README.md

metadata

language:
  - da
  - nb
  - nn
  - sv
dataset_info:
  - config_name: Danish
    features:
      - name: text
        dtype: string
      - name: corruption_type
        dtype: string
      - name: label
        dtype: string
    splits:
      - name: train
        num_bytes: 139194
        num_examples: 1024
      - name: test
        num_bytes: 281517
        num_examples: 2048
      - name: full_train
        num_bytes: 733506
        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
        num_bytes: 126028
        num_examples: 1024
      - name: test
        num_bytes: 258103
        num_examples: 2048
      - name: full_train
        num_bytes: 3221649
        num_examples: 25946
      - name: val
        num_bytes: 31302
        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:
      - name: train
        num_bytes: 136251
        num_examples: 1024
      - name: test
        num_bytes: 268761
        num_examples: 2048
      - name: full_train
        num_bytes: 3062138
        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
        num_bytes: 135999
        num_examples: 1024
      - name: test
        num_bytes: 262897
        num_examples: 2048
      - name: full_train
        num_bytes: 1014513
        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.",
}