README.md

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

# Swedish Causality Binary Classification Dataset

Binary causality detection dataset for Swedish text, extracted from Swedish Government Official Reports (SOU-corpus).

## Dataset Description

This dataset contains Swedish sentences annotated for the presence of causal relations. Each example includes:

- **theme**: The thematic category (e.g., "skog, växthuseffekt/klimat")
- **left_context**: Preceding context sentences
- **target_sentence**: The sentence to classify
- **right_context**: Following context sentences
- **label**: Binary annotation (0 = no causality, 1 = causality present)

## Usage

```python
from datasets import load_dataset

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

# Access train/test splits
train = dataset["train"]
test = dataset["test"]

# Example
print(train[0]["target_sentence"])
print(train[0]["label"])
```

## Dataset Statistics

| Split | Examples |
|-------|----------|
| Train | ~80% |
| Test  | ~20% |

## 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/).