README.md

---
annotations_creators:
- other
language:
- sv
language_creators:
- other
multilinguality:
- monolingual
pretty_name: >-
  A standardized suite for evaluation and analysis of Swedish natural language
  understanding systems.
size_categories:
- unknown
source_datasets: []
task_categories:
- multiple-choice
- text-classification
- question-answering
- sentence-similarity
- token-classification
- summarization
task_ids:
- sentiment-analysis
- acceptability-classification
- closed-domain-qa
- word-sense-disambiguation
- coreference-resolution
---
# Dataset Card for Superlim-2

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [The official homepage of Språkbanken](https://spraakbanken.gu.se/resurser/superlim/)
- **Repository:**
- **Paper:**[SwedishGLUE – Towards a Swedish Test Set for Evaluating Natural Language Understanding Models](https://gup.ub.gu.se/publication/299130?lang=sv)
- **Leaderboard:** https://lab.kb.se/leaderboard/
- **Point of Contact:**[sb-info@svenska.gu.se](sb-info@svenska.gu.se)

### Dataset Summary

SuperLim 2.0 is a continuation of SuperLim 1.0, which aims for a standardized suite for evaluation and analysis of Swedish natural language understanding systems. The projects is inspired by the GLUE/SuperGLUE projects from which the name is derived: "lim" is the Swedish translation of "glue".   

Since Superlim 2.0 is a collection of datasets, we refer for information about dataset structure, creation, social impact etc. to the specific data cards or documentation sheets in the official GitHub repository: https://github.com/spraakbanken/SuperLim-2/

### Supported Tasks and Leaderboards

See our leaderboard: https://lab.kb.se/leaderboard/

### Languages

Swedish

## Dataset Structure

### Data Instances

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Data Fields

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Data Splits

Most datasets have a train, dev and test split. However, there are a few (`supersim`, `sweanalogy` and `swesat-synonyms`) who only have a train and test split. The diagnostic tasks `swediagnostics` and `swewinogender` only have a test split, but they could be evaluated on models trained on `swenli` since they are also NLI-based.

## Dataset Creation


### Curation Rationale

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Source Data

#### Initial Data Collection and Normalization

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

#### Who are the source language producers?

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Annotations

#### Annotation process

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

#### Who are the annotators?

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Personal and Sensitive Information

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

## Considerations for Using the Data

### Social Impact of Dataset

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Discussion of Biases

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Other Known Limitations

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Dataset Curators

See individual datasets: https://github.com/spraakbanken/SuperLim-2/

### Licensing Information

All datasets constituting Superlim are available under Creative Commons licenses (CC BY 4.0, 8144 CC BY-SA 4.0, respectively).

### Citation Information

To cite as a whole, use the standard reference. If you use or reference individual resources, cite the references specific for these resources:
 
Standard reference:

Superlim: A Swedish Language Understanding Evaluation Benchmark (Berdicevskis et al., EMNLP 2023)

```

@inproceedings{berdicevskis-etal-2023-superlim,
    title = "Superlim: A {S}wedish Language Understanding Evaluation Benchmark",
    author = {Berdicevskis, Aleksandrs  and
      Bouma, Gerlof  and
      Kurtz, Robin  and
      Morger, Felix  and
      {\"O}hman, Joey  and
      Adesam, Yvonne  and
      Borin, Lars  and
      Dann{\'e}lls, Dana  and
      Forsberg, Markus  and
      Isbister, Tim  and
      Lindahl, Anna  and
      Malmsten, Martin  and
      Rekathati, Faton  and
      Sahlgren, Magnus  and
      Volodina, Elena  and
      B{\"o}rjeson, Love  and
      Hengchen, Simon  and
      Tahmasebi, Nina},
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.506",
    doi = "10.18653/v1/2023.emnlp-main.506",
    pages = "8137--8153",
    abstract = "We present Superlim, a multi-task NLP benchmark and analysis platform for evaluating Swedish language models, a counterpart to the English-language (Super)GLUE suite. We describe the dataset, the tasks, the leaderboard and report the baseline results yielded by a reference implementation. The tested models do not approach ceiling performance on any of the tasks, which suggests that Superlim is truly difficult, a desirable quality for a benchmark. We address methodological challenges, such as mitigating the Anglocentric bias when creating datasets for a less-resourced language; choosing the most appropriate measures; documenting the datasets and making the leaderboard convenient and transparent. We also highlight other potential usages of the dataset, such as, for instance, the evaluation of cross-lingual transfer learning.",
}


```

Thanks to [Felix Morger](https://github.com/felixhultin) for adding this dataset.