File size: 2,568 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 | from __future__ import annotations
from datasets import DatasetDict, load_dataset
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
_EVAL_SPLIT = "test"
class GeorgianFAQRetrieval(AbsTaskRetrieval):
metadata = TaskMetadata(
name="GeorgianFAQRetrieval",
dataset={
"path": "jupyterjazz/georgian-faq",
"revision": "2436d9bda047a80959b034a572fdda4d00c80d2e",
},
description=(
"Frequently asked questions (FAQs) and answers mined from Georgian websites via Common Crawl."
),
type="Retrieval",
category="s2p",
eval_splits=["test"],
eval_langs=["kat-Geor"],
main_score="ndcg_at_10",
domains=["Web"],
text_creation="created",
n_samples={_EVAL_SPLIT: 2566},
reference="https://huggingface.co/datasets/jupyterjazz/georgian-faq",
date=("2024-05-02", "2024-05-03"),
form=["written"],
task_subtypes=["Question answering"],
license="Not specified",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
bibtex_citation="",
avg_character_length={_EVAL_SPLIT: 572},
)
def load_data(self, **kwargs):
if self.data_loaded:
return
queries = {_EVAL_SPLIT: {}}
corpus = {_EVAL_SPLIT: {}}
relevant_docs = {_EVAL_SPLIT: {}}
data = load_dataset(
self.metadata_dict["dataset"]["path"],
split=_EVAL_SPLIT,
cache_dir=kwargs.get("cache_dir", None),
revision=self.metadata_dict["dataset"]["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[_EVAL_SPLIT][query_id] = question
doc_id = f"D{answer_ids[answer]}"
corpus[_EVAL_SPLIT][doc_id] = {"text": answer}
if query_id not in relevant_docs[_EVAL_SPLIT]:
relevant_docs[_EVAL_SPLIT][query_id] = {}
relevant_docs[_EVAL_SPLIT][query_id][doc_id] = 1
self.corpus = DatasetDict(corpus)
self.queries = DatasetDict(queries)
self.relevant_docs = DatasetDict(relevant_docs)
self.data_loaded = True
|