File size: 3,379 Bytes
5732810
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# 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 os
import json

import datasets

_LICENSE = "MIT License"

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_DATA_URL = (
    "https://raw.githubusercontent.com/yilunzhao/RobuT/main/robut_data.zip"
)


class KnowledgeMath(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="main",
        )
    ]

    DEFAULT_CONFIG_NAME = (
        "main"  # It's not mandatory to have a default configuration. Just use one if it make sense.
    )

    def _info(self):
        features = datasets.Features(
            {
                "question_id": datasets.Value("string"),
                "question": datasets.Value("string"),
                "tables": datasets.features.Sequence(datasets.Value("string")),
                "topic": datasets.Value("string"),
                "ground_truth": datasets.Value("float64"),
                "python_solution": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            features=features, 
            homepage=_HOMEPAGE,
        )

    def _split_generators(self, dl_manager):
        validation_path = datasets.DownloadManager.download("https://huggingface.co/datasets/yale-nlp/KnowledgeMath/raw/main/validation.json")
        test_path = datasets.DownloadManager.download("https://huggingface.co/datasets/yale-nlp/KnowledgeMath/raw/main/test.json")
        return [
            datasets.SplitGenerator(
                name="validation",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": validation_path
                },
            ),
            datasets.SplitGenerator(
                name="test",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": test_path
                },
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        qa_data = json.load(open(filepath))
        for idx, example in enumerate(qa_data):
            yield idx, {
                "question_id": example["question_id"],
                "question": example["question"],
                "tables": example["tables"],
                "topic": example["topic"],
                "ground_truth": example["ground_truth"],
                "python_solution": example["python_solution"],
            }