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README.md

@@ -1,33 +1,81 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: int64
-  - name: prompt
-    dtype: string
-  - name: sentence_1_left_context
-    dtype: string
-  - name: sentence_1_target
-    dtype: string
-  - name: sentence_1_right_context
-    dtype: string
-  - name: sentence_2_left_context
-    dtype: string
-  - name: sentence_2_target
-    dtype: string
-  - name: sentence_2_right_context
-    dtype: string
-  - name: annotation
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 1078561
-    num_examples: 800
-  download_size: 462799
-  dataset_size: 1078561
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+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/).