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import datasets
from mteb.abstasks import AbsTaskRetrieval, CrosslingualTask, TaskMetadata
_LANGUAGES = {
"mlqa.ar.ar": ["ara-Arab", "ara-Arab"],
"mlqa.ar.de": ["ara-Arab", "deu-Latn"],
"mlqa.ar.en": ["ara-Arab", "eng-Latn"],
"mlqa.ar.es": ["ara-Arab", "spa-Latn"],
"mlqa.ar.hi": ["ara-Arab", "hin-Deva"],
"mlqa.ar.vi": ["ara-Arab", "vie-Latn"],
"mlqa.ar.zh": ["ara-Arab", "zho-Hans"],
"mlqa.de.ar": ["deu-Latn", "ara-Arab"],
"mlqa.de.de": ["deu-Latn", "deu-Latn"],
"mlqa.de.en": ["deu-Latn", "eng-Latn"],
"mlqa.de.es": ["deu-Latn", "spa-Latn"],
"mlqa.de.hi": ["deu-Latn", "hin-Deva"],
"mlqa.de.vi": ["deu-Latn", "vie-Latn"],
"mlqa.de.zh": ["deu-Latn", "zho-Hans"],
"mlqa.en.ar": ["eng-Latn", "ara-Arab"],
"mlqa.en.de": ["eng-Latn", "deu-Latn"],
"mlqa.en.en": ["eng-Latn", "eng-Latn"],
"mlqa.en.es": ["eng-Latn", "spa-Latn"],
"mlqa.en.hi": ["eng-Latn", "hin-Deva"],
"mlqa.en.vi": ["eng-Latn", "vie-Latn"],
"mlqa.en.zh": ["eng-Latn", "zho-Hans"],
"mlqa.es.ar": ["spa-Latn", "ara-Arab"],
"mlqa.es.de": ["spa-Latn", "deu-Latn"],
"mlqa.es.en": ["spa-Latn", "eng-Latn"],
"mlqa.es.es": ["spa-Latn", "spa-Latn"],
"mlqa.es.hi": ["spa-Latn", "hin-Deva"],
"mlqa.es.vi": ["spa-Latn", "vie-Latn"],
"mlqa.es.zh": ["spa-Latn", "zho-Hans"],
"mlqa.hi.ar": ["hin-Deva", "ara-Arab"],
"mlqa.hi.de": ["hin-Deva", "deu-Latn"],
"mlqa.hi.en": ["hin-Deva", "eng-Latn"],
"mlqa.hi.es": ["hin-Deva", "spa-Latn"],
"mlqa.hi.hi": ["hin-Deva", "hin-Deva"],
"mlqa.hi.vi": ["hin-Deva", "vie-Latn"],
"mlqa.hi.zh": ["hin-Deva", "zho-Hans"],
"mlqa.vi.ar": ["vie-Latn", "ara-Arab"],
"mlqa.vi.de": ["vie-Latn", "deu-Latn"],
"mlqa.vi.en": ["vie-Latn", "eng-Latn"],
"mlqa.vi.es": ["vie-Latn", "spa-Latn"],
"mlqa.vi.hi": ["vie-Latn", "hin-Deva"],
"mlqa.vi.vi": ["vie-Latn", "vie-Latn"],
"mlqa.vi.zh": ["vie-Latn", "zho-Hans"],
"mlqa.zh.ar": ["zho-Hans", "ara-Arab"],
"mlqa.zh.de": ["zho-Hans", "deu-Latn"],
"mlqa.zh.en": ["zho-Hans", "eng-Latn"],
"mlqa.zh.es": ["zho-Hans", "spa-Latn"],
"mlqa.zh.hi": ["zho-Hans", "hin-Deva"],
"mlqa.zh.vi": ["zho-Hans", "vie-Latn"],
"mlqa.zh.zh": ["zho-Hans", "zho-Hans"],
}
def _build_lang_pair(langs: List[str]) -> str:
"""Builds a language pair separated by a dash.
e.g., ['eng-Latn', 'deu-Latn'] -> 'eng-deu'.
"""
return langs[0].split("-")[0] + "-" + langs[1].split("-")[0]
def extend_lang_pairs() -> Dict[str, List[str]]:
eval_langs = {}
for langs in _LANGUAGES.values():
lang_pair = _build_lang_pair(langs)
eval_langs[lang_pair] = langs
return eval_langs
_EVAL_LANGS = extend_lang_pairs()
class MLQARetrieval(AbsTaskRetrieval, CrosslingualTask):
metadata = TaskMetadata(
name="MLQARetrieval",
description="""MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.""",
reference="https://huggingface.co/datasets/mlqa",
dataset={
"path": "facebook/mlqa",
"revision": "397ed406c1a7902140303e7faf60fff35b58d285",
},
type="Retrieval",
category="s2p",
eval_splits=["validation", "test"],
eval_langs=_EVAL_LANGS,
main_score="ndcg_at_10",
date=("2019-01-01", "2020-12-31"),
form=["written"],
domains=["Encyclopaedic"],
task_subtypes=["Question answering"],
license="cc-by-sa-3.0",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@article{lewis2019mlqa,
title = {MLQA: Evaluating Cross-lingual Extractive Question Answering},
author = {Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
journal = {arXiv preprint arXiv:1910.07475},
year = 2019,
eid = {arXiv: 1910.07475}
}""",
n_samples={"test": 158083, "validation": 15747},
avg_character_length={
"test": 37352.28,
"validation": 36952.7,
}, # avergae context lengths
)
def load_data(self, **kwargs):
"""In this retrieval datasets, corpus is in lang XX and queries in lang YY."""
if self.data_loaded:
return
_dataset_raw = {}
self.queries, self.corpus, self.relevant_docs = {}, {}, {}
for hf_subset, langs in _LANGUAGES.items():
# Builds a language pair separated by an underscore. e.g., "ara-Arab_eng-Latn".
# Corpus is in ara-Arab and queries in eng-Latn
lang_pair = _build_lang_pair(langs)
_dataset_raw[lang_pair] = datasets.load_dataset(
name=hf_subset,
**self.metadata_dict["dataset"],
)
_dataset_raw[lang_pair] = _dataset_raw[lang_pair].rename_column(
"context", "text"
)
self.queries[lang_pair] = {
eval_split: {
str(i): q["question"]
for i, q in enumerate(_dataset_raw[lang_pair][eval_split])
}
for eval_split in self.metadata_dict["eval_splits"]
}
self.corpus[lang_pair] = {
eval_split: {
str(row["id"]): row for row in _dataset_raw[lang_pair][eval_split]
}
for eval_split in self.metadata_dict["eval_splits"]
}
self.relevant_docs[lang_pair] = {
eval_split: {
str(i): {str(q["id"]): 1}
for i, q in enumerate(_dataset_raw[lang_pair][eval_split])
}
for eval_split in self.metadata_dict["eval_splits"]
}
self.data_loaded = True
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