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feat: Add data, script and readme

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  1. .gitattributes +6 -54
  2. .gitignore +1 -0
  3. README.md +147 -1
  4. data/da.jsonl +3 -0
  5. data/fo.jsonl +3 -0
  6. data/is.jsonl +3 -0
  7. data/nb.jsonl +3 -0
  8. data/nn.jsonl +3 -0
  9. data/sv.jsonl +3 -0
  10. scandi_wiki.py +126 -0
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- *.zst filter=lfs diff=lfs merge=lfs -text
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- *tfevents* filter=lfs diff=lfs merge=lfs -text
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- # Audio files - uncompressed
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- *.jpeg filter=lfs diff=lfs merge=lfs -text
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- *.webp filter=lfs diff=lfs merge=lfs -text
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+ data/nn.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/sv.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/da.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/fo.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/is.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/nb.jsonl filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
.gitignore ADDED
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+ .venv
README.md CHANGED
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  ---
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- license: cc-by-sa-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ pretty_name: ScandiQA
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+ language:
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+ - da
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+ - sv
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+ - no
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+ license:
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+ - cc-by-sa-4.0
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+ multilinguality:
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+ - multilingual
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+ size_categories:
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+ - 1K<n<10K
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+ source_datasets:
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+ - mkqa
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+ - natural_questions
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+ task_categories:
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+ - question-answering
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+ task_ids:
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+ - extractive-qa
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  ---
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+
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+ # Dataset Card for ScandiQA
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+
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+ ## Dataset Description
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+
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+ - **Repository:** <https://github.com/alexandrainst/scandi-qa>
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+ - **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
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+ - **Size of downloaded dataset files:** 69 MB
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+ - **Size of the generated dataset:** 67 MB
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+ - **Total amount of disk used:** 136 MB
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+
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+ ### Dataset Summary
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+
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+ ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish
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+ languages. All samples come from the Natural Questions (NQ) dataset, which is a large
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+ question answering dataset from Google searches. The Scandinavian questions and answers
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+ come from the MKQA dataset, where 10,000 NQ samples were manually translated into,
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+ among others, Danish, Norwegian, and Swedish. However, this did not include a
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+ translated context, hindering the training of extractive question answering models.
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+
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+ We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long
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+ answers" from the NQ dataset, being the paragraph in which the answer was found, or
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+ otherwise we extract the context by locating the paragraphs which have the largest
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+ cosine similarity to the question, and which contains the desired answer.
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+
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+ Further, many answers in the MKQA dataset were "language normalised": for instance, all
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+ date answers were converted to the format "YYYY-MM-DD", meaning that in most cases
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+ these answers are not appearing in any paragraphs. We solve this by extending the MKQA
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+ answers with plausible "answer candidates", being slight perturbations or translations
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+ of the answer.
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+
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+ With the contexts extracted, we translated these to Danish, Swedish and Norwegian using
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+ the [DeepL translation service](https://www.deepl.com/pro-api?cta=header-pro-api) for
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+ Danish and Swedish, and the [Google Translation
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+ service](https://cloud.google.com/translate/docs/reference/rest/) for Norwegian. After
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+ translation we ensured that the Scandinavian answers do indeed occur in the translated
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+ contexts.
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+
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+ As we are filtering the MKQA samples at both the "merging stage" and the "translation
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+ stage", we are not able to fully convert the 10,000 samples to the Scandinavian
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+ languages, and instead get roughly 8,000 samples per language. These have further been
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+ split into a training, validation and test split, with the latter two containing
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+ roughly 750 samples. The splits have been created in such a way that the proportion of
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+ samples without an answer is roughly the same in each split.
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+
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ Training machine learning models for extractive question answering is the intended task
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+ for this dataset. No leaderboard is active at this point.
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+
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+
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+ ### Languages
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+
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+ The dataset is available in Danish (`da`), Swedish (`sv`) and Norwegian (`no`).
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+
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ - **Size of downloaded dataset files:** 69 MB
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+ - **Size of the generated dataset:** 67 MB
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+ - **Total amount of disk used:** 136 MB
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+
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+ An example from the `train` split of the `da` subset looks as follows.
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+ ```
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+ {
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+ 'example_id': 123,
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+ 'question': 'Er dette en test?',
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+ 'answer': 'Dette er en test',
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+ 'answer_start': 0,
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+ 'context': 'Dette er en testkontekst.',
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+ 'answer_en': 'This is a test',
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+ 'answer_start_en': 0,
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+ 'context_en': "This is a test",
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+ 'title_en': 'Train test'
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+ }
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+ ```
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+
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+ ### Data Fields
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+
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+ The data fields are the same among all splits.
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+
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+ - `example_id`: an `int64` feature.
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+ - `question`: a `string` feature.
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+ - `answer`: a `string` feature.
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+ - `answer_start`: an `int64` feature.
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+ - `context`: a `string` feature.
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+ - `answer_en`: a `string` feature.
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+ - `answer_start_en`: an `int64` feature.
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+ - `context_en`: a `string` feature.
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+ - `title_en`: a `string` feature.
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+
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+ ### Data Splits
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+
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+ | name | train | validation | test |
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+ |----------|------:|-----------:|-----:|
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+ | da | 6311 | 749 | 750 |
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+ | sv | 6299 | 750 | 749 |
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+ | no | 6314 | 749 | 750 |
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+
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ The Scandinavian languages does not have any gold standard question answering dataset.
