Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
#!/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") | |