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#!/usr/bin/env python3
"""
The script used to load the dataset from the original source.
"""
import json
import datasets
import glob
import os
_CITATION = """\
@inproceedings{suadaa-etal-2021-towards,
title = "Towards Table-to-Text Generation with Numerical Reasoning",
author = "Suadaa, Lya Hulliyyatus and
Kamigaito, Hidetaka and
Funakoshi, Kotaro and
Okumura, Manabu and
Takamura, Hiroya",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.115",
doi = "10.18653/v1/2021.acl-long.115",
pages = "1451--1465"
}
"""
_DESCRIPTION = """\
NumericNLG is a dataset for table-totext generation focusing on numerical reasoning.
The dataset consists of textual descriptions of numerical tables from scientific papers.
"""
_URL = "https://github.com/titech-nlp/numeric-nlg"
_LICENSE = "CC BY-SA 4.0"
class NumericNLG(datasets.GeneratorBasedBuilder):
VERSION = "1.0.0"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"table_id_paper": datasets.Value(dtype='string'),
"caption": datasets.Value(dtype='string'),
"row_header_level" : datasets.Value(dtype='int32'),
"row_headers" : datasets.Value(dtype='large_string'),
"column_header_level": datasets.Value(dtype='int32'),
"column_headers" : datasets.Value(dtype='large_string'),
"contents" : datasets.Value(dtype='large_string'),
"metrics_loc" : datasets.Value(dtype='string'),
"metrics_type" : datasets.Value(dtype='large_string'),
"target_entity": datasets.Value(dtype='large_string'),
"table_html_clean": datasets.Value(dtype='large_string'),
"table_name": datasets.Value(dtype='string'),
"table_id": datasets.Value(dtype='string'),
"paper_id": datasets.Value(dtype='string'),
"page_no": datasets.Value(dtype='int32'),
"dir": datasets.Value(dtype='string'),
"valid": datasets.Value(dtype='int32'),
}),
supervised_keys=None,
homepage="https://github.com/titech-nlp/numeric-nlg",
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": "data", "split" : "train"}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": "data", "split" : "dev"}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": "data", "split" : "test"}),
]
def _generate_examples(self, filepath, split):
filename = split if split != "dev" else "val"
with open(os.path.join(filepath, f"table_{filename}.json")) as f:
j = json.load(f)
for example_idx, entry in enumerate(j):
yield example_idx, {key: str(value) for key, value in entry.items()}
if __name__ == '__main__':
dataset = datasets.load_dataset(__file__)
dataset.push_to_hub("kasnerz/numericnlg")
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