_dev",
f"https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/dev_{ln}.jsonl.tar.gz",
),
(
f"crosslingual_with_para_{ln}_dev",
f"https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_{ln}_with_para.jsonl.tar.gz",
),
]
_URLs = dict(_URLS_LIST)
class ExamsConfig(datasets.BuilderConfig):
def __init__(self, lang, with_para, **kwargs):
super(ExamsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.lang = lang
self.with_para = "_with_para" if with_para else ""
class Exams(datasets.GeneratorBasedBuilder):
"""Exams dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG_CLASS = ExamsConfig
BUILDER_CONFIGS = [
ExamsConfig(
lang="",
with_para=False,
name="alignments",
description="loads the alignment between question IDs across languages",
),
ExamsConfig(
lang="all",
with_para=False,
name="multilingual",
description="Loads the unified multilingual train/dev/test split",
),
ExamsConfig(
lang="all",
with_para=True,
name="multilingual_with_para",
description="Loads the unified multilingual train/dev/test split with Wikipedia support paragraphs",
),
ExamsConfig(
lang="all", with_para=False, name="crosslingual_test", description="Loads crosslingual test set only"
),
ExamsConfig(
lang="all",
with_para=True,
name="crosslingual_with_para_test",
description="Loads crosslingual test set only with Wikipedia support paragraphs",
),
]
for ln in _CROSS_LANGUAGES:
BUILDER_CONFIGS += [
ExamsConfig(
lang=ln,
with_para=False,
name=f"crosslingual_{ln}",
description=f"Loads crosslingual train and dev set for {ln}",
),
ExamsConfig(
lang=ln,
with_para=True,
name=f"crosslingual_with_para_{ln}",
description=f"Loads crosslingual train and dev set for {ln} with Wikipedia support paragraphs",
),
]
DEFAULT_CONFIG_NAME = (
"multilingual_with_para" # It's not mandatory to have a default configuration. Just use one if it make sense.
)
def _info(self):
if self.config.name == "alignments": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"source_id": datasets.Value("string"),
"target_id_list": 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(
{
"id": datasets.Value("string"),
"question": {
"stem": datasets.Value("string"),
"choices": datasets.Sequence(
{
"text": datasets.Value("string"),
"label": datasets.Value("string"),
"para": datasets.Value("string"),
}
),
},
"answerKey": datasets.Value("string"),
"info": {
"grade": datasets.Value("int32"),
"subject": datasets.Value("string"),
"language": datasets.Value("string"),
},
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
archives = dl_manager.download(_URLs)
if self.config.name == "alignments":
return [
datasets.SplitGenerator(
name="full",
gen_kwargs={"filepath": archives["alignments"]},
),
]
elif self.config.name in ["multilingual", "multilingual_with_para"]:
return [
datasets.SplitGenerator(
name=spl_enum,
gen_kwargs={
"filepath": f"{spl}{self.config.with_para}.jsonl",
"files": dl_manager.iter_archive(archives[f"{self.config.name}_{spl}"]),
},
)
for spl, spl_enum in [
("train", datasets.Split.TRAIN),
("dev", datasets.Split.VALIDATION),
("test", datasets.Split.TEST),
]
]
elif self.config.name in ["crosslingual_test", "crosslingual_with_para_test"]:
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": f"test{self.config.with_para}.jsonl",
"files": dl_manager.iter_archive(archives[self.config.name]),
},
),
]
else:
return [
datasets.SplitGenerator(
name=spl_enum,
gen_kwargs={
"filepath": f"{spl}_{self.config.lang}{self.config.with_para}.jsonl",
"files": dl_manager.iter_archive(archives[f"{self.config.name}_{spl}"]),
},
)
for spl, spl_enum in [
("train", datasets.Split.TRAIN),
("dev", datasets.Split.VALIDATION),
]
]
def _generate_examples(self, filepath, files=None):
if self.config.name == "alignments":
with open(filepath, encoding="utf-8") as f:
for id_, line in enumerate(f):
line_dict = json.loads(line.strip())
in_id, out_list = list(line_dict.items())[0]
yield id_, {"source_id": in_id, "target_id_list": out_list}
else:
for path, f in files:
if path == filepath:
for id_, line in enumerate(f):
line_dict = json.loads(line.strip())
for choice in line_dict["question"]["choices"]:
choice["para"] = choice.get("para", "")
yield id_, {
"id": line_dict["id"],
"question": line_dict["question"],
"answerKey": line_dict["answerKey"],
"info": line_dict["info"],
}
break