| import datasets |
|
|
| from mteb.abstasks.TaskMetadata import TaskMetadata |
|
|
| from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval |
|
|
|
|
| class LEMBPasskeyRetrieval(AbsTaskRetrieval): |
| _EVAL_SPLIT = [ |
| "test_256", |
| "test_512", |
| "test_1024", |
| "test_2048", |
| "test_4096", |
| "test_8192", |
| "test_16384", |
| "test_32768", |
| ] |
|
|
| metadata = TaskMetadata( |
| name="LEMBPasskeyRetrieval", |
| dataset={ |
| "path": "dwzhu/LongEmbed", |
| "revision": "6e346642246bfb4928c560ee08640dc84d074e8c", |
| "name": "passkey", |
| }, |
| reference="https://huggingface.co/datasets/dwzhu/LongEmbed", |
| description=("passkey 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=["Fiction"], |
| task_subtypes=["Article retrieval"], |
| license="Not specified", |
| socioeconomic_status="low", |
| 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": 914.9, |
| "test_512": 1823.0, |
| "test_1024": 3644.7, |
| "test_2048": 7280.0, |
| "test_4096": 14555.5, |
| "test_8192": 29108.1, |
| "test_16384": 58213.9, |
| "test_32768": 116417.9, |
| }, |
| ) |
|
|
| 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" |
| ] |
| 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" |
| ] |
| 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" |
| ] |
| 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 |
|
|
