Add main file

This file is modified from the original mlqa.py code to make the data similar to other QA datasets.

mlqa.py

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+"""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(
+                        {"text": datasets.Value("string"), "answer_start": datasets.Value("int32")}
+                    ),
+                    "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_,
+                    }