Upload _numericnlg.py

_numericnlg.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 = """\
+@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")