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README.md

@@ -1,38 +1,73 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: int64
-  - name: keyword
-    dtype: string
-  - name: document
-    dtype: string
-  - name: section
-    dtype: string
-  - name: left_context
-    dtype: string
-  - name: target_sentence
-    dtype: string
-  - name: right_context
-    dtype: string
-  - name: label
-    dtype: int64
-  - name: annotator_agreement
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 120414
-    num_examples: 167
-  - name: test
-    num_bytes: 30284
-    num_examples: 42
-  download_size: 107228
-  dataset_size: 150698
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+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 Trial Dataset
+
+Binary causality detection trial dataset for Swedish text with keyword-based sampling.
+
+## Dataset Description
+
+This dataset contains Swedish sentences annotated for causality, sampled based on causal keywords (e.g., "bero på", "leda till").
+
+### Fields
+
+- **keyword**: The causal keyword used to sample the sentence
+- **document**: Source document ID
+- **section**: Section within the document
+- **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) - majority vote of 3 annotators
+- **annotator_agreement**: Number of annotators who agreed with the majority label (2 or 3)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("UppsalaNLP/swedish-causality-trial")
+
+# Access train/test splits
+train = dataset["train"]
+test = dataset["test"]
+
+# Example
+print(train[0]["keyword"])
+print(train[0]["target_sentence"])
+print(train[0]["label"])
+```
+
+## 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/).