Hugging Face's logo

Datasets:
amanrangapur
/
Fin-Fact

Tasks:
Text Classification
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
finance
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
4
Fin-Fact
File size: 2,579 Bytes 
ac8e140
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
"""Fin-Fact dataset."""

import json
import datasets

_CITATION = """\
@misc{rangapur2023finfact,
      title={Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation}, 
      author={Aman Rangapur and Haoran Wang and Kai Shu},
      year={2023},
      eprint={2309.08793},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
"""

_DESCRIPTION = """\
Fin-Fact is a comprehensive dataset designed specifically for financial fact-checking and explanation generation. 
The dataset consists of 3121 claims spanning multiple financial sectors.
"""

_HOMEPAGE = "https://github.com/IIT-DM/Fin-Fact"
_LICENSE = "Apache 2.0"
_URL = "https://huggingface.co/datasets/amanrangapur/Fin-Fact/resolve/main/finfact.json"

class FinFact(datasets.GeneratorBasedBuilder):
    """Fin-Fact dataset for financial fact-checking and text generation."""

    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="generation",
            version=VERSION,
            description="The Fin-Fact dataset for financial fact-checking and text generation",
        ),
    ]

    DEFAULT_CONFIG_NAME = "generation"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "url": datasets.Value("string"),
                    "claim": datasets.Value("string"),
                    "author": datasets.Value("string"),
                    "posted": datasets.Value("string"),
                    "label": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": downloaded_file,
                },
            ),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as f:
            data = json.load(f)
            for id_, row in enumerate(data):
                yield id_, {
                    "url": row.get("url", ""),
                    "claim": row.get("claim", ""),
                    "author": row.get("author", ""),
                    "posted": row.get("posted", ""),
                    "label": row.get("label", ""),
                }