from __future__ import annotations from mteb.abstasks.TaskMetadata import TaskMetadata from ....abstasks import AbsTaskClassification _LANGUAGES = { "pan": ["pan-Guru"], "bgc": ["bgc-Deva"], "mag": ["mag-Deva"], "bns": ["bns-Deva"], "kfq": ["kfg-Deva"], "noe": ["noe-Deva"], "bhb": ["bhb-Deva"], "bho": ["bho-Deva"], "gbm": ["gbm-Deva"], "mup": ["mup-Deva"], "anp": ["anp-Deva"], "hne": ["hne-Deva"], "bra": ["bra-Deva"], "raj": ["raj-Deva"], "awa": ["awa-Deva"], "guj": ["guj-Gujr"], "ben": ["ben-Beng"], "bhd": ["bhd-Deva"], "kfy": ["kfy-Deva"], "mar": ["mar-Deva"], "bjj": ["bjj-Deva"], } class HinDialectClassification(AbsTaskClassification): metadata = TaskMetadata( name="HinDialectClassification", dataset={ "path": "mlexplorer008/hin_dialect_classification", "revision": "944a44cf93932ce62b51e7c07d44d8cc03d6bcae", }, description="HinDialect: 26 Hindi-related languages and dialects of the Indic Continuum in North India", reference="https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-4839", category="s2s", type="Classification", eval_splits=["test"], eval_langs=_LANGUAGES, main_score="f1", date=("2010-01-01", "2023-01-01"), form=["written"], domains=["Social", "Spoken"], task_subtypes=["Language identification"], license="CC-BY-SA-4.0", socioeconomic_status="mixed", annotations_creators="expert-annotated", dialect=[], text_creation="found", bibtex_citation=""" @misc{11234/1-4839, title = {{HinDialect} 1.1: 26 Hindi-related languages and dialects of the Indic Continuum in North India}, author = {Bafna, Niyati and {\v Z}abokrtsk{\'y}, Zden{\v e}k and Espa{\~n}a-Bonet, Cristina and van Genabith, Josef and Kumar, Lalit "Samyak Lalit" and Suman, Sharda and Shivay, Rahul}, url = {http://hdl.handle.net/11234/1-4839}, note = {{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University}, copyright = {Creative Commons - Attribution-{NonCommercial}-{ShareAlike} 4.0 International ({CC} {BY}-{NC}-{SA} 4.0)}, year = {2022} } """, n_samples={"test": 1152}, avg_character_length={"test": 583.82}, ) def dataset_transform(self) -> None: self.dataset = self.dataset.rename_columns( {"folksong": "text", "language": "label"} )