Add dataset card

README.md

@@ -1,9 +1,17 @@
 ---
+annotations_creators:
+- human-annotated
 language:
-- da
-- nb
-- nn
-- sv
+- dan
+- nob
+- nno
+- swe
+license: cc-by-sa-4.0
+multilinguality: multilingual
+task_categories:
+- text-classification
+task_ids:
+- Linguistic acceptability
 dataset_info:
 - config_name: Danish
   features:
@@ -138,36 +146,291 @@ configs:
     path: Swedish/full_train-*
   - split: val
     path: Swedish/val-*
+tags:
+- mteb
+- text
 ---
+<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
 
-## ScandEval
+<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
+  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">ScalaClassification</h1>
+  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
+  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
+</div>
 
-Multilingual version of nordic languages dataset for linguistic acceptability classification.
+ScaLa a linguistic acceptability dataset for the mainland Scandinavian languages automatically constructed from dependency annotations in Universal Dependencies Treebanks.
+        Published as part of 'ScandEval: A Benchmark for Scandinavian Natural Language Processing'
 
-See versions for:
+|               |                                             |
+|---------------|---------------------------------------------|
+| Task category | t2c                              |
+| Domains       | Fiction, News, Non-fiction, Blog, Spoken, Web, Written                               |
+| Reference     | https://aclanthology.org/2023.nodalida-1.20/ |
 
-- Swedish: https://huggingface.co/datasets/mteb/scala_sv_classification
-- Norwegian Bokmål: https://huggingface.co/datasets/mteb/scala_nn_classification
-- Norwegian Nynorsk: https://huggingface.co/datasets/mteb/scala_nb_classification
-- Danish: https://huggingface.co/datasets/mteb/scala_da_classification
 
-Reference: https://aclanthology.org/2023.nodalida-1.20/
+## How to evaluate on this task
 
+You can evaluate an embedding model on this dataset using the following code:
 
