Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
English
Size:
100K - 1M
ArXiv:
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. | |
| """MRQA 2019 Shared task dataset.""" | |
| from __future__ import absolute_import, division, print_function | |
| import json | |
| import datasets | |
| _CITATION = """\ | |
| @inproceedings{fisch2019mrqa, | |
| title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension}, | |
| author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen}, | |
| booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP}, | |
| year={2019}, | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The MRQA 2019 Shared Task focuses on generalization in question answering. | |
| An effective question answering system should do more than merely | |
| interpolate from the training set to answer test examples drawn | |
| from the same distribution: it should also be able to extrapolate | |
| to out-of-distribution examples — a significantly harder challenge. | |
| The dataset is a collection of 18 existing QA dataset (carefully selected | |
| subset of them) and converted to the same format (SQuAD format). Among | |
| these 18 datasets, six datasets were made available for training, | |
| six datasets were made available for development, and the final six | |
| for testing. The dataset is released as part of the MRQA 2019 Shared Task. | |
| """ | |
| _HOMEPAGE = "https://mrqa.github.io/2019/shared.html" | |
| _LICENSE = "Unknwon" | |
| _URLs = { | |
| # Train sub-datasets | |
| "train+SQuAD": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SQuAD.jsonl.gz", | |
| "train+NewsQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NewsQA.jsonl.gz", | |
| "train+TriviaQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/TriviaQA-web.jsonl.gz", | |
| "train+SearchQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SearchQA.jsonl.gz", | |
| "train+HotpotQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/HotpotQA.jsonl.gz", | |
| "train+NaturalQuestions": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NaturalQuestionsShort.jsonl.gz", | |
| # Validation sub-datasets | |
| "validation+SQuAD": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/SQuAD.jsonl.gz", | |
| "validation+NewsQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/NewsQA.jsonl.gz", | |
| "validation+TriviaQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/TriviaQA-web.jsonl.gz", | |
| "validation+SearchQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/SearchQA.jsonl.gz", | |
| "validation+HotpotQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/HotpotQA.jsonl.gz", | |
| "validation+NaturalQuestions": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/NaturalQuestionsShort.jsonl.gz", | |
| # Test sub-datasets | |
| "test+BioASQ": "http://participants-area.bioasq.org/MRQA2019/", # BioASQ.jsonl.gz | |
| "test+DROP": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/DROP.jsonl.gz", | |
| "test+DuoRC": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/DuoRC.ParaphraseRC.jsonl.gz", | |
| "test+RACE": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/RACE.jsonl.gz", | |
| "test+RelationExtraction": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/RelationExtraction.jsonl.gz", | |
| "test+TextbookQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/TextbookQA.jsonl.gz", | |
| } | |
| class Mrqa(datasets.GeneratorBasedBuilder): | |
| """MRQA 2019 Shared task dataset.""" | |
| VERSION = datasets.Version("1.1.0") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig(name="plain_text", description="Plain text", version=VERSION), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| # Format is derived from https://github.com/mrqa/MRQA-Shared-Task-2019#mrqa-format | |
| features=datasets.Features( | |
| { | |
| "subset": datasets.Value("string"), | |
| "context": datasets.Value("string"), | |
| "context_tokens": datasets.Sequence( | |
| { | |
| "tokens": datasets.Value("string"), | |
| "offsets": datasets.Value("int32"), | |
| } | |
| ), | |
| "qid": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "question_tokens": datasets.Sequence( | |
| { | |
| "tokens": datasets.Value("string"), | |
| "offsets": datasets.Value("int32"), | |
| } | |
| ), | |
| "detected_answers": datasets.Sequence( | |
| { | |
| "text": datasets.Value("string"), | |
| "char_spans": datasets.Sequence( | |
| { | |
| "start": datasets.Value("int32"), | |
| "end": datasets.Value("int32"), | |
| } | |
| ), | |
| "token_spans": datasets.Sequence( | |
| { | |
| "start": datasets.Value("int32"), | |
| "end": datasets.Value("int32"), | |
| } | |
| ), | |
| } | |
| ), | |
| "answers": datasets.Sequence(datasets.Value("string")), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| data_dir = dl_manager.download_and_extract(_URLs) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepaths_dict": data_dir, | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepaths_dict": data_dir, | |
| "split": "test", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepaths_dict": data_dir, | |
| "split": "validation", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepaths_dict, split): | |
| """Yields examples.""" | |
| for source, filepath in filepaths_dict.items(): | |
| if split not in source: | |
| continue | |
| with open(filepath, encoding="utf-8") as f: | |
| header = next(f) | |
| subset = json.loads(header)["header"]["dataset"] | |
| for row in f: | |
| paragraph = json.loads(row) | |
| context = paragraph["context"].strip() | |
| context_tokens = [{"tokens": t[0], "offsets": t[1]} for t in paragraph["context_tokens"]] | |
| for qa in paragraph["qas"]: | |
| qid = qa["qid"] | |
| question = qa["question"].strip() | |
| question_tokens = [{"tokens": t[0], "offsets": t[1]} for t in qa["question_tokens"]] | |
| detected_answers = [] | |
| for detect_ans in qa["detected_answers"]: | |
| detected_answers.append( | |
| { | |
| "text": detect_ans["text"].strip(), | |
| "char_spans": [{"start": t[0], "end": t[1]} for t in detect_ans["char_spans"]], | |
| "token_spans": [{"start": t[0], "end": t[1]} for t in detect_ans["token_spans"]], | |
| } | |
| ) | |
| answers = qa["answers"] | |
| yield qid, { | |
| "subset": subset, | |
| "context": context, | |
| "context_tokens": context_tokens, | |
| "qid": qid, | |
| "question": question, | |
| "question_tokens": question_tokens, | |
| "detected_answers": detected_answers, | |
| "answers": answers, | |
| } | |