File size: 2,048 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 65 66 67 68 69 70 | 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
|