| from __future__ import annotations |
|
|
| import datasets |
|
|
| from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval |
| from mteb.abstasks.TaskMetadata import TaskMetadata |
|
|
|
|
| class GerDaLIR(AbsTaskRetrieval): |
| _EVAL_SPLIT = "test" |
|
|
| metadata = TaskMetadata( |
| name="GerDaLIR", |
| description="GerDaLIR is a legal information retrieval dataset created from the Open Legal Data platform.", |
| reference="https://github.com/lavis-nlp/GerDaLIR", |
| dataset={ |
| "path": "jinaai/ger_da_lir", |
| "revision": "0bb47f1d73827e96964edb84dfe552f62f4fd5eb", |
| }, |
| type="Retrieval", |
| category="s2p", |
| eval_splits=[_EVAL_SPLIT], |
| eval_langs=["deu-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=self._EVAL_SPLIT, |
| **self.metadata_dict["dataset"], |
| ) |
| corpus_rows = datasets.load_dataset( |
| name="corpus", |
| split=self._EVAL_SPLIT, |
| **self.metadata_dict["dataset"], |
| ) |
| qrels_rows = datasets.load_dataset( |
| name="qrels", |
| split=self._EVAL_SPLIT, |
| **self.metadata_dict["dataset"], |
| ) |
|
|
| self.queries = { |
| self._EVAL_SPLIT: {row["_id"]: row["text"] for row in query_rows} |
| } |
| self.corpus = {self._EVAL_SPLIT: {row["_id"]: row for row in corpus_rows}} |
| self.relevant_docs = { |
| self._EVAL_SPLIT: { |
| row["_id"]: {v: 1 for v in row["text"].split(" ")} for row in qrels_rows |
| } |
| } |
|
|
| self.data_loaded = True |
|
|
