Delete _eventnarrative.py

_eventnarrative.py

@@ -1,77 +0,0 @@
-#!/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")