Upload _eventnarrative.py

_eventnarrative.py

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+#!/usr/bin/env python3
+
+"""
+The script used to load the dataset from the original source.
+"""
+
+import os
+from collections import defaultdict
+
+import json
+import datasets
+
+_CITATION = """\
+@inproceedings{colas2021eventnarrative,
+  title={EventNarrative: A Large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation},
+  author={Colas, Anthony and Sadeghian, Ali and Wang, Yue and Wang, Daisy Zhe},
+  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
+  year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+EventNarrative is a knowledge graph-to-text dataset from publicly available open-world knowledge graphs, focusing on event-centric data. 
+EventNarrative consists of approximately 230,000 graphs and their corresponding natural language text, 6 times larger than the current largest parallel dataset. 
+It makes use of a rich ontology and all of the KGs entities are linked to the text."""
+
+_URL = "https://www.kaggle.com/datasets/acolas1/eventnarration"
+_LICENSE = "CC BY 4.0"
+
+class EventNarrative(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "Event_Name": datasets.Value("string"),
+                "entity_ref_dict": datasets.Value("large_string"),
+                "keep_triples": datasets.Value("large_string"),
+                "narration": datasets.Value("large_string"),
+                "types": datasets.Value("string"),
+                "wikipediaLabel": datasets.Value("string"),
+            }),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split" : "train"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"split" : "dev"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split" : "test"}),
+        ]
+
+    def _generate_examples(self, split):
+        """Yields examples."""
+        id_ = 0
+    
+        with open(f"{split}_data.json") as f:
+            j = json.load(f)
+
+            for example in j:
+                e = { key : str(value) for key, value in example.items()}
+                id_ += 1
+                yield id_, e
+            
+
+
+if __name__ == '__main__':
+    dataset = datasets.load_dataset(__file__)
+
+    import pdb; pdb.set_trace()  # breakpoint ffb6df83 //
+
+    # dataset.push_to_hub("kasnerz/eventnarrative")