--- annotations_creators: - human-annotated language: - dan - nno - nob - swe license: cc-by-sa-4.0 multilinguality: multilingual task_categories: - text-classification task_ids: - acceptability-classification dataset_info: - config_name: Danish features: - name: text dtype: string - name: corruption_type dtype: string - name: label dtype: string splits: - name: train num_bytes: 139194 num_examples: 1024 - name: test num_bytes: 281517 num_examples: 2048 - name: full_train num_bytes: 733506 num_examples: 5342 - name: val num_bytes: 32942 num_examples: 256 download_size: 700593 dataset_size: 1187159 - config_name: Norwegian_b features: - name: text dtype: string - name: corruption_type dtype: string - name: label dtype: string splits: - name: train num_bytes: 126028 num_examples: 1024 - name: test num_bytes: 258103 num_examples: 2048 - name: full_train num_bytes: 3221649 num_examples: 25946 - name: val num_bytes: 31302 num_examples: 256 download_size: 2161548 dataset_size: 3637082 - config_name: Norwegian_n features: - name: text dtype: string - name: corruption_type dtype: string - name: label dtype: string splits: - name: train num_bytes: 136251 num_examples: 1024 - name: test num_bytes: 268761 num_examples: 2048 - name: full_train num_bytes: 3062138 num_examples: 22800 - name: val num_bytes: 33910 num_examples: 256 download_size: 2088966 dataset_size: 3501060 - config_name: Swedish features: - name: text dtype: string - name: corruption_type dtype: string - name: label dtype: string splits: - name: train num_bytes: 135999 num_examples: 1024 - name: test num_bytes: 262897 num_examples: 2048 - name: full_train num_bytes: 1014513 num_examples: 7446 - name: val num_bytes: 36681 num_examples: 256 download_size: 807624 dataset_size: 1450090 configs: - config_name: Danish data_files: - split: train path: Danish/train-* - split: test path: Danish/test-* - split: full_train path: Danish/full_train-* - split: val path: Danish/val-* - config_name: Norwegian_b data_files: - split: train path: Norwegian_b/train-* - split: test path: Norwegian_b/test-* - split: full_train path: Norwegian_b/full_train-* - split: val path: Norwegian_b/val-* - config_name: Norwegian_n data_files: - split: train path: Norwegian_n/train-* - split: test path: Norwegian_n/test-* - split: full_train path: Norwegian_n/full_train-* - split: val path: Norwegian_n/val-* - config_name: Swedish data_files: - split: train path: Swedish/train-* - split: test path: Swedish/test-* - split: full_train path: Swedish/full_train-* - split: val path: Swedish/val-* tags: - mteb - text ---

ScalaClassification

An MTEB dataset
Massive Text Embedding Benchmark
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' | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Fiction, News, Non-fiction, Blog, Spoken, Web, Written | | 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: ```python import mteb task = mteb.get_tasks(["ScalaClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` 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, 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
Dataset Statistics 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 } } } } } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*