feat: Add symlink to scandiqa.py

scandiqa.py

@@ -1,166 +0,0 @@
-# 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.splits import SplitGenerator, Split
-from datasets.info import DatasetInfo
-from datasets.builder import GeneratorBasedBuilder, BuilderConfig
-from datasets.features import Features, Value
-from datasets import Version
-from datasets.download import DownloadManager
-
-
-_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

scandiqa.py

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