Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
"""TODO(mlqa): Add a description here.""" | |
import json | |
import os | |
import datasets | |
# TODO(mlqa): BibTeX citation | |
_CITATION = """\ | |
@article{lewis2019mlqa, | |
title={MLQA: Evaluating Cross-lingual Extractive Question Answering}, | |
author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, | |
journal={arXiv preprint arXiv:1910.07475}, | |
year={2019} | |
} | |
""" | |
# TODO(mlqa): | |
_DESCRIPTION = """\ | |
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. | |
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, | |
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between | |
4 different languages on average. | |
""" | |
_URL = "https://dl.fbaipublicfiles.com/MLQA/" | |
_DEV_TEST_URL = "MLQA_V1.zip" | |
_TRANSLATE_TEST_URL = "mlqa-translate-test.tar.gz" | |
_TRANSLATE_TRAIN_URL = "mlqa-translate-train.tar.gz" | |
_LANG = ["ar", "de", "vi", "zh", "en", "es", "hi"] | |
_TRANSLATE_LANG = ["ar", "de", "vi", "zh", "es", "hi"] | |
class MlqaConfig(datasets.BuilderConfig): | |
def __init__(self, data_url, **kwargs): | |
"""BuilderConfig for MLQA | |
Args: | |
data_url: `string`, url to the dataset | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(MlqaConfig, self).__init__( | |
version=datasets.Version( | |
"1.0.0", | |
), | |
**kwargs, | |
) | |
self.data_url = data_url | |
class Mlqa(datasets.GeneratorBasedBuilder): | |
"""TODO(mlqa): Short description of my dataset.""" | |
# TODO(mlqa): Set up version. | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = ( | |
[ | |
MlqaConfig( | |
name="mlqa-translate-train." + lang, | |
data_url=_URL + _TRANSLATE_TRAIN_URL, | |
description="Machine-translated data for Translate-train (SQuAD Train and Dev sets machine-translated into " | |
"Arabic, German, Hindi, Vietnamese, Simplified Chinese and Spanish)", | |
) | |
for lang in _LANG | |
if lang != "en" | |
] | |
+ [ | |
MlqaConfig( | |
name="mlqa-translate-test." + lang, | |
data_url=_URL + _TRANSLATE_TEST_URL, | |
description="Machine-translated data for Translate-Test (MLQA-test set machine-translated into English) ", | |
) | |
for lang in _LANG | |
if lang != "en" | |
] | |
+ [ | |
MlqaConfig( | |
name="mlqa." + lang1 + "." + lang2, | |
data_url=_URL + _DEV_TEST_URL, | |
description="development and test splits", | |
) | |
for lang1 in _LANG | |
for lang2 in _LANG | |
] | |
) | |
def _info(self): | |
# TODO(mlqa): Specifies the datasets.DatasetInfo object | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# datasets.features.FeatureConnectors | |
features=datasets.Features( | |
{ | |
"context": datasets.Value("string"), | |
"question": datasets.Value("string"), | |
"answers": datasets.features.Sequence( | |
{"answer_start": datasets.Value("int32"), "text": datasets.Value("string")} | |
), | |
"id": datasets.Value("string"), | |
# These are the features of your dataset like images, labels ... | |
} | |
), | |
# 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="https://github.com/facebookresearch/MLQA", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
# TODO(mlqa): Downloads the data and defines the splits | |
# dl_manager is a datasets.download.DownloadManager that can be used to | |
# download and extract URLs | |
if self.config.name.startswith("mlqa-translate-train"): | |
archive = dl_manager.download(self.config.data_url) | |
lang = self.config.name.split(".")[-1] | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": f"mlqa-translate-train/{lang}_squad-translate-train-train-v1.1.json", | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": f"mlqa-translate-train/{lang}_squad-translate-train-dev-v1.1.json", | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
] | |
else: | |
if self.config.name.startswith("mlqa."): | |
dl_file = dl_manager.download_and_extract(self.config.data_url) | |
name = self.config.name.split(".") | |
l1, l2 = name[1:] | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join( | |
os.path.join(dl_file, "MLQA_V1/test"), | |
f"test-context-{l1}-question-{l2}.json", | |
) | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join( | |
os.path.join(dl_file, "MLQA_V1/dev"), f"dev-context-{l1}-question-{l2}.json" | |
) | |
}, | |
), | |
] | |
else: | |
if self.config.name.startswith("mlqa-translate-test"): | |
archive = dl_manager.download(self.config.data_url) | |
lang = self.config.name.split(".")[-1] | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": f"mlqa-translate-test/translate-test-context-{lang}-question-{lang}.json", | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, files=None): | |
"""Yields examples.""" | |
if self.config.name.startswith("mlqa-translate"): | |
for path, f in files: | |
if path == filepath: | |
data = json.loads(f.read().decode("utf-8")) | |
break | |
else: | |
with open(filepath, encoding="utf-8") as f: | |
data = json.load(f) | |
for examples in data["data"]: | |
for example in examples["paragraphs"]: | |
context = example["context"] | |
for qa in example["qas"]: | |
question = qa["question"] | |
id_ = qa["id"] | |
answers = qa["answers"] | |
answers_start = [answer["answer_start"] for answer in answers] | |
answers_text = [answer["text"] for answer in answers] | |
yield id_, { | |
"context": context, | |
"question": question, | |
"answers": {"answer_start": answers_start, "text": answers_text}, | |
"id": id_, | |
} | |