---
language:
- da
dataset_info:
- config_name: Danish
  features:
  - name: text
    dtype: string
  - name: corruption_type
    dtype: string
  - name: label
    dtype: string
  splits:
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    num_examples: 1024
  - name: test
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    num_examples: 2048
  - name: full_train
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    num_examples: 5342
  - name: val
    num_bytes: 32942
    num_examples: 256
  download_size: 700593
  dataset_size: 1187159
- config_name: Norwegian_b
  features:
  - name: text
    dtype: string
  - name: corruption_type
    dtype: string
  - name: label
    dtype: string
  splits:
  - name: train
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    num_examples: 1024
  - name: test
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  - name: full_train
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  - name: val
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    num_examples: 256
  download_size: 2161548
  dataset_size: 3637082
- config_name: Norwegian_n
  features:
  - name: text
    dtype: string
  - name: corruption_type
    dtype: string
  - name: label
    dtype: string
  splits:
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    num_examples: 1024
  - name: test
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    num_examples: 2048
  - name: full_train
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    num_examples: 22800
  - name: val
    num_bytes: 33910
    num_examples: 256
  download_size: 2088966
  dataset_size: 3501060
- config_name: Swedish
  features:
  - name: text
    dtype: string
  - name: corruption_type
    dtype: string
  - name: label
    dtype: string
  splits:
  - name: train
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    num_examples: 1024
  - name: test
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    num_examples: 2048
  - name: full_train
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    num_examples: 7446
  - name: val
    num_bytes: 36681
    num_examples: 256
  download_size: 807624
  dataset_size: 1450090
configs:
- config_name: Danish
  data_files:
  - split: train
    path: Danish/train-*
  - split: test
    path: Danish/test-*
  - split: full_train
    path: Danish/full_train-*
  - split: val
    path: Danish/val-*
- config_name: Norwegian_b
  data_files:
  - split: train
    path: Norwegian_b/train-*
  - split: test
    path: Norwegian_b/test-*
  - split: full_train
    path: Norwegian_b/full_train-*
  - split: val
    path: Norwegian_b/val-*
- config_name: Norwegian_n
  data_files:
  - split: train
    path: Norwegian_n/train-*
  - split: test
    path: Norwegian_n/test-*
  - split: full_train
    path: Norwegian_n/full_train-*
  - split: val
    path: Norwegian_n/val-*
- config_name: Swedish
  data_files:
  - split: train
    path: Swedish/train-*
  - split: test
    path: Swedish/test-*
  - split: full_train
    path: Swedish/full_train-*
  - split: val
    path: Swedish/val-*
---

## ScandEval

Multilingual version of nordic languages dataset for linguistic acceptability classification.

See versions for:

- Swedish: https://huggingface.co/datasets/mteb/scala_sv_classification
- Norwegian Bokmål: https://huggingface.co/datasets/mteb/scala_nn_classification
- Norwegian Nynorsk: https://huggingface.co/datasets/mteb/scala_nb_classification
- Danish: https://huggingface.co/datasets/mteb/scala_da_classification

Reference: https://aclanthology.org/2023.nodalida-1.20/


Cite:
```
@inproceedings{nielsen-2023-scandeval,
    title = "{S}cand{E}val: A Benchmark for {S}candinavian Natural Language Processing",
    author = "Nielsen, Dan",
    editor = {Alum{\"a}e, Tanel  and
      Fishel, Mark},
    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
    month = may,
    year = "2023",
    address = "T{\'o}rshavn, Faroe Islands",
    publisher = "University of Tartu Library",
    url = "https://aclanthology.org/2023.nodalida-1.20",
    pages = "185--201",
    abstract = "This paper introduces a Scandinavian benchmarking platform, ScandEval, which can benchmark any pretrained model on four different tasks in the Scandinavian languages. The datasets used in two of the tasks, linguistic acceptability and question answering, are new. We develop and release a Python package and command-line interface, scandeval, which can benchmark any model that has been uploaded to the Hugging Face Hub, with reproducible results. Using this package, we benchmark more than 80 Scandinavian or multilingual models and present the results of these in an interactive online leaderboard, as well as provide an analysis of the results. The analysis shows that there is substantial cross-lingual transfer among the the Mainland Scandinavian languages (Danish, Swedish and Norwegian), with limited cross-lingual transfer between the group of Mainland Scandinavian languages and the group of Insular Scandinavian languages (Icelandic and Faroese). The benchmarking results also show that the investment in language technology in Norway and Sweden has led to language models that outperform massively multilingual models such as XLM-RoBERTa and mDeBERTaV3. We release the source code for both the package and leaderboard.",
}
```