from __future__ import annotations import datasets from mteb.abstasks.TaskMetadata import TaskMetadata from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval class SpanishPassageRetrievalS2P(AbsTaskRetrieval): metadata = TaskMetadata( name="SpanishPassageRetrievalS2P", description="Test collection for passage retrieval from health-related Web resources in Spanish.", reference="https://mklab.iti.gr/results/spanish-passage-retrieval-dataset/", dataset={ "path": "jinaai/spanish_passage_retrieval", "revision": "9cddf2ce5209ade52c2115ccfa00eb22c6d3a837", }, type="Retrieval", category="s2p", eval_splits=["test"], eval_langs=["spa-Latn"], main_score="ndcg_at_10", date=None, form=None, domains=None, task_subtypes=None, license=None, socioeconomic_status=None, annotations_creators=None, dialect=None, text_creation=None, bibtex_citation=None, n_samples=None, avg_character_length=None, ) def load_data(self, **kwargs): if self.data_loaded: return query_rows = datasets.load_dataset( name="queries", split="test", trust_remote_code=True, **self.metadata_dict["dataset"], ) corpus_rows = datasets.load_dataset( name="corpus.documents", split="test", trust_remote_code=True, **self.metadata_dict["dataset"], ) qrels_rows = datasets.load_dataset( name="qrels.s2p", split="test", trust_remote_code=True, **self.metadata_dict["dataset"], ) self.queries = {"test": {row["_id"]: row["text"] for row in query_rows}} self.corpus = {"test": {row["_id"]: row for row in corpus_rows}} self.relevant_docs = { "test": { row["_id"]: {v: 1 for v in row["text"].split(" ")} for row in qrels_rows } } self.data_loaded = True