Delete loading script

mlqa.py

@@ -1,206 +0,0 @@
-"""TODO(mlqa): Add a description here."""
-
-
-import json
-import os
-
-import datasets
-
-
-# TODO(mlqa): BibTeX citation
-_CITATION = """\
-@article{lewis2019mlqa,
-  title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
-  author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
-  journal={arXiv preprint arXiv:1910.07475},
-  year={2019}
-}
-"""
-
-# TODO(mlqa):
-_DESCRIPTION = """\
-    MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
-    MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
-    German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
-    4 different languages on average.
-"""
-_URL = "https://dl.fbaipublicfiles.com/MLQA/"
-_DEV_TEST_URL = "MLQA_V1.zip"
-_TRANSLATE_TEST_URL = "mlqa-translate-test.tar.gz"
-_TRANSLATE_TRAIN_URL = "mlqa-translate-train.tar.gz"
-_LANG = ["ar", "de", "vi", "zh", "en", "es", "hi"]
-_TRANSLATE_LANG = ["ar", "de", "vi", "zh", "es", "hi"]
-
-
-class MlqaConfig(datasets.BuilderConfig):
-    def __init__(self, data_url, **kwargs):
-        """BuilderConfig for MLQA
-
-        Args:
-          data_url: `string`, url to the dataset
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(MlqaConfig, self).__init__(
-            version=datasets.Version(
-                "1.0.0",
-            ),
-            **kwargs,
-        )
-        self.data_url = data_url
-
-
-class Mlqa(datasets.GeneratorBasedBuilder):
-    """TODO(mlqa): Short description of my dataset."""
-
-    # TODO(mlqa): Set up version.
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = (
-        [
-            MlqaConfig(
-                name="mlqa-translate-train." + lang,
-                data_url=_URL + _TRANSLATE_TRAIN_URL,
-                description="Machine-translated data for Translate-train (SQuAD Train and Dev sets machine-translated into "
-                "Arabic, German, Hindi, Vietnamese, Simplified Chinese and Spanish)",
-            )
-            for lang in _LANG
-            if lang != "en"
-        ]
-        + [
-            MlqaConfig(
-                name="mlqa-translate-test." + lang,
-                data_url=_URL + _TRANSLATE_TEST_URL,
-                description="Machine-translated data for Translate-Test (MLQA-test set machine-translated into English) ",
-            )
-            for lang in _LANG
-            if lang != "en"
-        ]
-        + [
-            MlqaConfig(
-                name="mlqa." + lang1 + "." + lang2,
-                data_url=_URL + _DEV_TEST_URL,
-                description="development and test splits",
-            )
-            for lang1 in _LANG
-            for lang2 in _LANG
-        ]
-    )
-
-    def _info(self):
-        # TODO(mlqa): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {"answer_start": datasets.Value("int32"), "text": datasets.Value("string")}
-                    ),
-                    "id": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # 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="https://github.com/facebookresearch/MLQA",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(mlqa): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        if self.config.name.startswith("mlqa-translate-train"):
-            archive = dl_manager.download(self.config.data_url)
-            lang = self.config.name.split(".")[-1]
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": f"mlqa-translate-train/{lang}_squad-translate-train-train-v1.1.json",
-                        "files": dl_manager.iter_archive(archive),
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": f"mlqa-translate-train/{lang}_squad-translate-train-dev-v1.1.json",
-                        "files": dl_manager.iter_archive(archive),
-                    },
-                ),
-            ]
-
-        else:
-            if self.config.name.startswith("mlqa."):
-                dl_file = dl_manager.download_and_extract(self.config.data_url)
-                name = self.config.name.split(".")
-                l1, l2 = name[1:]
-                return [
-                    datasets.SplitGenerator(
-                        name=datasets.Split.TEST,
-                        # These kwargs will be passed to _generate_examples
-                        gen_kwargs={
-                            "filepath": os.path.join(
-                                os.path.join(dl_file, "MLQA_V1/test"),
-                                f"test-context-{l1}-question-{l2}.json",
-                            )
-                        },
-                    ),
-                    datasets.SplitGenerator(
-                        name=datasets.Split.VALIDATION,
-                        # These kwargs will be passed to _generate_examples
-                        gen_kwargs={
-                            "filepath": os.path.join(
-                                os.path.join(dl_file, "MLQA_V1/dev"), f"dev-context-{l1}-question-{l2}.json"
-                            )
-                        },
-                    ),
-                ]
-            else:
-                if self.config.name.startswith("mlqa-translate-test"):
-                    archive = dl_manager.download(self.config.data_url)
-                    lang = self.config.name.split(".")[-1]
-                    return [
-                        datasets.SplitGenerator(
-                            name=datasets.Split.TEST,
-                            # These kwargs will be passed to _generate_examples
-                            gen_kwargs={
-                                "filepath": f"mlqa-translate-test/translate-test-context-{lang}-question-{lang}.json",
-                                "files": dl_manager.iter_archive(archive),
-                            },
-                        ),
-                    ]
-
-    def _generate_examples(self, filepath, files=None):
-        """Yields examples."""
-        if self.config.name.startswith("mlqa-translate"):
-            for path, f in files:
-                if path == filepath:
-                    data = json.loads(f.read().decode("utf-8"))
-                    break
-        else:
-            with open(filepath, encoding="utf-8") as f:
-                data = json.load(f)
-        for examples in data["data"]:
-            for example in examples["paragraphs"]:
-                context = example["context"]
-                for qa in example["qas"]:
-                    question = qa["question"]
-                    id_ = qa["id"]
-                    answers = qa["answers"]
-                    answers_start = [answer["answer_start"] for answer in answers]
-                    answers_text = [answer["text"] for answer in answers]
-                    yield id_, {
-                        "context": context,
-                        "question": question,
-                        "answers": {"answer_start": answers_start, "text": answers_text},
-                        "id": id_,
-                    }