129
+ This is not quite gold standard, but the fact both the questions and answers are all
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+ manually translated, it is a solid silver standard dataset.
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+
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+ ### Source Data
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+
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+ The original data was collected from the [MKQA](https://github.com/apple/ml-mkqa/) and
135
+ [Natural Questions](https://ai.google.com/research/NaturalQuestions) datasets from
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+ Apple and Google, respectively.
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+
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ [Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
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+ Institute](https://alexandra.dk/) curated this dataset.
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+
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+ ### Licensing Information
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+
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+ The dataset is licensed under the [CC BY-SA 4.0
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+ license](https://creativecommons.org/licenses/by-sa/4.0/).
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scandi_wiki.py ADDED
@@ -0,0 +1,126 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Copyright 2022 The HuggingFace Datasets Authors and Dan Saattrup Nielsen.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """Python build script for the ScandiWiki dataset."""
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+
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+
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+ import json
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+ from pathlib import Path
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+ from typing import List
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+
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+ import datasets
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+ from datasets import Version
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+ from datasets.builder import BuilderConfig, GeneratorBasedBuilder
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+ from datasets.download import DownloadManager
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+ from datasets.features import Features, Value
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+ from datasets.info import DatasetInfo
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+ from datasets.splits import SplitGenerator
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+
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+ _DESCRIPTION = """
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+ ScandiWiki is a parsed and deduplicated version of the Danish, Norwegian Bokmål,
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+ Norwegian Nynorsk, Swedish, Icelandic and Faroese Wikipedia corpora, as of January
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+ 2023.
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+ """
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+
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+ _LICENSE = "CC BY-SA 4.0"
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+ _URLS = {
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+ "da": "https://huggingface.co/datasets/alexandrainst/scandi-wiki/resolve/main/data/da.jsonl",
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+ "sv": "https://huggingface.co/datasets/alexandrainst/scandi-wiki/resolve/main/data/sv.jsonl",
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+ "nb": "https://huggingface.co/datasets/alexandrainst/scandi-wiki/resolve/main/data/nb.jsonl",
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+ "nn": "https://huggingface.co/datasets/alexandrainst/scandi-wiki/resolve/main/data/nn.jsonl",
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+ "is": "https://huggingface.co/datasets/alexandrainst/scandi-wiki/resolve/main/data/is.jsonl",
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+ "fo": "https://huggingface.co/datasets/alexandrainst/scandi-wiki/resolve/main/data/fo.jsonl",
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+ }
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+
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+ # _CITATION = """
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+ # @InProceedings{huggingface:dataset,
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+ # title = {ScandiWiki: A Scandinavian Wikipedia Dump},
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+ # author={Dan Saattrup Nielsen},
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+ # year={2022}
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+ # }
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+ # """
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+
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+
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+ class ScandiWiki(GeneratorBasedBuilder):
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+ """Scandinavian part of Wikipedia."""
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+
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+ VERSION = Version("1.0.0")
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+
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+ BUILDER_CONFIGS = [
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+ BuilderConfig(
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+ name="da",
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+ version=VERSION,
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+ description="The deduplicated Danish part of Wikipedia.",
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+ ),
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+ BuilderConfig(
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+ name="sv",
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+ version=VERSION,
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+ description="The deduplicated Swedish part of Wikipedia.",
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+ ),
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+ BuilderConfig(
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+ name="nb",
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+ version=VERSION,
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+ description="The deduplicated Norwegian Bokmål part of Wikipedia.",
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+ ),
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+ BuilderConfig(
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+ name="nn",
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+ version=VERSION,
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+ description="The deduplicated Norwegian Nynorsk part of Wikipedia.",
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+ ),
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+ BuilderConfig(
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+ name="is",
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+ version=VERSION,
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+ description="The deduplicated Icelandic part of Wikipedia.",
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+ ),
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+ BuilderConfig(
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+ name="fo",
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+ version=VERSION,
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+ description="The deduplicated Faroese part of Wikipedia.",
89
+ ),
90
+ ]
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+
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+ def _info(self) -> DatasetInfo:
93
+ features = Features(
94
+ {
95
+ "id": Value("string"),
96
+ "url": Value("string"),
97
+ "title": Value("string"),
98
+ "text": Value("string"),
99
+ }
100
+ )
101
+ return DatasetInfo(
102
+ description=_DESCRIPTION,
103
+ features=features,
104
+ # homepage=_HOMEPAGE,
105
+ license=_LICENSE,
106
+ # citation=_CITATION,
107
+ )
108
+
109
+ def _split_generators(self, dl_manager: DownloadManager) -> List[SplitGenerator]:
110
+ url = _URLS[self.config.name]
111
+ downloaded_file = dl_manager.download(url)
112
+ return [
113
+ SplitGenerator(
114
+ name=datasets.Split.TRAIN,
115
+ gen_kwargs=dict(
116
+ filepath=downloaded_file,
117
+ split="train",
118
+ ),
119
+ ),
120
+ ]
121
+
122
+ def _generate_examples(self, filepath: str, split):
123
+ with Path(filepath).open(encoding="utf-8") as f:
124
+ for key, row in enumerate(f):
125
+ data = json.loads(row)
126
+ yield key, data