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