# Dataset Card for [Dataset Name]

## 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:** [To be implemented]
- **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".   

### 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:

To appear in EMNLP 2023, citation will come soon.

Dataset references:

[More information needed]

Thanks to [Felix Morger](https://github.com/felixhultin) for adding this dataset.
task_categories:multiple-choice task_categories:text-classification task_categories:question-answering task_categories:sentence-similarity task_categories:token-classification task_ids:sentiment-analysis task_ids:acceptability-classification task_ids:closed-domain-qa task_ids:word-sense-disambiguation task_ids:coreference-resolution annotations_creators:other language_creators:other multilinguality:monolingual size_categories:unknown language:sv region:us
{"annotations_creators": ["other"], "language_creators": ["other"], "language": ["sv"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "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"], "pretty_name": "A standardized suite for evaluation and analysis of Swedish natural language understanding systems.", "tags": []}
TAGS
#task_categories-multiple-choice #task_categories-text-classification #task_categories-question-answering #task_categories-sentence-similarity #task_categories-token-classification #task_ids-sentiment-analysis #task_ids-acceptability-classification #task_ids-closed-domain-qa #task_ids-word-sense-disambiguation #task_ids-coreference-resolution #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-unknown #language-Swedish #region-us 

# 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@URL

### 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

 

### Languages

Swedish

## Dataset Structure

### Data Instances



### Data Fields



### 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



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### 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







### 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:

To appear in EMNLP 2023, citation will come soon.

Dataset references:

[More information needed]

Thanks to Felix Morger for adding this dataset.
# 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@URL ### 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 ### Languages

Swedish ## Dataset Structure ### Data Instances ### Data Fields ### 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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### 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:

To appear in EMNLP 2023, citation will come soon.

Dataset references:

[More information needed]

Thanks to Felix Morger for adding this dataset.
TAGS
#task_categories-multiple-choice #task_categories-text-classification #task_categories-question-answering #task_categories-sentence-similarity #task_categories-token-classification #task_ids-sentiment-analysis #task_ids-acceptability-classification #task_ids-closed-domain-qa #task_ids-word-sense-disambiguation #task_ids-coreference-resolution #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-unknown #language-Swedish #region-us 
 # 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@URL ### 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 ### Languages

Swedish ## Dataset Structure ### Data Instances ### Data Fields ### 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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### 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:

To appear in EMNLP 2023, citation will come soon.

Dataset references:

[More information needed]

Thanks to Felix Morger for adding this dataset.