Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
English
Size:
100K - 1M
ArXiv:
License:
Commit
•
f3178d9
1
Parent(s):
5effdb8
Convert dataset to Parquet (#6)
Browse files- Convert dataset to Parquet (df02e042b8345d44acdba68b3b268ecf497a9723)
- Delete loading script (241b331d6cea3479dcb46677f87cb3f3fd82008c)
- README.md +18 -8
- mrqa.py +0 -196
- plain_text/test-00000-of-00001.parquet +3 -0
- plain_text/train-00000-of-00009.parquet +3 -0
- plain_text/train-00001-of-00009.parquet +3 -0
- plain_text/train-00002-of-00009.parquet +3 -0
- plain_text/train-00003-of-00009.parquet +3 -0
- plain_text/train-00004-of-00009.parquet +3 -0
- plain_text/train-00005-of-00009.parquet +3 -0
- plain_text/train-00006-of-00009.parquet +3 -0
- plain_text/train-00007-of-00009.parquet +3 -0
- plain_text/train-00008-of-00009.parquet +3 -0
- plain_text/validation-00000-of-00001.parquet +3 -0
README.md
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@@ -26,6 +26,7 @@ task_ids:
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paperswithcode_id: mrqa-2019
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pretty_name: MRQA 2019
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dataset_info:
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features:
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- name: subset
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dtype: string
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dtype: int32
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- name: answers
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sequence: string
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config_name: plain_text
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splits:
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- name: train
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num_bytes:
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num_examples: 516819
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- name: test
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num_bytes: 57712177
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num_examples: 9633
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- name: validation
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num_bytes:
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num_examples: 58221
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---
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# Dataset Card for MRQA 2019
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paperswithcode_id: mrqa-2019
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pretty_name: MRQA 2019
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dataset_info:
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+
config_name: plain_text
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features:
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- name: subset
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dtype: string
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dtype: int32
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- name: answers
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sequence: string
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splits:
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- name: train
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num_bytes: 4090677713
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num_examples: 516819
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- name: validation
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num_bytes: 484106546
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num_examples: 58221
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- name: test
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num_bytes: 57712097
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num_examples: 9633
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download_size: 1679161250
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dataset_size: 4632496356
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configs:
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- config_name: plain_text
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data_files:
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- split: train
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path: plain_text/train-*
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- split: validation
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path: plain_text/validation-*
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- split: test
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path: plain_text/test-*
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default: true
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---
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# Dataset Card for MRQA 2019
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mrqa.py
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-
# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MRQA 2019 Shared task dataset."""
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-
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-
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import json
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-
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import datasets
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-
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-
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_CITATION = """\
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@inproceedings{fisch2019mrqa,
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title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
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author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
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booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP},
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year={2019},
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}
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"""
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-
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_DESCRIPTION = """\
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The MRQA 2019 Shared Task focuses on generalization in question answering.
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An effective question answering system should do more than merely
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interpolate from the training set to answer test examples drawn
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from the same distribution: it should also be able to extrapolate
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to out-of-distribution examples — a significantly harder challenge.
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-
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The dataset is a collection of 18 existing QA dataset (carefully selected
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subset of them) and converted to the same format (SQuAD format). Among
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these 18 datasets, six datasets were made available for training,
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six datasets were made available for development, and the final six
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for testing. The dataset is released as part of the MRQA 2019 Shared Task.
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"""
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-
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_HOMEPAGE = "https://mrqa.github.io/2019/shared.html"
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-
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_LICENSE = "Unknwon"
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-
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_URLs = {
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# Train sub-datasets
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"train+SQuAD": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SQuAD.jsonl.gz",
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"train+NewsQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NewsQA.jsonl.gz",
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"train+TriviaQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/TriviaQA-web.jsonl.gz",
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"train+SearchQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SearchQA.jsonl.gz",
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"train+HotpotQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/HotpotQA.jsonl.gz",
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"train+NaturalQuestions": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NaturalQuestionsShort.jsonl.gz",
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# Validation sub-datasets
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"validation+SQuAD": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/SQuAD.jsonl.gz",
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"validation+NewsQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/NewsQA.jsonl.gz",
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"validation+TriviaQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/TriviaQA-web.jsonl.gz",
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"validation+SearchQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/SearchQA.jsonl.gz",
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"validation+HotpotQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/HotpotQA.jsonl.gz",
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"validation+NaturalQuestions": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/NaturalQuestionsShort.jsonl.gz",
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# Test sub-datasets
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"test+BioASQ": "http://participants-area.bioasq.org/MRQA2019/", # BioASQ.jsonl.gz
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"test+DROP": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/DROP.jsonl.gz",
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"test+DuoRC": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/DuoRC.ParaphraseRC.jsonl.gz",
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"test+RACE": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/RACE.jsonl.gz",
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"test+RelationExtraction": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/RelationExtraction.jsonl.gz",
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"test+TextbookQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/TextbookQA.jsonl.gz",
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}
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class Mrqa(datasets.GeneratorBasedBuilder):
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"""MRQA 2019 Shared task dataset."""
