README.md

metadata

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

Dataset Card for [Dataset Name]

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: The official homepage of Språkbanken
• Repository:
• Paper:SwedishGLUE – Towards a Swedish Test Set for Evaluating Natural Language Understanding Models
• Leaderboard: [To be implemented]
• Point of Contact: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".

Supported Tasks and Leaderboards

[More Information Needed]

Languages

Swedish

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

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

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

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:

Yvonne Adesam, Aleksandrs Berdicevskis, Felix Morger (2020): [SwedishGLUE – Towards a Swedish Test Set for Evaluating Natural Language Understanding Models] (https://gup.ub.gu.se/publication/299130?lang=sv)

Dataset references:

[More information needed]

Thanks to Felix Morger for adding this dataset.