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import datasets
from mteb.abstasks import AbsTaskRetrieval, CrosslingualTask, TaskMetadata
_EVAL_LANGS = {
"ara-ara": ["ara-Arab", "ara-Arab"],
"eng-ara": ["eng-Latn", "ara-Arab"],
"ara-eng": ["ara-Arab", "eng-Latn"],
"deu-deu": ["deu-Latn", "deu-Latn"],
"eng-deu": ["eng-Latn", "deu-Latn"],
"deu-eng": ["deu-Latn", "eng-Latn"],
"spa-spa": ["spa-Latn", "spa-Latn"],
"eng-spa": ["eng-Latn", "spa-Latn"],
"spa-eng": ["spa-Latn", "eng-Latn"],
"fra-fra": ["fra-Latn", "fra-Latn"],
"eng-fra": ["eng-Latn", "fra-Latn"],
"fra-eng": ["fra-Latn", "eng-Latn"],
"hin-hin": ["hin-Deva", "hin-Deva"],
"eng-hin": ["eng-Latn", "hin-Deva"],
"hin-eng": ["hin-Deva", "eng-Latn"],
"ita-ita": ["ita-Latn", "ita-Latn"],
"eng-ita": ["eng-Latn", "ita-Latn"],
"ita-eng": ["ita-Latn", "eng-Latn"],
"jpn-jpn": ["jpn-Hira", "jpn-Hira"],
"eng-jpn": ["eng-Latn", "jpn-Hira"],
"jpn-eng": ["jpn-Hira", "eng-Latn"],
"kor-kor": ["kor-Hang", "kor-Hang"],
"eng-kor": ["eng-Latn", "kor-Hang"],
"kor-eng": ["kor-Hang", "eng-Latn"],
"pol-pol": ["pol-Latn", "pol-Latn"],
"eng-pol": ["eng-Latn", "pol-Latn"],
"pol-eng": ["pol-Latn", "eng-Latn"],
"por-por": ["por-Latn", "por-Latn"],
"eng-por": ["eng-Latn", "por-Latn"],
"por-eng": ["por-Latn", "eng-Latn"],
"tam-tam": ["tam-Taml", "tam-Taml"],
"eng-tam": ["eng-Latn", "tam-Taml"],
"tam-eng": ["tam-Taml", "eng-Latn"],
"cmn-cmn": ["cmn-Hans", "cmn-Hans"],
"eng-cmn": ["eng-Latn", "cmn-Hans"],
"cmn-eng": ["cmn-Hans", "eng-Latn"],
}
_LANG_CONVERSION = {
"ara": "ar",
"deu": "de",
"spa": "es",
"fra": "fr",
"hin": "hi",
"ita": "it",
"jpn": "ja",
"kor": "ko",
"pol": "pl",
"por": "pt",
"tam": "ta",
"cmn": "zh",
"eng": "en",
}
class XPQARetrieval(AbsTaskRetrieval, CrosslingualTask):
metadata = TaskMetadata(
name="XPQARetrieval",
description="XPQARetrieval",
reference="https://arxiv.org/abs/2305.09249",
dataset={
"path": "jinaai/xpqa",
"revision": "c99d599f0a6ab9b85b065da6f9d94f9cf731679f",
},
type="Retrieval",
category="s2p",
eval_splits=["test"],
eval_langs=_EVAL_LANGS,
main_score="ndcg_at_10",
date=("2022-01-01", "2023-07-31"), # best guess
form=["written"],
domains=["Reviews"],
task_subtypes=["Question answering"],
license="CDLA-Sharing-1.0",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@inproceedings{shen2023xpqa,
title={xPQA: Cross-Lingual Product Question Answering in 12 Languages},
author={Shen, Xiaoyu and Asai, Akari and Byrne, Bill and De Gispert, Adria},
booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)},
pages={103--115},
year={2023}
}""",
n_samples={"test": 19801},
avg_character_length={"test": 104.68}, # answer
)
def load_data(self, **kwargs):
if self.data_loaded:
return
path = self.metadata_dict["dataset"]["path"]
revision = self.metadata_dict["dataset"]["revision"]
eval_splits = self.metadata_dict["eval_splits"]
dataset = _load_dataset_csv(path, revision, eval_splits)
self.queries, self.corpus, self.relevant_docs = {}, {}, {}
for lang_pair, _ in self.metadata.eval_langs.items():
lang_corpus, lang_question = (
lang_pair.split("-")[0],
lang_pair.split("-")[1],
)
lang_not_english = lang_corpus if lang_corpus != "eng" else lang_question
dataset_language = dataset.filter(
lambda x: x["lang"] == _LANG_CONVERSION.get(lang_not_english)
)
question_key = "question_en" if lang_question == "eng" else "question"
corpus_key = "candidate" if lang_corpus == "eng" else "answer"
queries_to_ids = {
eval_split: {
q: str(_id)
for _id, q in enumerate(
set(dataset_language[eval_split][question_key])
)
}
for eval_split in eval_splits
}
self.queries[lang_pair] = {
eval_split: {v: k for k, v in queries_to_ids[eval_split].items()}
for eval_split in eval_splits
}
corpus_to_ids = {
eval_split: {
document: str(_id)
for _id, document in enumerate(
set(dataset_language[eval_split][corpus_key])
)
}
for eval_split in eval_splits
}
self.corpus[lang_pair] = {
eval_split: {
v: {"text": k} for k, v in corpus_to_ids[eval_split].items()
}
for eval_split in eval_splits
}
self.relevant_docs[lang_pair] = {}
for eval_split in eval_splits:
self.relevant_docs[lang_pair][eval_split] = {}
for example in dataset_language[eval_split]:
query_id = queries_to_ids[eval_split].get(example[question_key])
document_id = corpus_to_ids[eval_split].get(example[corpus_key])
if query_id in self.relevant_docs[lang_pair][eval_split]:
self.relevant_docs[lang_pair][eval_split][query_id][
document_id
] = 1
else:
self.relevant_docs[lang_pair][eval_split][query_id] = {
document_id: 1
}
self.data_loaded = True
def _load_dataset_csv(path: str, revision: str, eval_splits: List[str]):
data_files = {
eval_split: f"https://huggingface.co/datasets/{path}/resolve/{revision}/{eval_split}.csv"
for eval_split in eval_splits
}
dataset = datasets.load_dataset("csv", data_files=data_files)
dataset = dataset.filter(lambda x: x["answer"] is not None)
return dataset
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