FEA-Bench / testbed /embeddings-benchmark__mteb /mteb /tasks /Classification /multilingual /AfriSentiLangClassification.py
| from __future__ import annotations | |
| from mteb.abstasks import AbsTaskClassification | |
| from mteb.abstasks.TaskMetadata import TaskMetadata | |
| class AfriSentiLangClassification(AbsTaskClassification): | |
| metadata = TaskMetadata( | |
| name="AfriSentiLangClassification", | |
| description="AfriSentiLID is the largest LID classification dataset for African Languages.", | |
| dataset={ | |
| "path": "HausaNLP/afrisenti-lid-data", | |
| "revision": "f17cb5f3ec522ac604601fd09db9fd644ac66ca5", | |
| }, | |
| reference="https://huggingface.co/datasets/HausaNLP/afrisenti-lid-data/", | |
| type="Classification", | |
| category="s2s", | |
| eval_splits=["test"], | |
| eval_langs=[ | |
| "amh-Ethi", # Amharic (Ethiopic script) | |
| "arq-Arab", | |
| "ary-Arab", # Moroccan Arabic, Standard Arabic (Arabic script) | |
| "hau-Latn", # Hausa (Latin script), additional script if written in Ajami (Arabic script) | |
| "ibo-Latn", # Igbo (Latin script) | |
| "kin-Latn", # Kinyarwanda (Latin script) | |
| "por-Latn", # Portuguese (Latin script) | |
| "pcm-Latn", # Nigerian Pidgin (Latin script) | |
| "swa-Latn", # Swahili (macrolanguage) (Latin script) | |
| "twi-Latn", # Twi (Latin script) | |
| "tso-Latn", # Tsonga (Latin script) | |
| "yor-Latn", # Yoruba (Latin script) | |
| ], | |
| main_score="accuracy", | |
| date=("2023-07-04", "2023-08-04"), | |
| form=["written"], | |
| domains=["Social"], | |
| task_subtypes=["Language identification"], | |
| license="cc-by-nc-sa-4.0", | |
| socioeconomic_status="low", | |
| annotations_creators="derived", | |
| dialect=[], | |
| text_creation="found", | |
| bibtex_citation=""" | |
| """, | |
| n_samples={"test": 5754}, | |
| avg_character_length={"test": 77.84}, | |
| ) | |
| def metadata_dict(self) -> dict[str, str]: | |
| metadata_dict = super().metadata_dict | |
| metadata_dict["n_experiments"] = 10 | |
| metadata_dict["samples_per_label"] = 32 | |
| return metadata_dict | |
| def dataset_transform(self): | |
| self.dataset = self.dataset.rename_column("tweet", "text") | |
| self.dataset = self.stratified_subsampling( | |
| self.dataset, seed=self.seed, splits=["test"] | |
| ) | |