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"""TODO(winogrande): Add a description here.""" | |
import json | |
import os | |
import datasets | |
# TODO(winogrande): BibTeX citation | |
_CITATION = """\ | |
@InProceedings{ai2:winogrande, | |
title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale}, | |
authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi | |
}, | |
year={2019} | |
} | |
""" | |
# TODO(winogrande): | |
_DESCRIPTION = """\ | |
WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern | |
2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a | |
fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires | |
commonsense reasoning. | |
""" | |
_URL = "https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip" | |
_FORMATS = ["xs", "s", "m", "l", "xl", "debiased"] | |
class WinograndeConfig(datasets.BuilderConfig): | |
"""BuilderConfig for Discofuse""" | |
def __init__(self, data_size, **kwargs): | |
""" | |
Args: | |
data_size: the format of the training set we want to use (xs, s, m, l, xl, debiased) | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(WinograndeConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs) | |
self.data_size = data_size | |
class Winogrande(datasets.GeneratorBasedBuilder): | |
"""TODO(winogrande): Short description of my dataset.""" | |
# TODO(winogrande): Set up version. | |
VERSION = datasets.Version("1.1.0") | |
BUILDER_CONFIGS = [ | |
WinograndeConfig(name="winogrande_" + data_size, description="AI2 dataset", data_size=data_size) | |
for data_size in _FORMATS | |
] | |
def _info(self): | |
# TODO(winogrande): 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( | |
{ | |
"sentence": datasets.Value("string"), | |
"option1": datasets.Value("string"), | |
"option2": datasets.Value("string"), | |
"answer": 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://leaderboard.allenai.org/winogrande/submissions/get-started", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
# TODO(winogrande): Downloads the data and defines the splits | |
# dl_manager is a datasets.download.DownloadManager that can be used to | |
# download and extract URLs | |
dl_dir = dl_manager.download_and_extract(_URL) | |
data_dir = os.path.join(dl_dir, "winogrande_1.1") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, f"train_{self.config.data_size}.jsonl"), | |
# 'labelpath': os.path.join(data_dir, 'train_{}-labels.lst'.format(self.config.data_size)), | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl"), "split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "dev.jsonl"), | |
# 'labelpath': os.path.join(data_dir, 'dev-labels.lst'), | |
"split": "dev", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""Yields examples.""" | |
# TODO(winogrande): Yields (key, example) tuples from the dataset | |
with open(filepath, encoding="utf-8") as f: | |
for id_, row in enumerate(f): | |
data = json.loads(row) | |
if split == "test": | |
yield id_, { | |
"sentence": data["sentence"], | |
"option1": data["option1"], | |
"option2": data["option2"], | |
"answer": "", | |
} | |
else: | |
yield id_, { | |
"sentence": data["sentence"], | |
"option1": data["option1"], | |
"option2": data["option2"], | |
"answer": data["answer"], | |
} | |
# def _generate_test_example(filepath, split, labelpath=None): | |
# with open(filepath, encoding="utf-8") as f: | |
# for id_, row in enumerate(f): | |
# data = json.loads(row) | |
# yield id_,{ | |
# 'sentence': data['sentence'], | |
# 'option1': data['option1'], | |
# 'option2': data['option2'], | |
# 'answer': None | |
# } | |