File size: 2,526 Bytes
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from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
class LEMBWikimQARetrieval(AbsTaskRetrieval):
_EVAL_SPLIT = "test"
metadata = TaskMetadata(
name="LEMBWikimQARetrieval",
dataset={
"path": "dwzhu/LongEmbed",
"revision": "6e346642246bfb4928c560ee08640dc84d074e8c",
"name": "2wikimqa",
},
reference="https://huggingface.co/datasets/dwzhu/LongEmbed",
description=("2wikimqa subset of dwzhu/LongEmbed dataset."),
type="Retrieval",
category="s2p",
eval_splits=[_EVAL_SPLIT],
eval_langs=["eng-Latn"],
main_score="ndcg_at_10",
date=("1950-01-01", "2019-12-31"),
form=["written"],
domains=["Encyclopaedic"],
task_subtypes=["Article retrieval"],
license="Not specified",
socioeconomic_status="medium",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@inproceedings{ho2020constructing,
title={Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps},
author={Ho, Xanh and Nguyen, Anh-Khoa Duong and Sugawara, Saku and Aizawa, Akiko},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={6609--6625},
year={2020}
}
""",
n_samples={_EVAL_SPLIT: 500},
avg_character_length={_EVAL_SPLIT: 37513},
)
def load_data(self, **kwargs):
if self.data_loaded:
return
query_list = datasets.load_dataset(**self.metadata_dict["dataset"])[
"queries"
] # dict_keys(['qid', 'text'])
queries = {row["qid"]: row["text"] for row in query_list}
corpus_list = datasets.load_dataset(**self.metadata_dict["dataset"])[
"corpus"
] # dict_keys(['doc_id', 'text'])
corpus = {row["doc_id"]: {"text": row["text"]} for row in corpus_list}
qrels_list = datasets.load_dataset(**self.metadata_dict["dataset"])[
"qrels"
] # dict_keys(['qid', 'doc_id'])
qrels = {row["qid"]: {row["doc_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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