feat: Add builder script

scandiqa.py

@@ -1 +0,0 @@
-src/scandi_qa/scandiqa.py

scandiqa.py

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+# Copyright 2022 The HuggingFace Datasets Authors and Dan Saattrup Nielsen.
+#
+# 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.
+"""Python build script for the ScandiQA dataset."""
+
+
+import json
+from pathlib import Path
+from typing import List
+
+from datasets import Version
+from datasets.builder import BuilderConfig, GeneratorBasedBuilder
+from datasets.download import DownloadManager
+from datasets.features import Features, Value
+from datasets.info import DatasetInfo
+from datasets.splits import Split, SplitGenerator
+
+_DESCRIPTION = """
+ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish
+languages. All samples come from the Natural Questions (NQ) dataset, which is a large
+question answering dataset from Google searches. The Scandinavian questions and answers
+come from the MKQA dataset, where 10,000 NQ samples were manually translated into,
+among others, Danish, Norwegian, and Swedish. However, this did not include a
+translated context, hindering the training of extractive question answering models.
+
+We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long
+answers" from the NQ dataset, being the paragraph in which the answer was found, or
+otherwise we extract the context by locating the paragraphs which have the largest
+cosine similarity to the question, and which contains the desired answer.
+
+Further, many answers in the MKQA dataset were "language normalised": for instance, all
+date answers were converted to the format "YYYY-MM-DD", meaning that in most cases
+these answers are not appearing in any paragraphs. We solve this by extending the MKQA
+answers with plausible "answer candidates", being slight perturbations or translations
+of the answer.
+
+With the contexts extracted, we translated these to Danish, Swedish and Norwegian using
+the DeepL translation service for Danish and Swedish, and the Google Translation
+service for Norwegian. After translation we ensured that the Scandinavian answers do
+indeed occur in the translated contexts.
+
+As we are filtering the MKQA samples at both the "merging stage" and the "translation
+stage", we are not able to fully convert the 10,000 samples to the Scandinavian
+languages, and instead get roughly 8,000 samples per language. These have further been
+split into a training, validation and test split, with the former two containing
+roughly 750 samples. The splits have been created in such a way that the proportion of
+samples without an answer is roughly the same in each split.
+"""
+
+_HOMEPAGE = "https://huggingface.co/alexandrainst/scandiqa"
+_LICENSE = "CC BY 4.0"
+_URLS = {
+    "da": [
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/da/train.jsonl",
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/da/val.jsonl",
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/da/test.jsonl",
+    ],
+    "sv": [
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/sv/train.jsonl",
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/sv/val.jsonl",
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/sv/test.jsonl",
+    ],
+    "no": [
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/no/train.jsonl",
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/no/val.jsonl",
+        "https://huggingface.co/datasets/saattrupdan/scandiqa/resolve/main/data/no/test.jsonl",
+    ],
+}
+
+# _CITATION = """
+# @InProceedings{huggingface:dataset,
+# title = {ScandiQA: A Scandinavian Question Answering Dataset},
+# author={Dan Saattrup Nielsen},
+# year={2022}
+# }
+# """
+
+
+class ScandiQA(GeneratorBasedBuilder):
+    """Scandinavian question answering dataset."""
+
+    VERSION = Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        BuilderConfig(
+            name="da",
+            version=VERSION,
+            description="The Danish part of the ScandiQA dataset.",
+        ),
+        BuilderConfig(
+            name="sv",
+            version=VERSION,
+            description="The Swedish part of the ScandiQA dataset.",
+        ),
+        BuilderConfig(
+            name="no",
+            version=VERSION,
+            description="The Norwegian part of the ScandiQA dataset.",
+        ),
+    ]
+
+    def _info(self) -> DatasetInfo:
+        features = Features(
+            {
+                "example_id": Value("int64"),
+                "question": Value("string"),
+                "answer": Value("string"),
+                "answer_start": Value("int64"),
+                "context": Value("string"),
+                "answer_en": Value("string"),
+                "answer_start_en": Value("int64"),
+                "context_en": Value("string"),
+                "title_en": Value("string"),
+            }
+        )
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            # citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> List[SplitGenerator]:
+        urls = _URLS[self.config.name]
+        downloaded_files = dl_manager.download_and_extract(urls)
+        return [
+            SplitGenerator(
+                name=str(Split.TRAIN),
+                gen_kwargs=dict(
+                    filepath=downloaded_files[0],
+                    split="train",
+                ),
+            ),
+            SplitGenerator(
+                name=str(Split.VALIDATION),
+                gen_kwargs=dict(
+                    filepath=downloaded_files[1],
+                    split="val",
+                ),
+            ),
+            SplitGenerator(
+                name=str(Split.TEST),
+                gen_kwargs=dict(filepath=downloaded_files[2], split="test"),
+            ),
+        ]
+
+    def _generate_examples(self, filepath: str, split):
+        with Path(filepath).open(encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, data