| |
|
|
| """ |
| 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") |
|
|
