Delete _numericnlg.py

_numericnlg.py

@@ -1,88 +0,0 @@
-#!/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")