File size: 3,052 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | from __future__ import annotations
import datasets
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import MultilingualTask
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
_EVAL_SPLIT = "test"
_LANGS = {
"ar": ["ara-Arab"],
"de": ["deu-Latn"],
"es": ["spa-Latn"],
"fr": ["fra-Latn"],
"hi": ["hin-Deva"],
"it": ["ita-Latn"],
"ja": ["jpn-Hira"],
"pt": ["por-Latn"],
}
def _load_mintaka_data(
path: str, langs: list, split: str, cache_dir: str = None, revision: str = None
):
queries = {lang: {split: {}} for lang in langs}
corpus = {lang: {split: {}} for lang in langs}
relevant_docs = {lang: {split: {}} for lang in langs}
for lang in langs:
data = datasets.load_dataset(
path,
lang,
split=split,
cache_dir=cache_dir,
revision=revision,
)
question_ids = {
question: _id for _id, question in enumerate(set(data["question"]))
}
answer_ids = {answer: _id for _id, answer in enumerate(set(data["answer"]))}
for row in data:
question = row["question"]
answer = row["answer"]
query_id = f"Q{question_ids[question]}"
queries[lang][split][query_id] = question
doc_id = f"D{answer_ids[answer]}"
corpus[lang][split][doc_id] = {"text": answer}
if query_id not in relevant_docs[lang][split]:
relevant_docs[lang][split][query_id] = {}
relevant_docs[lang][split][query_id][doc_id] = 1
corpus = datasets.DatasetDict(corpus)
queries = datasets.DatasetDict(queries)
relevant_docs = datasets.DatasetDict(relevant_docs)
return corpus, queries, relevant_docs
class MintakaRetrieval(MultilingualTask, AbsTaskRetrieval):
metadata = TaskMetadata(
name="MintakaRetrieval",
description="MintakaRetrieval",
reference=None,
dataset={
"path": "jinaai/mintakaqa",
"revision": "efa78cc2f74bbcd21eff2261f9e13aebe40b814e",
},
type="Retrieval",
category="s2p",
eval_splits=[_EVAL_SPLIT],
eval_langs=_LANGS,
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
self.corpus, self.queries, self.relevant_docs = _load_mintaka_data(
path=self.metadata_dict["dataset"]["path"],
langs=self.metadata.eval_langs,
split=self.metadata_dict["eval_splits"][0],
cache_dir=kwargs.get("cache_dir", None),
revision=self.metadata_dict["dataset"]["revision"],
)
self.data_loaded = True
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