title: GATE: General Arabic Text Embedding for Enhanced Semantic Textual
    Similarity with Matryoshka Representation Learning and Hybrid Loss Training
summary: Semantic textual similarity (STS) is a critical task in natural language
  processing (NLP), enabling applications in retrieval, clustering, and
  understanding semantic relationships between texts. However, research in this
  area for the Arabic language remains limited due to the lack of high-quality
  datasets and pre-trained models. This scarcity of resources has restricted the
  accurate evaluation and advance of semantic similarity in Arabic text. This
  paper introduces General Arabic Text Embedding (GATE) models that achieve
  state-of-the-art performance on the Semantic Textual Similarity task within the
  MTEB benchmark. GATE leverages Matryoshka Representation Learning and a hybrid
  loss training approach with Arabic triplet datasets for Natural Language
  Inference, which are essential for enhancing model performance in tasks that
  demand fine-grained semantic understanding. GATE outperforms larger models,
  including OpenAI, with a 20-25% performance improvement on STS benchmarks,
  effectively capturing the unique semantic nuances of Arabic.
paper_authors._id: 683d7ba30ba4502a904432b3
paper_authors.name: Omer Nacar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:49:36.827Z
paper_authors._id: 628f7a71dd993507cfcbe587
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/628f7a71dd993507cfcbe587/3frAcMgfBCx-OJsrdAhnb.png
paper_authors.fullname: Omartificial Intelligence Space
paper_authors.type: user
paper_authors.user: Omartificial-Intelligence-Space
paper_authors._id: 683d7ba30ba4502a904432b4
paper_authors.name: Anis Koubaa
paper_authors._id: 683d7ba30ba4502a904432b5
paper_authors.name: Serry Sibaee
paper_authors._id: 683d7ba30ba4502a904432b6
paper_authors.name: Yasser Al-Habashi
paper_authors._id: 683d7ba30ba4502a904432b7
paper_authors.name: Adel Ammar
paper_authors._id: 683d7ba30ba4502a904432b8
paper_authors.name: Wadii Boulila
paper_title: GATE: General Arabic Text Embedding for Enhanced Semantic Textual
    Similarity with Matryoshka Representation Learning and Hybrid Loss Training
paper_summary: Semantic textual similarity (STS) is a critical task in natural language
  processing (NLP), enabling applications in retrieval, clustering, and
  understanding semantic relationships between texts. However, research in this
  area for the Arabic language remains limited due to the lack of high-quality
  datasets and pre-trained models. This scarcity of resources has restricted the
  accurate evaluation and advance of semantic similarity in Arabic text. This
  paper introduces General Arabic Text Embedding (GATE) models that achieve
  state-of-the-art performance on the Semantic Textual Similarity task within the
  MTEB benchmark. GATE leverages Matryoshka Representation Learning and a hybrid
  loss training approach with Arabic triplet datasets for Natural Language
  Inference, which are essential for enhancing model performance in tasks that
  demand fine-grained semantic understanding. GATE outperforms larger models,
  including OpenAI, with a 20-25% performance improvement on STS benchmarks,
  effectively capturing the unique semantic nuances of Arabic.
paper_ai_summary: GATE models using Matryoshka Representation Learning and a hybrid loss approach achieve state-of-the-art performance on Arabic Semantic Textual Similarity benchmarks.
paper_ai_keywords: Matryoshka Representation Learning hybrid loss training triplet datasets Natural Language Inference Semantic Textual Similarity STS benchmarks