File size: 2,127 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 | from __future__ import annotations
from collections import defaultdict
from datasets import DatasetDict, load_dataset
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
def load_retrieval_data(dataset_path, eval_splits):
eval_split = eval_splits[0]
corpus_dataset = load_dataset(dataset_path, "corpus")
queries_dataset = load_dataset(dataset_path, "queries")
qrels = load_dataset(dataset_path + "-qrels")[eval_split]
corpus = {e["_id"]: {"text": e["text"]} for e in corpus_dataset["corpus"]}
queries = {e["_id"]: e["text"] for e in queries_dataset["queries"]}
relevant_docs = defaultdict(dict)
for e in qrels:
relevant_docs[e["query-id"]][e["corpus-id"]] = e["score"]
corpus = DatasetDict({eval_split: corpus})
queries = DatasetDict({eval_split: queries})
relevant_docs = DatasetDict({eval_split: relevant_docs})
return corpus, queries, relevant_docs
class GermanQuADRetrieval(AbsTaskRetrieval):
metadata = TaskMetadata(
name="GermanQuAD-Retrieval",
description="Context Retrieval for German Question Answering",
reference="https://www.kaggle.com/datasets/GermanQuAD",
dataset={
"path": "mteb/germanquad-retrieval",
"revision": "f5c87ae5a2e7a5106606314eef45255f03151bb3",
},
type="Retrieval",
category="s2p",
eval_splits=["test"],
eval_langs=["deu-Latn"],
main_score="mrr_at_5",
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
self.corpus, self.queries, self.relevant_docs = load_retrieval_data(
self.metadata_dict["dataset"]["path"], self.metadata_dict["eval_splits"]
)
self.data_loaded = True
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