Commit
•
1664417
1
Parent(s):
870fda1
Convert dataset to Parquet (#4)
Browse files- Convert dataset to Parquet (e41544f1cce95e1e671a8c93438616372ad49fd4)
- Add ARC-Easy data files (4d4ed1b0a54f45df33cd8eb45c157cc00cdaf35e)
- Delete loading script (a47c900f11d268f8922aa5d672e71e8e2c58f27c)
- Delete legacy dataset_infos.json (0555bd897c85012514721df4e419709132bb00d4)
- ARC-Challenge/test-00000-of-00001.parquet +3 -0
- ARC-Challenge/train-00000-of-00001.parquet +3 -0
- ARC-Challenge/validation-00000-of-00001.parquet +3 -0
- ARC-Easy/test-00000-of-00001.parquet +3 -0
- ARC-Easy/train-00000-of-00001.parquet +3 -0
- ARC-Easy/validation-00000-of-00001.parquet +3 -0
- README.md +29 -13
- ai2_arc.py +0 -132
- dataset_infos.json +0 -1
ARC-Challenge/test-00000-of-00001.parquet
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size 203808
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ARC-Challenge/train-00000-of-00001.parquet
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ARC-Challenge/validation-00000-of-00001.parquet
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oid sha256:395a5c88d1580d69855fbaee9450270578df1ad5af6259771cd0a42c20e99f05
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size 55743
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ARC-Easy/test-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:4160597d618ae851c7eb04e281574f3f654776216ac6b6641588d64527b47177
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size 346257
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ARC-Easy/train-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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size 330598
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ARC-Easy/validation-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:ed890ff1e4cef7a7140d3a30dcea3ed2c9d467c6458f447ad9ef0176d8dcbb74
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size 86080
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README.md
CHANGED
@@ -5,8 +5,6 @@ language_creators:
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- found
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language:
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- en
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-
language_bcp47:
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-
- en-US
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license:
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- cc-by-sa-4.0
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multilinguality:
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@@ -20,8 +18,9 @@ task_categories:
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task_ids:
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- open-domain-qa
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- multiple-choice-qa
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-
paperswithcode_id: null
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pretty_name: Ai2Arc
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dataset_info:
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- config_name: ARC-Challenge
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features:
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@@ -39,16 +38,16 @@ dataset_info:
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dtype: string
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splits:
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- name: train
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-
num_bytes:
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num_examples: 1119
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- name: test
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-
num_bytes:
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num_examples: 1172
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- name: validation
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-
num_bytes:
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num_examples: 299
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-
download_size:
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-
dataset_size:
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- config_name: ARC-Easy
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features:
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- name: id
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dtype: string
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splits:
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- name: train
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-
num_bytes:
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num_examples: 2251
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- name: test
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-
num_bytes:
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num_examples: 2376
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- name: validation
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-
num_bytes:
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num_examples: 570
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-
download_size:
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-
dataset_size:
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---
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# Dataset Card for "ai2_arc"
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- found
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language:
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- en
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license:
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- cc-by-sa-4.0
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multilinguality:
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task_ids:
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- open-domain-qa
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- multiple-choice-qa
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pretty_name: Ai2Arc
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+
language_bcp47:
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+
- en-US
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dataset_info:
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- config_name: ARC-Challenge
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features:
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dtype: string
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splits:
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- name: train
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+
num_bytes: 349760
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num_examples: 1119
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- name: test
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+
num_bytes: 375511
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num_examples: 1172
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- name: validation
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+
num_bytes: 96660
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num_examples: 299
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+
download_size: 449460
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+
dataset_size: 821931
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- config_name: ARC-Easy
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features:
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- name: id
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dtype: string
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splits:
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- name: train
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+
num_bytes: 619000
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num_examples: 2251
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- name: test
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+
num_bytes: 657514
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num_examples: 2376
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- name: validation
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+
num_bytes: 157394
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num_examples: 570
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+
download_size: 762935
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+
dataset_size: 1433908
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+
configs:
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+
- config_name: ARC-Challenge
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data_files:
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- split: train
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path: ARC-Challenge/train-*
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+
- split: test
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path: ARC-Challenge/test-*
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+
- split: validation
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path: ARC-Challenge/validation-*
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+
- config_name: ARC-Easy
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data_files:
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+
- split: train
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path: ARC-Easy/train-*
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+
- split: test
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+
path: ARC-Easy/test-*
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+
- split: validation
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path: ARC-Easy/validation-*
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---
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# Dataset Card for "ai2_arc"
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ai2_arc.py
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"""TODO(arc): Add a description here."""