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VERSION = datasets.Version("1.1.0")
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-
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="plain_text", description="Plain text", version=VERSION),
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]
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-
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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# Format is derived from https://github.com/mrqa/MRQA-Shared-Task-2019#mrqa-format
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features=datasets.Features(
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{
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"subset": datasets.Value("string"),
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"context": datasets.Value("string"),
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"context_tokens": datasets.Sequence(
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{
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"tokens": datasets.Value("string"),
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"offsets": datasets.Value("int32"),
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}
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),
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"qid": datasets.Value("string"),
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"question": datasets.Value("string"),
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"question_tokens": datasets.Sequence(
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{
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"tokens": datasets.Value("string"),
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"offsets": datasets.Value("int32"),
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}
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),
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"detected_answers": datasets.Sequence(
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{
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"text": datasets.Value("string"),
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"char_spans": datasets.Sequence(
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{
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"start": datasets.Value("int32"),
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"end": datasets.Value("int32"),
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}
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),
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"token_spans": datasets.Sequence(
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{
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"start": datasets.Value("int32"),
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"end": datasets.Value("int32"),
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}
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),
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}
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),
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"answers": datasets.Sequence(datasets.Value("string")),
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}
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),
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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data_dir = dl_manager.download_and_extract(_URLs)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepaths_dict": data_dir,
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepaths_dict": data_dir,
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"split": "test",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"filepaths_dict": data_dir,
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"split": "validation",
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},
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),
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]
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def _generate_examples(self, filepaths_dict, split):
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"""Yields examples."""
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for source, filepath in filepaths_dict.items():
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if split not in source:
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continue
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with open(filepath, encoding="utf-8") as f:
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header = next(f)
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subset = json.loads(header)["header"]["dataset"]
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for row in f:
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paragraph = json.loads(row)
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context = paragraph["context"].strip()
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context_tokens = [{"tokens": t[0], "offsets": t[1]} for t in paragraph["context_tokens"]]
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for qa in paragraph["qas"]:
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qid = qa["qid"]
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question = qa["question"].strip()
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question_tokens = [{"tokens": t[0], "offsets": t[1]} for t in qa["question_tokens"]]
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detected_answers = []
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for detect_ans in qa["detected_answers"]:
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detected_answers.append(
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{
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"text": detect_ans["text"].strip(),
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"char_spans": [{"start": t[0], "end": t[1]} for t in detect_ans["char_spans"]],
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"token_spans": [{"start": t[0], "end": t[1]} for t in detect_ans["token_spans"]],
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}
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)
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answers = qa["answers"]
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yield f"{source}_{qid}", {
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"subset": subset,
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"context": context,
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"context_tokens": context_tokens,
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"qid": qid,
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"question": question,
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"question_tokens": question_tokens,
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"detected_answers": detected_answers,
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"answers": answers,
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}
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plain_text/test-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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oid sha256:f99b099a4701074612b2b98bb3c937b4e09cc72411d9a8915c6ec51186bbb5a2
|
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size 15483098
|
plain_text/train-00000-of-00009.parquet
ADDED
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version https://git-lfs.github.com/spec/v1
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size 40937968
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plain_text/train-00001-of-00009.parquet
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 83288515
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plain_text/train-00002-of-00009.parquet
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 168678531
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plain_text/train-00003-of-00009.parquet
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 330623073
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plain_text/train-00004-of-00009.parquet
ADDED
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version https://git-lfs.github.com/spec/v1
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size 313450289
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plain_text/train-00005-of-00009.parquet
ADDED
@@ -0,0 +1,3 @@
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