File size: 3,648 Bytes
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from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
class LEMBNeedleRetrieval(AbsTaskRetrieval):
_EVAL_SPLIT = [
"test_256",
"test_512",
"test_1024",
"test_2048",
"test_4096",
"test_8192",
"test_16384",
"test_32768",
]
metadata = TaskMetadata(
name="LEMBNeedleRetrieval",
dataset={
"path": "dwzhu/LongEmbed",
"revision": "6e346642246bfb4928c560ee08640dc84d074e8c",
"name": "needle",
},
reference="https://huggingface.co/datasets/dwzhu/LongEmbed",
description=("needle subset of dwzhu/LongEmbed dataset."),
type="Retrieval",
category="s2p",
eval_splits=_EVAL_SPLIT,
eval_langs=["eng-Latn"],
main_score="ndcg_at_10",
date=("2000-01-01", "2023-12-31"),
form=["written"],
domains=["Academic", "Blog"],
task_subtypes=["Article retrieval"],
license="Not specified",
socioeconomic_status="high",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@article{zhu2024longembed,
title={LongEmbed: Extending Embedding Models for Long Context Retrieval},
author={Zhu, Dawei and Wang, Liang and Yang, Nan and Song, Yifan and Wu, Wenhao and Wei, Furu and Li, Sujian},
journal={arXiv preprint arXiv:2404.12096},
year={2024}
}
""",
n_samples={
"test_256": 150,
"test_512": 150,
"test_1024": 150,
"test_2048": 150,
"test_4096": 150,
"test_8192": 150,
"test_16384": 150,
"test_32768": 150,
},
avg_character_length={
"test_256": 1074.4,
"test_512": 2067.0,
"test_1024": 4129.5,
"test_2048": 8513.4,
"test_4096": 17452.7,
"test_8192": 35261.6,
"test_16384": 72113.7,
"test_32768": 141829.0,
},
)
def load_data(self, **kwargs):
if self.data_loaded:
return
self.corpus = {}
self.queries = {}
self.relevant_docs = {}
for split in self._EVAL_SPLIT:
context_length = int(split.split("_")[1])
query_list = datasets.load_dataset(**self.metadata_dict["dataset"])[
"queries"
] # dict_keys(['qid', 'text'])
query_list = query_list.filter(
lambda x: x["context_length"] == context_length
)
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_list = corpus_list.filter(
lambda x: x["context_length"] == context_length
)
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_list = qrels_list.filter(
lambda x: x["context_length"] == context_length
)
qrels = {row["qid"]: {row["doc_id"]: 1} for row in qrels_list}
self.corpus[split] = corpus
self.queries[split] = queries
self.relevant_docs[split] = qrels
self.data_loaded = True
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