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mlqa.py

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+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = r"""\
+@article{lewis2019mlqa,
+    author={Lewis, Patrick and O\{g}uz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
+    title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
+    journal={arXiv preprint arXiv:1910.07475},
+    year={2019}
+}
+"""
+
+_DATASETNAME = "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.
+"""
+
+_HOMEPAGE = "https://github.com/facebookresearch/MLQA"
+_LICENSE = Licenses.CC_BY_SA_3_0.value
+_LANGUAGES = ["vie"]
+_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"
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_LOCAL = False
+
+
+class MLQADataset(datasets.GeneratorBasedBuilder):
+    """
+    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.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    subsets = [
+        "mlqa-translate-test.vi",
+        "mlqa-translate-train.vi",
+        "mlqa.vi.ar",
+        "mlqa.vi.de",
+        "mlqa.vi.zh",
+        "mlqa.vi.en",
+        "mlqa.vi.es",
+        "mlqa.vi.hi",
+        "mlqa.vi.vi",
+        "mlqa.ar.vi",
+        "mlqa.de.vi",
+        "mlqa.zh.vi",
+        "mlqa.en.vi",
+        "mlqa.es.vi",
+        "mlqa.hi.vi",
+    ]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="{sub}_source".format(sub=subset),
+            version=datasets.Version(_SOURCE_VERSION),
+            description="{sub} source schema".format(sub=subset),
+            schema="source",
+            subset_id="{sub}".format(sub=subset),
+        )
+        for subset in subsets
+    ] + [
+        SEACrowdConfig(
+            name="{sub}_seacrowd_qa".format(sub=subset),
+            version=datasets.Version(_SEACROWD_VERSION),
+            description="{sub} SEACrowd schema".format(sub=subset),
+            schema="seacrowd_qa",
+            subset_id="{sub}".format(sub=subset),
+        )
+        for subset in subsets
+    ]
+
+    DEFAULT_CONFIG_NAME = "mlqa.vi.vi_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {"context": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.Features({"answer_start": [datasets.Value("int64")], "text": [datasets.Value("string")]}), "id": datasets.Value("string")}
+            )
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        name_split = self.config.name.split("_")
+        url = ""
+        data_path = ""
+
+        if name_split[0].startswith("mlqa-translate-train"):
+            config_name, lang = name_split[0].split(".")
+            url = _URL + _TRANSLATE_TRAIN_URL
+            data_path = dl_manager.download(url)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # Whatever you put in gen_kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": f"{config_name}/{lang}_squad-translate-train-train-v1.1.json",
+                        "files": dl_manager.iter_archive(data_path),
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": f"{config_name}/{lang}_squad-translate-train-dev-v1.1.json",
+                        "files": dl_manager.iter_archive(data_path),
+                        "split": "test",
+                    },
+                ),
+            ]
+
+        elif name_split[0].startswith("mlqa-translate-test"):
+            config_name, lang = name_split[0].split(".")
+            url = _URL + _TRANSLATE_TEST_URL
+            data_path = dl_manager.download(url)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": f"{config_name}/translate-test-context-{lang}-question-{lang}.json",
+                        "files": dl_manager.iter_archive(data_path),
+                        "split": "test",
+                    },
+                ),
+            ]
+
+        elif name_split[0].startswith("mlqa."):
+            url = _URL + _DEV_TEST_URL
+            data_path = dl_manager.download_and_extract(url)
+            ctx_lang, qst_lang = name_split[0].split(".")[1:]
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            os.path.join(data_path, "MLQA_V1/dev"),
+                            f"dev-context-{ctx_lang}-question-{qst_lang}.json",
+                        ),
+                        "split": "dev",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            os.path.join(data_path, "MLQA_V1/test"),
+                            f"test-context-{ctx_lang}-question-{qst_lang}.json",
+                        ),
+                        "split": "test",
+                    },
+                ),
+            ]
+        elif name_split[0] == "mlqa":
+            url = _URL + _DEV_TEST_URL
+            data_path = dl_manager.download_and_extract(url)
+            ctx_lang = qst_lang = "vi"
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            os.path.join(data_path, "MLQA_V1/dev"),
+                            f"dev-context-{ctx_lang}-question-{qst_lang}.json",
+                        ),
+                        "split": "dev",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            os.path.join(data_path, "MLQA_V1/test"),
+                            f"test-context-{ctx_lang}-question-{qst_lang}.json",
+                        ),
+                        "split": "test",
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath: Path, split: str, files=None) -> Tuple[int, Dict]:
+        is_config_ok = True
+        if self.config.name.startswith("mlqa-translate"):
+            for path, f in files:
+                if path == filepath:
+                    data = json.loads(f.read().decode("utf-8"))
+                    break
+
+        elif self.config.schema == "source" or self.config.schema == "seacrowd_qa":
+            with open(filepath, encoding="utf-8") as f:
+                data = json.load(f)
+        else:
+            is_config_ok = False
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+        if is_config_ok:
+            count = 0
+            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]
+
+                        if self.config.schema == "source":
+                            yield count, {
+                                "context": context,
+                                "question": question,
+                                "answers": {"answer_start": answers_start, "text": answers_text},
+                                "id": id_,
+                            }
+                            count += 1
+
+                        elif self.config.schema == "seacrowd_qa":
+                            yield count, {"question_id": id_, "context": context, "question": question, "answer": {"answer_start": answers_start[0], "text": answers_text[0]}, "id": id_, "choices": [], "type": "extractive", "document_id": count, "meta":{}}
+                            count += 1