Delete _hitab.py

_hitab.py

@@ -1,83 +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 = """\
-@article{cheng2021hitab,
-  title={HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation},
-  author={Cheng, Zhoujun and Dong, Haoyu and Wang, Zhiruo and Jia, Ran and Guo, Jiaqi and Gao, Yan and Han, Shi and Lou, Jian-Guang and Zhang, Dongmei},
-  journal={arXiv preprint arXiv:2108.06712},
-  year={2021}
-}
-"""
-_DESCRIPTION = """\
-HiTab is a dataset for question answering and data-to-text over hierarchical tables. It contains 10,672 samples and 3,597 tables from statistical reports (StatCan, NSF) and Wikipedia (ToTTo). 98.1% of the tables in HiTab are with hierarchies.
-"""
-
-_URL = "https://github.com/microsoft/HiTab"
-_LICENSE = "C-UDA 1.0"
-
-class HiTab(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("2022.2.7")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                'id' : datasets.Value(dtype='string'),
-                'table_id' : datasets.Value(dtype='string'),
-                'table_source' : datasets.Value(dtype='string'),
-                'sentence_id' : datasets.Value(dtype='string'),
-                'sub_sentence_id' : datasets.Value(dtype='string'),
-                'sub_sentence' : datasets.Value(dtype='string'),
-                'question' : datasets.Value(dtype='string'),
-                'answer' : datasets.Value(dtype='large_string'),
-                'aggregation' : datasets.Value(dtype='large_string'),
-                'linked_cells' : datasets.Value(dtype='large_string'),
-                'answer_formulas' :  datasets.Value(dtype='large_string'),
-                'reference_cells_map' : datasets.Value(dtype='large_string'),
-                'table_content' : datasets.Value(dtype='large_string'),
-            }),
-            supervised_keys=None,
-            homepage="https://www.microsoft.com/en-us/research/publication/hitab-a-hierarchical-table-dataset-for-question-answering-and-natural-language-generation/",
-            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):
-        table_content = {}
-        data = []
-
-        for filename in glob.glob(os.path.join(filepath, "tables", "raw", "*.json")):
-            with open(filename) as f:
-                j = json.load(f)
-                table_name = os.path.basename(filename).rstrip(".json")
-                table_content[table_name] = j
-
-        with open(os.path.join(filepath, f"{split}_samples.jsonl")) as f:
-            for i, line in enumerate(f.readlines()):
-                j = json.loads(line)
-                data.append(j)
-
-        for example_idx, entry in enumerate(data):
-            entry["table_content"] = table_content.get(entry["table_id"])
-            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/hitab")