---
license: cc-by-4.0
language:
- sv
task_categories:
- text-classification
tags:
- causality
- swedish
- nlp
- ranking
- causality-detection
size_categories:
- n<1K
source_datasets:
- original
---

# Swedish Causality Ranking Dataset

Causality ranking dataset for Swedish text, comparing sentence pairs on how well they match a causal prompt.

## Dataset Description

This dataset contains pairs of Swedish sentences annotated on a 6-point scale for their causal relevance to a given prompt containing a cause or effect.

### Fields

- **prompt**: Query containing a cause or effect (e.g., "Verkan: växthuseffekt")
- **sentence_1_left_context**: Context before sentence 1
- **sentence_1_target**: First target sentence
- **sentence_1_right_context**: Context after sentence 1
- **sentence_2_left_context**: Context before sentence 2
- **sentence_2_target**: Second target sentence
- **sentence_2_right_context**: Context after sentence 2
- **annotation**: Ranking score (1-6 scale)

### Annotation Scale

The 6-point scale compares which sentence better expresses a causal relation matching the prompt:
- Lower scores: Sentence 1 is more relevant
- Higher scores: Sentence 2 is more relevant
- Middle scores: Both sentences are similarly relevant

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("UppsalaNLP/swedish-causality-ranking")

# Access the data
data = dataset["train"]

# Example
print(data[0]["prompt"])
print(data[0]["sentence_1_target"])
print(data[0]["sentence_2_target"])
print(data[0]["annotation"])
```

## Source

Text extracted from the [SOU-corpus](https://github.com/UppsalaNLP/SOU-corpus) (Swedish Government Official Reports).

## Citation

```bibtex
@inproceedings{durlich-etal-2022-cause,
    title = "Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish",
    author = "D{\"u}rlich, Luise and Reimann, Sebastian and Finnveden, Gustav and Nivre, Joakim and Stymne, Sara",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Political Sciences",
    month = jun,
    year = "2022",
    address = "Marseilles, France"
}
```

## License

This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).