Delete loading script

qa_zre.py

@@ -1,99 +0,0 @@
-"""A dataset reducing relation extraction to simple reading comprehension questions"""
-
-import csv
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{levy-etal-2017-zero,
-    title = "Zero-Shot Relation Extraction via Reading Comprehension",
-    author = "Levy, Omer  and
-      Seo, Minjoon  and
-      Choi, Eunsol  and
-      Zettlemoyer, Luke",
-    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
-    month = aug,
-    year = "2017",
-    address = "Vancouver, Canada",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/K17-1034",
-    doi = "10.18653/v1/K17-1034",
-    pages = "333--342",
-}
-"""
-
-_DESCRIPTION = """\
-A dataset reducing relation extraction to simple reading comprehension questions
-"""
-
-_DATA_URL = "http://nlp.cs.washington.edu/zeroshot/relation_splits.tar.bz2"
-
-
-class QaZre(datasets.GeneratorBasedBuilder):
-    """QA-ZRE: Reducing relation extraction to simple reading comprehension questions"""
-
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "relation": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "subject": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(datasets.Value("string")),
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="http://nlp.cs.washington.edu/zeroshot",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        dl_dir = dl_manager.download_and_extract(_DATA_URL)
-        dl_dir = os.path.join(dl_dir, "relation_splits")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths": [os.path.join(dl_dir, "test." + str(i)) for i in range(10)],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepaths": [os.path.join(dl_dir, "dev." + str(i)) for i in range(10)],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": [os.path.join(dl_dir, "train." + str(i)) for i in range(10)],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepaths):
-        """Yields examples."""
-
-        for file_idx, filepath in enumerate(filepaths):
-            with open(filepath, encoding="utf-8") as f:
-                data = csv.reader(f, delimiter="\t")
-                for idx, row in enumerate(data):
-                    yield f"{file_idx}_{idx}", {
-                        "relation": row[0],
-                        "question": row[1],
-                        "subject": row[2],
-                        "context": row[3],
-                        "answers": row[4:],
-                    }