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import json
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import os
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import datasets
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# TODO(ai2_arc): BibTeX citation
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_CITATION = """\
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@article{allenai:arc,
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author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
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Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
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title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
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journal = {arXiv:1803.05457v1},
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year = {2018},
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}
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"""
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-
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# TODO(ai2_arc):
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_DESCRIPTION = """\
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A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
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advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
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only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
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including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.
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"""
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_URL = "https://s3-us-west-2.amazonaws.com/ai2-website/data/ARC-V1-Feb2018.zip"
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class Ai2ArcConfig(datasets.BuilderConfig):
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"""BuilderConfig for Ai2ARC."""
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def __init__(self, **kwargs):
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"""BuilderConfig for Ai2Arc.
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-
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(Ai2ArcConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
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-
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class Ai2Arc(datasets.GeneratorBasedBuilder):
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"""TODO(arc): Short description of my dataset."""
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# TODO(arc): Set up version.
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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Ai2ArcConfig(
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name="ARC-Challenge",
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description="""\
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Challenge Set of 2590 “hard” questions (those that both a retrieval and a co-occurrence method fail to answer correctly)
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""",
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),
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Ai2ArcConfig(
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name="ARC-Easy",
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description="""\
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Easy Set of 5197 questions
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""",
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),
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]
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-
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def _info(self):
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# TODO(ai2_arc): Specifies the datasets.DatasetInfo object
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# datasets.features.FeatureConnectors
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features=datasets.Features(
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{
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"id": datasets.Value("string"),
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"question": datasets.Value("string"),
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"choices": datasets.features.Sequence(
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{"text": datasets.Value("string"), "label": datasets.Value("string")}
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),
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"answerKey": datasets.Value("string")
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# These are the features of your dataset like images, labels ...
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}
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),
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage="https://allenai.org/data/arc",
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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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# TODO(ai2_arc): Downloads the data and defines the splits
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# dl_manager is a datasets.download.DownloadManager that can be used to
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# download and extract URLs
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dl_dir = dl_manager.download_and_extract(_URL)
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data_dir = os.path.join(dl_dir, "ARC-V1-Feb2018-2")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Train.jsonl")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Test.jsonl")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Dev.jsonl")},
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),
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]
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-
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def _generate_examples(self, filepath):
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"""Yields examples."""
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# TODO(ai2_arc): Yields (key, example) tuples from the dataset
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with open(filepath, encoding="utf-8") as f:
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for row in f:
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data = json.loads(row)
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answerkey = data["answerKey"]
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id_ = data["id"]
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question = data["question"]["stem"]
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choices = data["question"]["choices"]
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text_choices = [choice["text"] for choice in choices]
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label_choices = [choice["label"] for choice in choices]
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yield id_, {
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"id": id_,
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"answerKey": answerkey,
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"question": question,
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"choices": {"text": text_choices, "label": label_choices},
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}
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dataset_infos.json
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{"ARC-Challenge": {"description": "A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in\n advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains\n only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also\n including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.\n", "citation": "@article{allenai:arc,\n author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and\n Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},\n title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},\n journal = {arXiv:1803.05457v1},\n year = {2018},\n}\n", "homepage": "https://allenai.org/data/arc", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "ai2_arc", "config_name": "ARC-Challenge", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 377740, "num_examples": 1172, "dataset_name": "ai2_arc"}, "train": {"name": "train", "num_bytes": 351888, "num_examples": 1119, "dataset_name": "ai2_arc"}, "validation": {"name": "validation", "num_bytes": 97254, "num_examples": 299, "dataset_name": "ai2_arc"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/ai2-website/data/ARC-V1-Feb2018.zip": {"num_bytes": 680841265, "checksum": "6d2d5ab50b2ceec6ba5f79c921be77cf2de712ea25a2b3f4fff3acc101cecfa0"}}, "download_size": 680841265, "dataset_size": 826882, "size_in_bytes": 681668147}, "ARC-Easy": {"description": "A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in\n advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains\n only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also\n including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.\n", "citation": "@article{allenai:arc,\n author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and\n Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},\n title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},\n journal = {arXiv:1803.05457v1},\n year = {2018},\n}\n", "homepage": "https://allenai.org/data/arc", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "ai2_arc", "config_name": "ARC-Easy", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 661997, "num_examples": 2376, "dataset_name": "ai2_arc"}, "train": {"name": "train", "num_bytes": 623254, "num_examples": 2251, "dataset_name": "ai2_arc"}, "validation": {"name": "validation", "num_bytes": 158498, "num_examples": 570, "dataset_name": "ai2_arc"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/ai2-website/data/ARC-V1-Feb2018.zip": {"num_bytes": 680841265, "checksum": "6d2d5ab50b2ceec6ba5f79c921be77cf2de712ea25a2b3f4fff3acc101cecfa0"}}, "download_size": 680841265, "dataset_size": 1443749, "size_in_bytes": 682285014}}
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