Mintaka first upload

mintaka.py

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+# 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 sample in data:
+                for lang in langs:
+                    questionEntity = [
+                        {
+                            "name": str(qe["name"]),
+                            "entityType": qe["entityType"],
+                            "label": qe["label"] if "label" in qe else "",
+                            "mention": qe["mention"],
+                            "span": qe["span"],
+                        } 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']
+
+                    def get_label(labels, lang):
+                        if lang in labels:
+                            return labels[lang]
+                        if 'en' in labels:
+                            return labels['en']
+                        return ""
+
+                    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

test_mintaka.py

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+from datasets import load_dataset
+
+source = "AmazonScience/mintaka"
+
+#dataset = load_dataset(source, "all", download_mode="force_redownload")
+dataset = load_dataset(source, "all")
+
+print(dataset)
+print(dataset["train"][0])
+print(dataset["train"][0:10]['question'])
+
+
+dataset = load_dataset(source, "en")
+dataset = load_dataset(source, "ar")
+
+