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xquadr.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses, TASK_TO_SCHEMA, SCHEMA_TO_FEATURES
+
+_CITATION = """\
+@article{,@inproceedings{roy-etal-2020-lareqa,
+    title = "{LAR}e{QA}: Language-Agnostic Answer Retrieval from a Multilingual Pool",
+    author = "Roy, Uma  and
+        Constant, Noah  and
+        Al-Rfou, Rami  and
+        Barua, Aditya  and
+        Phillips, Aaron  and
+        Yang, Yinfei",
+    editor = "Webber, Bonnie  and
+        Cohn, Trevor  and
+        He, Yulan  and
+        Liu, Yang",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.emnlp-main.477",
+    doi = "10.18653/v1/2020.emnlp-main.477",
+    pages = "5919--5930",
+}
+"""
+
+_DATASETNAME = "xquadr"
+
+_DESCRIPTION = """\
+XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive
+QA dataset) that is a part of the LAReQA benchmark. Like XQuAD, XQUAD-R is an
+11-way parallel dataset, where each question (out of around 1200) appears in 11
+different languages and has 11 parallel correct answers across the languages. It
+is designed so as to include parallel QA pairs across languages, allowing
+questions to be matched with answers from different languages. The span-tagging
+task in XQuAD is converted into a retrieval task by breaking up each contextual
+paragraph into sentences, and treating each sentence as a possible target
+answer. There are around 1000 candidate answers in each language.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/lareqa"
+
+_LANGUAGES = ["tha", "vie"]
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "tha": "https://github.com/google-research-datasets/lareqa/raw/master/xquad-r/th.json",
+    "vie": "https://github.com/google-research-datasets/lareqa/raw/master/xquad-r/vi.json",
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING_RETRIEVAL]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # qa
+
+_SOURCE_VERSION = "1.1.0"  # inside the dataset
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class XquadRDataset(datasets.GeneratorBasedBuilder):
+    """A retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset)"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset in _LANGUAGES:
+        BUILDER_CONFIGS += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} {subset} source schema",
+                schema="source",
+                subset_id=subset,
+            ),
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} {subset} SEACrowd schema",
+                schema=_SEACROWD_SCHEMA,
+                subset_id=subset,
+            ),
+        ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{_LANGUAGES[0]}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "paragraphs": datasets.Sequence(
+                        {
+                            "context": datasets.Value("string"),
+                            "qas": datasets.Sequence(
+                                {
+                                    "answers": datasets.Sequence(
+                                        {
+                                            "answer_start": datasets.Value("int32"),
+                                            "text": datasets.Value("string"),
+                                        }
+                                    ),
+                                    "id": datasets.Value("string"),
+                                    "question": datasets.Value("string"),
+                                }
+                            ),
+                            "sentence_breaks": datasets.Sequence(
+                                datasets.Sequence(datasets.Value("int32"))
+                            ),
+                            "sentences": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ),
+                    "title": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[
+                TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]
+            ]  # qa_features
+            features["meta"] = {
+                "title": datasets.Value("string"),
+                "answers_start": datasets.Sequence(datasets.Value("int32")),
+                "answers_text": datasets.Sequence(datasets.Value("string")),
+            }
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        url = _URLS[self.config.subset_id]
+        data_path = Path(dl_manager.download(url))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_path": data_path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_path: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(data_path, "r", encoding="utf-8") as file:
+            data = json.load(file)
+
+        key = 0
+        for example in data["data"]:
+
+            if self.config.schema == "source":
+                yield key, example
+                key += 1
+
+            elif self.config.schema == _SEACROWD_SCHEMA:
+                for paragraph in example["paragraphs"]:
+                    # get sentence breaks (sentences' string stop index)
+                    break_list = [breaks[1] for breaks in paragraph["sentence_breaks"]]
+
+                    for qa in paragraph["qas"]:
+                        # get answers' string start index
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+
+                        # retrieve answers' relevant sentence
+                        answers = []
+                        for start in answer_starts:
+                            for i, end in enumerate(break_list):
+                                if start < end:
+                                    answers.append(paragraph["sentences"][i])
+                                    break
+
+                        yield key, {
+                            "id": str(key),
+                            "question_id": qa["id"],
+                            # "document_id": None,
+                            "question": qa["question"],
+                            "type": "retrieval",
+                            "choices": [],  # escape multiple choice qa seacrowd test
+                            "context": paragraph["context"],
+                            "answer": answers,
+                            "meta": {
+                                "title": example["title"],
+                                "answers_start": answer_starts,
+                                "answers_text": [answer["text"] for answer in qa["answers"]],
+                            },
+                        }
+                        key += 1