Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 3,128 Bytes
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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{chen2020logical,
title={Logical Natural Language Generation from Open-Domain Tables},
author={Chen, Wenhu and Chen, Jianshu and Su, Yu and Chen, Zhiyu and Wang, William Yang},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
pages={7929--7942},
year={2020}
}
"""
_DESCRIPTION = """\
LogicNLG is a dataset for natural language generation from open-domain tables.
LogicNLG is based on TabFact (Chen et al., 2019), which is a table-based fact-checking dataset with rich logical inferences in the annotated statements.
"""
_URL = "https://github.com/wenhuchen/LogicNLG"
_LICENSE = "MIT"
class LogicNLG(datasets.GeneratorBasedBuilder):
VERSION = "1.0.0"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
'table': datasets.Value(dtype='large_string'),
"ref": datasets.Value(dtype='string'),
"linked_columns": datasets.Value(dtype='string'),
"title": datasets.Value(dtype='string'),
"template": datasets.Value(dtype='string'),
"table_id": datasets.Value(dtype='string'),
}),
supervised_keys=None,
homepage="https://wenhuchen.github.io/logicnlg.github.io/",
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"
data = []
with open(os.path.join(filepath, f"{filename}_lm.json")) as f:
j = json.load(f)
for i, (table_id, examples) in enumerate(j.items()):
table = []
with open(os.path.join(filepath, "all_csv", table_id)) as f:
for line in f.readlines():
table.append(line.rstrip("\n").split("#"))
for example in examples:
data.append({
"table": table,
"ref": example[0],
"linked_columns": example[1],
"title": example[2],
"template": example[3],
"table_id": table_id,
})
for example_idx, entry in enumerate(data):
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/logicnlg")
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