| from __future__ import annotations | |
| import datasets | |
| from mteb.abstasks.TaskMetadata import TaskMetadata | |
| from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval | |
| class SadeemQuestionRetrieval(AbsTaskRetrieval): | |
| _EVAL_SPLIT = "test" | |
| metadata = TaskMetadata( | |
| name="SadeemQuestionRetrieval", | |
| dataset={ | |
| "path": "sadeem-ai/sadeem-ar-eval-retrieval-questions", | |
| "revision": "3cb0752b182e5d5d740df547748b06663c8e0bd9", | |
| "name": "test", | |
| }, | |
| reference="https://huggingface.co/datasets/sadeem-ai/sadeem-ar-eval-retrieval-questions", | |
| description="SadeemQuestion: A Benchmark Data Set for Community Question-Retrieval Research", | |
| type="Retrieval", | |
| category="s2p", | |
| eval_splits=[_EVAL_SPLIT], | |
| eval_langs=["ara-Arab"], | |
| main_score="ndcg_at_10", | |
| date=("2024-01-01", "2024-04-01"), | |
| form=["written"], | |
| domains=["written"], | |
| task_subtypes=["Article retrieval"], | |
| license="Not specified", | |
| socioeconomic_status="medium", | |
| annotations_creators="derived", | |
| dialect=[], | |
| text_creation="found", | |
| bibtex_citation=""" | |
| @inproceedings{sadeem-2024-ar-retrieval-questions, | |
| title = "SadeemQuestionRetrieval: A New Benchmark for Arabic questions-based Articles Searching.", | |
| author = "abubakr.soliman@sadeem.app" | |
| } | |
| """, | |
| n_samples={_EVAL_SPLIT: 22979}, | |
| avg_character_length={_EVAL_SPLIT: 500.0}, | |
| ) | |
| def load_data(self, **kwargs): | |
| if self.data_loaded: | |
| return | |
| query_list = datasets.load_dataset(**self.metadata_dict["dataset"])["queries"] | |
| queries = {row["query-id"]: row["text"] for row in query_list} | |
| corpus_list = datasets.load_dataset(**self.metadata_dict["dataset"])["corpus"] | |
| corpus = {row["corpus-id"]: {"text": row["text"]} for row in corpus_list} | |
| qrels_list = datasets.load_dataset(**self.metadata_dict["dataset"])["qrels"] | |
| qrels = {row["query-id"]: {row["corpus-id"]: 1} for row in qrels_list} | |
| self.corpus = {self._EVAL_SPLIT: corpus} | |
| self.queries = {self._EVAL_SPLIT: queries} | |
| self.relevant_docs = {self._EVAL_SPLIT: qrels} | |
| self.data_loaded = True | |