Removed script + moved original JSON files

webnlgqa.py

@@ -1,199 +0,0 @@
-import os
-import zipfile
-import json
-import base64
-import sys
-import traceback
-
-import datasets
-
-_CITATION = """\
-    @inproceedings{lecorve2022sparql2text,
-        title={SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications},
-        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": "train.json",
-    "dev": "dev.json",
-    "test": "test.json",
-    "challenge": "challenge.json"
-}
-
-_DESCRIPTION = """\
-Augmented version of WebNLG v3.0 English with follow-up SPARQL queries with their associated answer(s). A small portion of it also contains natural language questions associated with the queries.
-"""
-
-class WebNLGQA(datasets.GeneratorBasedBuilder):
-    """
-    WebNLG-QA: Augmented version of WebNLG v3.0 English with follow-up SPARQL queries with their associated answer(s). A small portion of it also contains natural language questions associated with the queries.
-    """
-
-    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(
-                {
-                    "category": datasets.Value("string"),
-                    "size": datasets.Value("int32"),
-                    "id": datasets.Value("string"),
-                    "eid": datasets.Value("string"),
-                    "original_triple_sets": [
-                        {"subject": datasets.Value("string"),
-                         "property": datasets.Value("string"),
-                         "object": datasets.Value("string")}
-                    ],
-                    "modified_triple_sets": [
-                        {"subject": datasets.Value("string"),
-                         "property": datasets.Value("string"),
-                         "object": datasets.Value("string")}
-                    ],
-                    "shape": datasets.Value("string"),
-                    "shape_type": datasets.Value("string"),
-                    "lex": datasets.Sequence(
-                        {
-                            "comment": datasets.Value("string"),
-                            "lid": datasets.Value("string"),
-                            "text": datasets.Value("string"),
-                            "lang": datasets.Value("string"),
-                        }
-                    ),
-                    "test_category": datasets.Value("string"),
-                    "dbpedia_links": datasets.Sequence(datasets.Value("string")),
-                    "links": datasets.Sequence(datasets.Value("string")),
-                    "graph": [
-                        [datasets.Value("string")]
-                    ],
-                    "main_entity": datasets.Value("string"),
-                    "mappings": [
-                                    {
-                                        "modified": datasets.Value("string"),
-                                        "readable": datasets.Value("string"),
-                                        "graph": datasets.Value("string")
-                                    }
-                                ],
-                    "dialogue": [
-                            {
-                                "question": [ {
-                                  "source": datasets.Value("string"),
-                                  "text": datasets.Value("string")
-                                }],
-                                "graph_query": datasets.Value("string"),
-                                "readable_query": datasets.Value("string"),
-                                "graph_answer": [
-                                    datasets.Value("string")
-                                ],
-                                "readable_answer": [
-                                    datasets.Value("string")
-                                ],
-                                "type": [ 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=_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": paths['train'],
-                            "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": paths['dev'],
-                            "split": "dev"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": paths['test'],
-                            "split": "test"}
-            ),
-            datasets.SplitGenerator(
-                name="challenge",
-                gen_kwargs={"filepath": paths['challenge'],
-                            "split": "challenge"}
-            )
-        ]
-
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-
-        def transform_sample(original_sample):
-            transformed_sample = {
-                    "category": "",
-                    "size": -1,
-                    "id": "",
-                    "eid": "",
-                    "original_triple_sets": [],
-                    "modified_triple_sets": [],
-                    "shape": "",
-                    "shape_type": "",
-                    "lex": [],
-                    "test_category": "",
-                    "dbpedia_links": [],
-                    "links": [],
-                    "graph": [],
-                    "main_entity": "",
-                    "mappings": [],
-                    "dialogue": []
-                }
-
-            for (old_key, new_key) in [("modifiedtripleset", "modified_triple_sets"), ("originaltriplesets", "original_triple_sets"), ("dbpedialinks", "dbpedia_links"), ("lexicalisations", "lex"), ("xml_id", "eid")]:
-                original_sample[new_key] = original_sample[old_key]
-                del original_sample[old_key]
-
-            original_sample["original_triple_sets"] = original_sample["original_triple_sets"]["originaltripleset"][0]
-
-            for l in original_sample["lex"]:
-                l["lid"] = l["xml_id"]
-                del l["xml_id"]
-                l["text"] = l["lex"]
-                del l["lex"]
-
-            for turn in original_sample["dialogue"]:
-                if "question" in turn:
-                    old_format = turn["question"]
-                    new_format = []
-                    for source, text in old_format.items():
-                        new_format.append({"source": source, "text": text})
-                    turn["question"] = new_format
-
-
-            for k in transformed_sample:
-                if k in original_sample:
-                    transformed_sample[k] = original_sample[k]
-            # 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