title: Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and
    Cross-Cultural Embedding Models and Benchmarks
summary: 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.
paper_authors._id: 6729ea15374c0ab99330f8b7
paper_authors.name: Gagan Bhatia
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-05T14:44:10.856Z
paper_authors._id: 60394599033b61166496163b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1614366097007-noauth.jpeg
paper_authors.fullname: Gagan Bhatia
paper_authors.type: user
paper_authors.user: gagan3012
paper_authors._id: 6729ea15374c0ab99330f8b8
paper_authors.name: El Moatez Billah Nagoudi
paper_authors._id: 6729ea15374c0ab99330f8b9
paper_authors.name: Abdellah El Mekki
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-05T14:44:22.207Z
paper_authors._id: 631650cb894404e2506aac43
paper_authors.avatarUrl: /avatars/d47134b29a968b71c5c9c2d65093945c.svg
paper_authors.fullname: Abdellah EL MEKKI
paper_authors.type: user
paper_authors.user: 3ebdola
paper_authors._id: 6729ea15374c0ab99330f8ba
paper_authors.name: Fakhraddin Alwajih
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-05T14:44:27.624Z
paper_authors._id: 6265c5fe5909b93b24d797f1
paper_authors.avatarUrl: /avatars/94f8e5f925c740d498c2709fe784a40b.svg
paper_authors.fullname: Fakhraddin Alwajih
paper_authors.type: user
paper_authors.user: Fakhraddin
paper_authors._id: 6729ea15374c0ab99330f8bb
paper_authors.name: Muhammad Abdul-Mageed
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-05T14:44:33.167Z
paper_authors._id: 5feeae8dd8e0b2d24ed258cc
paper_authors.avatarUrl: /avatars/1d66840db4ed6dc12bdee7ba8168ea9f.svg
paper_authors.fullname: Muhammad Abdul-Mageed
paper_authors.type: user
paper_authors.user: mageed
paper_title: Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and
    Cross-Cultural Embedding Models and Benchmarks
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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