Datasets:
Tasks:
Text Classification
Multilinguality:
translation
Size Categories:
unknown
Language Creators:
other
Annotations Creators:
other
License:
| annotations_creators: | |
| - other | |
| language_creators: | |
| - other | |
| language: | |
| - sv | |
| - da | |
| - nb | |
| license: | |
| - cc-by-4.0 | |
| multilinguality: | |
| - translation | |
| size_categories: | |
| - unknown | |
| source_datasets: | |
| - extended|glue | |
| - extended|super_glue | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - natural-language-inference | |
| - semantic-similarity-classification | |
| - sentiment-classification | |
| - text-scoring | |
| pretty_name: overlim | |
| tags: | |
| - qa-nli | |
| - paraphrase-identification | |
| # Dataset Card for OverLim | |
| ## Dataset Description | |
| - **Homepage:** | |
| - **Repository:** | |
| - **Paper:** | |
| - **Leaderboard:** | |
| - **Point of Contact:** | |
| ### Dataset Summary | |
| The _OverLim_ dataset contains some of the GLUE and SuperGLUE tasks automatically | |
| translated to Swedish, Danish, and Norwegian (bokmål), using the OpusMT models | |
| for MarianMT. | |
| The translation quality was not manually checked and may thus be faulty. | |
| Results on these datasets should thus be interpreted carefully. | |
| If you want to have an easy script to train and evaluate your models have a look [here](https://github.com/kb-labb/overlim_eval) | |
| ### Supported Tasks and Leaderboards | |
| The data contains the following tasks from GLUE and SuperGLUE: | |
| - GLUE | |
| - `mnli` | |
| - `mrpc` | |
| - `qnli` | |
| - `qqp` | |
| - `rte` | |
| - `sst` | |
| - `stsb` | |
| - `wnli` | |
| - SuperGLUE | |
| - `boolq` | |
| - `cb` | |
| - `copa` | |
| - `rte` | |
| ### Languages | |
| - Swedish | |
| - Danish | |
| - Norwegian (bokmål) | |
| ## Dataset Structure | |
| ### Data Instances | |
| Every task has their own set of features, but all share an `idx` and `label`. | |
| - GLUE | |
| - `mnli` | |
| - `premise`, `hypothesis` | |
| - `mrpc` | |
| - `text_a`, `text_b` | |
| - `qnli` | |
| - `premise`, `hypothesis` | |
| - `qqp` | |
| - `text_a`, `text_b` | |
| - `sst` | |
| - `text` | |
| - `stsb` | |
| - `text_a`, `text_b` | |
| - `wnli` | |
| - `premise`, `hypothesis` | |
| - SuperGLUE | |
| - `boolq` | |
| - `question`, `passage` | |
| - `cb` | |
| - `premise`, `hypothesis` | |
| - `copa` | |
| - `premise`, `choice1`, `choice2`, `question` | |
| - `rte` | |
| - `premise`, `hypothesis` | |
| ### Data Splits | |
| In order to have test-split, we repurpose the original validation-split as | |
| test-split, and split the training-split into a new training- and | |
| validation-split, with an 80-20 distribution. | |
| ## Dataset Creation | |
| For more information about the individual tasks see (https://gluebenchmark.com) and (https://super.gluebenchmark.com). | |
| ### Curation Rationale | |
| Training non-English models is easy, but there is a lack of evaluation datasets to compare their actual performance. | |
| ### 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 | |
| Thanks to [@kb-labb](https://github.com/kb-labb) for adding this dataset. | |