from __future__ import annotations from mteb.abstasks import AbsTaskClassification from mteb.abstasks.TaskMetadata import TaskMetadata class CyrillicTurkicLangClassification(AbsTaskClassification): metadata = TaskMetadata( name="CyrillicTurkicLangClassification", description="Cyrillic dataset of 8 Turkic languages spoken in Russia and former USSR", dataset={ "path": "tatiana-merz/cyrillic_turkic_langs", "revision": "e42d330f33d65b7b72dfd408883daf1661f06f18", }, reference="https://huggingface.co/datasets/tatiana-merz/cyrillic_turkic_langs", type="Classification", category="s2s", eval_splits=["test"], eval_langs=[ "bak-Cyrl", # Bashkir "chv-Cyrl", # Chuvash "tat-Cyrl", # Tatar "kir-Cyrl", # Kyrgyz "rus-Cyrl", # Russian "kaz-Cyrl", # Kazakh "tyv-Cyrl", # Tuvinian "krc-Cyrl", # Karachay-Balkar "sah-Cyrl", # Yakut ], main_score="accuracy", date=("1998-01-01", "2012-05-01"), form=["written"], domains=["Web"], task_subtypes=["Language identification"], license="CC BY-NC 4.0 DEED", socioeconomic_status="mixed", annotations_creators="derived", dialect=[], text_creation="found", bibtex_citation=""" @inproceedings{goldhahn2012building, title={Building Large Monolingual Dictionaries at the Leipzig Corpora Collection: From 100 to 200 Languages}, author={Goldhahn, Dirk and Eckart, Thomas and Quasthoff, Uwe}, booktitle={Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)}, year={2012} } """, n_samples={"test": 2048}, avg_character_length={"test": 92.22}, ) def dataset_transform(self): self.dataset = self.stratified_subsampling( self.dataset, seed=self.seed, splits=["test"] )