Delete loading script

mintaka.py

@@ -1,177 +0,0 @@
-# coding=utf-8
-
-"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""
-
-import json
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = """\
-         Mintaka is a complex, natural, and multilingual dataset designed for experimenting with end-to-end
-         question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English,
-         annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian,
-         Japanese, Portuguese, and Spanish for a total of 180,000 samples.
-         Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, 
-         which were naturally elicited from crowd workers. 
-"""
-
-_CITATION = """\
-        @inproceedings{sen-etal-2022-mintaka,
-            title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
-            author = "Sen, Priyanka and Aji, Alham Fikri and Saffari, Amir",
-            booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
-            month = oct,
-            year = "2022",
-            address = "Gyeongju, Republic of Korea",
-            publisher = "International Committee on Computational Linguistics",
-            url = "https://aclanthology.org/2022.coling-1.138",
-            pages = "1604--1619"
-        }
-"""
-
-_LICENSE = """\
-Copyright Amazon.com Inc. or its affiliates.
-Attribution 4.0 International
-"""
-
-_TRAIN_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_train.json"
-_DEV_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_dev.json"
-_TEST_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_test.json"
-
-
-_LANGUAGES = ['en', 'ar', 'de', 'ja', 'hi', 'pt', 'es', 'it', 'fr']
-
-_ALL = "all"
-
-class Mintaka(datasets.GeneratorBasedBuilder):
-    """Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name = name,
-            version = datasets.Version("1.0.0"),
-            description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering for {name}",
-        ) for name in _LANGUAGES
-    ]
-
-    BUILDER_CONFIGS.append(datasets.BuilderConfig(
-        name = _ALL,
-        version = datasets.Version("1.0.0"),
-        description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
-    ))
-
-    DEFAULT_CONFIG_NAME = 'en'
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "lang": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answerText": datasets.Value("string"),
-                    "category": datasets.Value("string"),
-                    "complexityType": datasets.Value("string"),
-                    "questionEntity": [{
-                        "name": datasets.Value("string"),
-                        "entityType": datasets.Value("string"),
-                        "label": datasets.Value("string"),
-                        "mention": datasets.Value("string"),
-                        "span": [datasets.Value("int32")],
-                    }],
-                    "answerEntity": [{
-                        "name": datasets.Value("string"),
-                        "label": datasets.Value("string"),
-                    }]
-                },
-            ),
-            supervised_keys=None,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "file": dl_manager.download_and_extract(_TRAIN_URL),
-                    "lang": self.config.name,
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "file": dl_manager.download_and_extract(_DEV_URL),
-                    "lang": self.config.name,
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "file": dl_manager.download_and_extract(_TEST_URL),
-                    "lang": self.config.name,
-                }
-            ),
-        ]
-
-    def _generate_examples(self, file, lang):
-        if lang == _ALL:
-            langs = _LANGUAGES
-        else:
-            langs = [lang]
-
-        key_ = 0
-
-        logger.info("⏳ Generating examples from = %s", ", ".join(lang))
-
-        with open(file, encoding='utf-8') as json_file:
-            data = json.load(json_file)
-            for lang in langs:
-                for sample in data:
-                    questionEntity = [
-                        {
-                            "name": str(qe["name"]),
-                            "entityType": qe["entityType"],
-                            "label": qe["label"] if "label" in qe else "",
-                            # span only applies for English question
-                            "mention": qe["mention"] if lang == "en" else None,
-                            "span": qe["span"] if lang == "en" else [],
-                        } for qe in sample["questionEntity"]
-                    ]
-
-                    answers = []
-                    if sample['answer']["answerType"] == "entity" and sample['answer']['answer'] is not None:
-                        answers = sample['answer']['answer']
-                    elif sample['answer']["answerType"] == "numerical" and "supportingEnt" in sample["answer"]:
-                        answers = sample['answer']['supportingEnt']
-
-                    # helper to get language for the corresponding language
-                    def get_label(labels, lang):
-                        if lang in labels:
-                            return labels[lang]
-                        if 'en' in labels:
-                            return labels['en']
-                        return None
-
-                    answerEntity = [
-                        {
-                            "name": str(ae["name"]),
-                            "label": get_label(ae["label"], lang),
-                        } for ae in answers
-                    ]
-
-                    yield key_, {
-                        "id": sample["id"],
-                        "lang": lang,
-                        "question": sample["question"] if lang == 'en' else sample['translations'][lang],
-                        "answerText": sample["answer"]["mention"],
-                        "category": sample["category"],
-                        "complexityType": sample["complexityType"],
-                        "questionEntity": questionEntity,
-                        "answerEntity": answerEntity,
-                    }
-
-                    key_ += 1