File size: 3,590 Bytes
34369d3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# 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.


import csv
import json

import datasets


_CITATION = """\
"""

_DESCRIPTION = """\
Legal Questions is a dataset for evaluating passage retrievers. 
"""

_HOMEPAGE = ""

_LICENSE = ""

_FEATURES_PAIRS = datasets.Features(
    {        
        "question_id": datasets.Value("int32"),
        "question": datasets.Value("string"),
        "passage_id": datasets.Value("string"),
        "answers": datasets.Value("string"),
        "passage_title": datasets.Value("string"),
        "passage_text": datasets.Value("string"),
        "relevant": datasets.Value("bool"),
    }
)

_FEATURES_PASSAGES = datasets.Features(
    {        
        "id": datasets.Value("string"),
        "title": datasets.Value("string"),
        "text": datasets.Value("string"),
    }
)

_URLS = {
    "pairs": {
        "test": ["data/test.csv"],
    },
    "passages": {
        "test": ["data/passages.jsonl"],
    },
}


class LegalQuestions(datasets.GeneratorBasedBuilder):
    """Legal Questions is a dataset for evaluating passage retrievers. """

    BUILDER_CONFIGS = list(map(lambda x: datasets.BuilderConfig(name=x, version=datasets.Version("1.0.0")), _URLS.keys()))
    DEFAULT_CONFIG_NAME = "pairs"

    def _info(self):
        if self.config.name == "pairs":
            features = _FEATURES_PAIRS
        else:
            features = _FEATURES_PASSAGES

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepaths": data_dir["test"],
                },
            ),
        ]

    @staticmethod
    def _parse_bool(text):
        if text == 'True':
            return True
        elif text == 'False':
            return False
        else:
            raise ValueError

    def _generate_examples(self, filepaths):
        if self.config.name == "pairs":
            boolean_features = [name for name, val in _FEATURES_PAIRS.items() if val.dtype == "bool"]

            for filepath in filepaths:
                with open(filepath, encoding="utf-8") as f:
                    data = csv.DictReader(f)
                    for i, row in enumerate(data):
                        for boolean_feature in boolean_features:
                            row[boolean_feature] = self._parse_bool(row[boolean_feature])
                        yield i, row
        else:
            for filepath in filepaths:
                with open(filepath, encoding="utf-8") as f:
                    for i, row in enumerate(f):
                        parsed_row = json.loads(row)
                        yield i, parsed_row