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README.md

@@ -1,30 +1,114 @@
 ---
-dataset_info:
-  features:
-  - name: document_id
-    dtype: string
-  - name: text_type
-    dtype: string
-  - name: section
-    dtype: string
-  - name: text
-    dtype: string
-  - name: parsed
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1191446985
-    num_examples: 1351894
-  - name: test
-    num_bytes: 132383487
-    num_examples: 150211
-  download_size: 548366122
-  dataset_size: 1323830472
-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-generation
+- token-classification
+tags:
+- swedish
+- government-reports
+- dependency-parsing
+- universal-dependencies
+- nlp
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
 ---
+
+# SOU Corpus - Swedish Government Official Reports
+
+Cleaned and dependency-parsed Swedish Government Official Reports (Statens offentliga utredningar) from 1994-2020.
+
+## Dataset Description
+
+This dataset contains sentence-segmented and dependency-parsed text from Swedish Government Official Reports. The original documents were cleaned, processed, and annotated with Universal Dependencies-style parsing.
+
+### Fields
+
+- **document_id**: Original document identifier (can be linked to Riksdagen open data)
+- **text_type**: Type of text section
+  - `full_text`: Main report body
+  - `summary_swedish`: Standard Swedish summary
+  - `summary_simple_swedish`: Simple Swedish (lättläst) summary
+  - `summary_english`: English summary
+- **section**: Section header from the document
+- **text**: Plain text sentence
+- **parsed**: Dependency-parsed sentence (token//POS//deprel//head format)
+
+### Parsed Format
+
+Each token in the `parsed` field follows the format:
+```
+word//POS_TAG//DEPENDENCY_RELATION//HEAD_INDEX
+```
+
+Example:
+```
+Sverige//PM|NOM//nsubj//3 är//VB|PRS|AKT//cop//3 ett//DT|NEU|SIN|IND//det//3 land//NN|NEU|SIN|IND|NOM//ROOT//3
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("UppsalaNLP/sou-corpus")
+
+# Access train/test splits
+train = dataset["train"]
+test = dataset["test"]
+
+# Example
+print(train[0]["text"])
+print(train[0]["section"])
+print(train[0]["text_type"])
+```
+
+### Extract Tokens
+
+```python
+def parse_tokens(parsed_str):
+    tokens = []
+    for t in parsed_str.split(' '):
+        parts = t.split('//')
+        if len(parts) >= 4:
+            tokens.append({
+                'word': parts[0],
+                'pos': parts[1],
+                'deprel': parts[2],
+                'head': int(parts[3]) if parts[3].isdigit() else 0
+            })
+    return tokens
+
+tokens = parse_tokens(train[0]["parsed"])
+```
+
+## Source
+
+Documents obtained from [Riksdagens öppna data](http://data.riksdagen.se). Original document URLs follow the pattern:
+`https://data.riksdagen.se/dokument/{document_id}.html`
+
+## 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/).
+
+## Links
+
+- [Uppsala NLP](https://huggingface.co/UppsalaNLP)
+- [GitHub Repository](https://github.com/UppsalaNLP/SOU-corpus)
+- [Riksdagen Open Data](http://data.riksdagen.se)