Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and
  Cross-Cultural Embedding Models and Benchmarks
We introduce Swan, a family of embedding models centred around the Arabic
language, addressing both small-scale and large-scale use cases. Swan includes
two variants: Swan-Small, based on ARBERTv2, and Swan-Large, built on
ArMistral, a pretrained Arabic large language model. To evaluate these models,
we propose ArabicMTEB, a comprehensive benchmark suite that assesses
cross-lingual, multi-dialectal, multi-domain, and multi-cultural Arabic text
embedding performance, covering eight diverse tasks and spanning 94 datasets.
Swan-Large achieves state-of-the-art results, outperforming
Multilingual-E5-large in most Arabic tasks, while the Swan-Small consistently
surpasses Multilingual-E5 base. Our extensive evaluations demonstrate that Swan
models are both dialectally and culturally aware, excelling across various
Arabic domains while offering significant monetary efficiency. This work
significantly advances the field of Arabic language modelling and provides
valuable resources for future research and applications in Arabic natural
language processing. Our models and benchmark will be made publicly accessible
for research.
6729ea15374c0ab99330f8b7
Gagan Bhatia
admin_assigned
2024-11-05T14:44:10.856Z
60394599033b61166496163b
https://cdn-avatars.huggingface.co/v1/production/uploads/1614366097007-noauth.jpeg
Gagan Bhatia
user
gagan3012
6729ea15374c0ab99330f8b8
El Moatez Billah Nagoudi
6729ea15374c0ab99330f8b9
Abdellah El Mekki
admin_assigned
2024-11-05T14:44:22.207Z
631650cb894404e2506aac43
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Abdellah EL MEKKI
user
3ebdola
6729ea15374c0ab99330f8ba
Fakhraddin Alwajih
admin_assigned
2024-11-05T14:44:27.624Z
6265c5fe5909b93b24d797f1
/avatars/94f8e5f925c740d498c2709fe784a40b.svg
Fakhraddin Alwajih
user
Fakhraddin
6729ea15374c0ab99330f8bb
Muhammad Abdul-Mageed
admin_assigned
2024-11-05T14:44:33.167Z
5feeae8dd8e0b2d24ed258cc
/avatars/1d66840db4ed6dc12bdee7ba8168ea9f.svg
Muhammad Abdul-Mageed
user
mageed
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and
  Cross-Cultural Embedding Models and Benchmarks
We introduce Swan, a family of embedding models centred around the Arabic
language, addressing both small-scale and large-scale use cases. Swan includes
two variants: Swan-Small, based on ARBERTv2, and Swan-Large, built on
ArMistral, a pretrained Arabic large language model. To evaluate these models,
we propose ArabicMTEB, a comprehensive benchmark suite that assesses
cross-lingual, multi-dialectal, multi-domain, and multi-cultural Arabic text
embedding performance, covering eight diverse tasks and spanning 94 datasets.
Swan-Large achieves state-of-the-art results, outperforming
Multilingual-E5-large in most Arabic tasks, while the Swan-Small consistently
surpasses Multilingual-E5 base. Our extensive evaluations demonstrate that Swan
models are both dialectally and culturally aware, excelling across various
Arabic domains while offering significant monetary efficiency. This work
significantly advances the field of Arabic language modelling and provides
valuable resources for future research and applications in Arabic natural
language processing. Our models and benchmark will be made publicly accessible
for research.
Swan, a family of Arabic embedding models, achieves state-of-the-art performance in diverse Arabic text embedding tasks using comprehensive benchmarks, advancing Arabic language modeling.
embedding models ARBERTv2 ArMistral ArabicMTEB Multilingual-E5-large Multilingual-E5 base cross-lingual multi-dialectal multi-domain multi-cultural Arabic text embedding dialectal awareness cultural awareness Arabic natural language processing