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xquad.py

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+import json
+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 = """\
+@article{Artetxe:etal:2019,
+      author    = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
+      title     = {On the cross-lingual transferability of monolingual representations},
+      journal   = {CoRR},
+      volume    = {abs/1910.11856},
+      year      = {2019},
+      archivePrefix = {arXiv},
+      eprint    = {1910.11856}
+}
+"""
+
+_DATASETNAME = "xquad"
+
+_DESCRIPTION = """\
+XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance.
+The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 together (Rajpurkar et al., 2016)
+with their professional translations into ten languages in their original implementation: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi and two in this dataloader: Vietnamese & Thai
+"""
+
+_HOMEPAGE = "https://github.com/google-deepmind/xquad"
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_LOCAL = False
+_LANGUAGES = ["tha", "vie"]
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class XQuADDataset(datasets.GeneratorBasedBuilder):
+    """
+    XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance.
+    The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 together
+    with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi.
+    """
+
+    subsets = ["xquad.vi", "xquad.th"]
+
+    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 = "xquad.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]:
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN
+            )
+        ]
+
+    def _generate_examples(self) -> Tuple[int, Dict]:
+        name_split = self.config.name.split("_")
+        subset_name = name_split[0]
+        dataset = datasets.load_dataset(_DATASETNAME, subset_name)
+        
+        # Validation is the only subset name available for this dataset
+        for data in dataset['validation']:
+            if self.config.schema == "source":
+                yield data['id'], {
+                    "context": data['context'],
+                    "question": data['question'],
+                    "answers": {"answer_start": str(data['answers']['answer_start'][0]), "text": data['answers']['text'][0]},
+                    "id": data['id'],
+                }
+
+            elif self.config.schema == "seacrowd_qa":
+                yield data['id'], {
+                    "question_id": data['id'],
+                    "context": data['context'],
+                    "question": data['question'],
+                    "answer": {"answer_start": data['answers']['answer_start'][0], "text": data['answers']['text'][0]},
+                    "id": data['id'],
+                    "choices": [],
+                    "type": "",
+                    "document_id": data['id'],
+                    "meta": {},
+                }