lc_quad2-sparqltotext.py

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/train.json",
    "valid": "json/valid.json",
    "test": "json/test.json"
}

_DESCRIPTION = """\
Special version of LCQuAD-2.0 for the SPARQL-to-Text task
"""


class LCQuAD20_SPARQL2Text(datasets.GeneratorBasedBuilder):
    """
    LCQuAD_2.0-SPARQL2Text: Special version of LCQuAD-2.0 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(
                {
                    "uid": datasets.Value('int32'),
                    "NNQT_question": datasets.Value('string'),
                    "paraphrased_question": datasets.Value('string'),
                    "question": datasets.Value('string'),
                    "simplified_query": datasets.Value('string'),
                    "sparql_dbpedia18": datasets.Value('string'),
                    "sparql_wikidata": datasets.Value('string'),
                    "answer": [datasets.Value("string")],
                    "solved_answer": [datasets.Value("string")],
                    "subgraph": datasets.Value('string'),
                    "template": datasets.Value('string'),
                    "template_id": datasets.Value('string'),
                    "template_index": datasets.Value('int32')
                }
            ),
            # 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=("simplified_query", "question"),
            # 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
        # download and extract URLs
        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 = {
                    "uid": -1,
                    "NNQT_question": "",
                    "paraphrased_question": "",
                    "question":  "",
                    "simplified_query": "",
                    "sparql_dbpedia18": "",
                    "sparql_wikidata": "",
                    "answer": [],
                    "solved_answer": [],
                    "subgraph": "",
                    "template": "",
                    "template_id": "",
                    "template_index": -1
            }
            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