File size: 5,649 Bytes
a36bd1b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
76cbf96
a36bd1b
 
 
 
5b3e2a8
a36bd1b
 
 
 
 
76cbf96
a36bd1b
 
76cbf96
a36bd1b
 
 
 
 
 
 
 
 
76cbf96
 
a36bd1b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
40192e8
a36bd1b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
"""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
#               }