Inflate JSON dataset

.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+json/annotated_wd_data_test.json filter=lfs diff=lfs merge=lfs -text
+json/annotated_wd_data_test_answerable.json filter=lfs diff=lfs merge=lfs -text
+json/annotated_wd_data_train.json filter=lfs diff=lfs merge=lfs -text
+json/annotated_wd_data_train_answerable.json filter=lfs diff=lfs merge=lfs -text
+json/annotated_wd_data_valid.json filter=lfs diff=lfs merge=lfs -text
+json/annotated_wd_data_valid_answerable.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+---
+
+# Dataset Card for SimpleQuestions-SPARQLtoText 
+

json/annotated_wd_data_test.json

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json/annotated_wd_data_train.json

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json/annotated_wd_data_train_answerable.json

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json/annotated_wd_data_valid_answerable.json

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simplequestions-sparqltotext.py

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+import os
+import zipfile
+import json
+import base64
+
+import datasets
+
+try:
+    import gitlab
+except ImportError:
+    print("ERROR: To be able to retrieve this dataset you need to install the `python-gitlab` package")
+
+_CITATION = """\
+    @inproceedings{lecorve2022sparql2text,
+        title={Coqar: Question rewriting on coqa},
+        author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.},
+        journal={Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)},
+        year={2022}
+    }
+"""
+
+_HOMEPAGE = ""
+
+_URLS = {
+    "train": "json/annotated_wd_data_train.json",
+    "valid": "json/annotated_wd_data_valid.json",
+    "test": "json/annotated_wd_data_test.json"
+}
+
+_DESCRIPTION = """\
+SimpleQuestions-SPARQL2Text: Special version of SimpleQuestions with SPARQL queries formatted for the SPARQL-to-Text task
+"""
+
+
+class SimpleQuestions_SPARQL2Text(datasets.GeneratorBasedBuilder):
+    """
+    SimpleQuestions-SPARQL2Text: Special version of SimpleQuestions with
+    SPARQL queries formatted for the SPARQL-to-Text task
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "original_nl_question": datasets.Value('string'),
+                    "recased_nl_question": datasets.Value('string'),
+                    "sparql_query": datasets.Value('string'),
+                    "verbalized_sparql_query": datasets.Value('string'),
+                    "nl_subject": datasets.Value('string'),
+                    "nl_property": datasets.Value('string'),
+                    "nl_object": datasets.Value('string'),
+                    "nl_answer": datasets.Value('string'),
+                    "rdf_subject": datasets.Value('string'),
+                    "rdf_property": datasets.Value('string'),
+                    "rdf_object": datasets.Value('string'),
+                    "rdf_answer": datasets.Value('string'),
+                    "rdf_target": 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=("recased_nl_question", "verbalized_sparql_query"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        paths = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": dl_manager.extract(paths['train']),
+                            "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": dl_manager.extract(paths['valid']),
+                            "split": "valid"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": dl_manager.extract(paths['test']),
+                            "split": "test"}
+            )
+        ]
+        
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        
+        def transform_sample(original_sample):
+            transformed_sample = {
+                "original_nl_question": "",
+                "recased_nl_question": "",
+                "sparql_query": "",
+                "verbalized_sparql_query": "",
+                "nl_subject": "",
+                "nl_property": "",
+                "nl_object": "",
+                "nl_answer": "",
+                "rdf_subject": "",
+                "rdf_property": "",
+                "rdf_object": "",
+                "rdf_answer": "",
+                "rdf_target": ""
+            }
+            transformed_sample.update(original_sample)
+            
+            return transformed_sample
+        
+        # Yields (key, example) tuples from the dataset
+        with open(filepath,'r') as f:
+            data = json.load(f)
+            key = 0
+            for it in data:
+                yield key, transform_sample(it)
+                key += 1