File size: 2,367 Bytes
73cc8d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | 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
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