-Cite:
+```python
+import mteb
+
+task = mteb.get_tasks(["ScalaClassification"])
+evaluator = mteb.MTEB(task)
+
+model = mteb.get_model(YOUR_MODEL)
+evaluator.run(model)
 ```
+
+<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
+To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). 
+
+## Citation
+
+If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
+
+```bibtex
+
 @inproceedings{nielsen-2023-scandeval,
-    title = "{S}cand{E}val: A Benchmark for {S}candinavian Natural Language Processing",
-    author = "Nielsen, Dan",
-    editor = {Alum{\"a}e, Tanel  and
-      Fishel, Mark},
-    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
-    month = may,
-    year = "2023",
-    address = "T{\'o}rshavn, Faroe Islands",
-    publisher = "University of Tartu Library",
-    url = "https://aclanthology.org/2023.nodalida-1.20",
-    pages = "185--201",
-    abstract = "This paper introduces a Scandinavian benchmarking platform, ScandEval, which can benchmark any pretrained model on four different tasks in the Scandinavian languages. The datasets used in two of the tasks, linguistic acceptability and question answering, are new. We develop and release a Python package and command-line interface, scandeval, which can benchmark any model that has been uploaded to the Hugging Face Hub, with reproducible results. Using this package, we benchmark more than 80 Scandinavian or multilingual models and present the results of these in an interactive online leaderboard, as well as provide an analysis of the results. The analysis shows that there is substantial cross-lingual transfer among the the Mainland Scandinavian languages (Danish, Swedish and Norwegian), with limited cross-lingual transfer between the group of Mainland Scandinavian languages and the group of Insular Scandinavian languages (Icelandic and Faroese). The benchmarking results also show that the investment in language technology in Norway and Sweden has led to language models that outperform massively multilingual models such as XLM-RoBERTa and mDeBERTaV3. We release the source code for both the package and leaderboard.",
+  address = {T{\'o}rshavn, Faroe Islands},
+  author = {Nielsen, Dan},
+  booktitle = {Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)},
+  editor = {Alum{\"a}e, Tanel  and
+Fishel, Mark},
+  month = may,
+  pages = {185--201},
+  publisher = {University of Tartu Library},
+  title = {{S}cand{E}val: A Benchmark for {S}candinavian Natural Language Processing},
+  url = {https://aclanthology.org/2023.nodalida-1.20},
+  year = {2023},
 }
-```
+
+
+@article{enevoldsen2025mmtebmassivemultilingualtext,
+  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
+  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
+  publisher = {arXiv},
+  journal={arXiv preprint arXiv:2502.13595},
+  year={2025},
+  url={https://arxiv.org/abs/2502.13595},
+  doi = {10.48550/arXiv.2502.13595},
+}
+
+@article{muennighoff2022mteb,
+  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
+  title = {MTEB: Massive Text Embedding Benchmark},
+  publisher = {arXiv},
+  journal={arXiv preprint arXiv:2210.07316},
+  year = {2022}
+  url = {https://arxiv.org/abs/2210.07316},
+  doi = {10.48550/ARXIV.2210.07316},
+}
+```
+
+# Dataset Statistics
+<details>
+  <summary> Dataset Statistics</summary>
+
+The following code contains the descriptive statistics from the task. These can also be obtained using:
+
+```python
+import mteb
+
+task = mteb.get_task("ScalaClassification")
+
+desc_stats = task.metadata.descriptive_stats
+```
+
+```json
+{
+    "test": {
+        "num_samples": 8192,
+        "number_of_characters": 839257,
+        "number_texts_intersect_with_train": 0,
+        "min_text_length": 13,
+        "average_text_length": 102.4483642578125,
+        "max_text_length": 613,
+        "unique_text": 8192,
+        "unique_labels": 2,
+        "labels": {
+            "0": {
+                "count": 4096
+            },
+            "1": {
+                "count": 4096
+            }
+        },
+        "hf_subset_descriptive_stats": {
+            "Danish": {
+                "num_samples": 2048,
+                "number_of_characters": 224132,
+                "number_texts_intersect_with_train": 0,
+                "min_text_length": 13,
+                "average_text_length": 109.439453125,
+                "max_text_length": 443,
+                "unique_text": 2048,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 1024
+                    },
+                    "1": {
+                        "count": 1024
+                    }
+                }
+            },
+            "Norwegian_b": {
+                "num_samples": 2048,
+                "number_of_characters": 201596,
+                "number_texts_intersect_with_train": 0,
+                "min_text_length": 18,
+                "average_text_length": 98.435546875,
+                "max_text_length": 397,
+                "unique_text": 2048,
+                "unique_labels": 2,
+                "labels": {
+                    "1": {
+                        "count": 1024
+                    },
+                    "0": {
+                        "count": 1024
+                    }
+                }
+            },
+            "Norwegian_n": {
+                "num_samples": 2048,
+                "number_of_characters": 212059,
+                "number_texts_intersect_with_train": 0,
+                "min_text_length": 18,
+                "average_text_length": 103.54443359375,
+                "max_text_length": 349,
+                "unique_text": 2048,
+                "unique_labels": 2,
+                "labels": {
+                    "1": {
+                        "count": 1024
+                    },
+                    "0": {
+                        "count": 1024
+                    }
+                }
+            },
+            "Swedish": {
+                "num_samples": 2048,
+                "number_of_characters": 201470,
+                "number_texts_intersect_with_train": 0,
+                "min_text_length": 17,
+                "average_text_length": 98.3740234375,
+                "max_text_length": 613,
+                "unique_text": 2048,
+                "unique_labels": 2,
+                "labels": {
+                    "1": {
+                        "count": 1024
+                    },
+                    "0": {
+                        "count": 1024
+                    }
+                }
+            }
+        }
+    },
+    "train": {
+        "num_samples": 4096,
+        "number_of_characters": 421198,
+        "number_texts_intersect_with_train": null,
+        "min_text_length": 14,
+        "average_text_length": 102.83154296875,
+        "max_text_length": 402,
+        "unique_text": 4096,
+        "unique_labels": 2,
+        "labels": {
+            "1": {
+                "count": 2048
+            },
+            "0": {
+                "count": 2048
+            }
+        },
+        "hf_subset_descriptive_stats": {
+            "Danish": {
+                "num_samples": 1024,
+                "number_of_characters": 110271,
+                "number_texts_intersect_with_train": null,
+                "min_text_length": 14,
+                "average_text_length": 107.6865234375,
+                "max_text_length": 392,
+                "unique_text": 1024,
+                "unique_labels": 2,
+                "labels": {
+                    "1": {
+                        "count": 512
+                    },
+                    "0": {
+                        "count": 512
+                    }
+                }
+            },
+            "Norwegian_b": {
+                "num_samples": 1024,
+                "number_of_characters": 97878,
+                "number_texts_intersect_with_train": null,
+                "min_text_length": 18,
+                "average_text_length": 95.583984375,
+                "max_text_length": 350,
+                "unique_text": 1024,
+                "unique_labels": 2,
+                "labels": {
+                    "1": {
+                        "count": 512
+                    },
+                    "0": {
+                        "count": 512
+                    }
+                }
+            },
+            "Norwegian_n": {
+                "num_samples": 1024,
+                "number_of_characters": 107913,
+                "number_texts_intersect_with_train": null,
+                "min_text_length": 20,
+                "average_text_length": 105.3837890625,
+                "max_text_length": 402,
+                "unique_text": 1024,
+                "unique_labels": 2,
+                "labels": {
+                    "1": {
+                        "count": 512
+                    },
+                    "0": {
+                        "count": 512
+                    }
+                }
+            },
+            "Swedish": {
+                "num_samples": 1024,
+                "number_of_characters": 105136,
+                "number_texts_intersect_with_train": null,
+                "min_text_length": 19,
+                "average_text_length": 102.671875,
+                "max_text_length": 326,
+                "unique_text": 1024,
+                "unique_labels": 2,
+                "labels": {
+                    "1": {
+                        "count": 512
+                    },
+                    "0": {
+                        "count": 512
+                    }
+                }
+            }
+        }
+    }
+}
+```
+
+</details>
+
+---
+*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*