Upload _hitab.py

_hitab.py

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+#!/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")