Kawn Embedding Model
The embedding model in Kawn plays a pivotal role in transforming Arabic texts into numerical representations (vectors) that enable precise understanding of meaning and context. These models serve as the backbone for Retrieval-Augmented Generation (RAG)techniques, semantic search engines, classification tasks, and contextual linking applications.
A high-quality, general-purpose Arabic embedding model has been developed and is available in multiple sizes to suit varying performance and computing needs—from cloud applications to on-device embedded solutions. Additionally, specialized domain-specific versions have been created to provide higher accuracy in critical fields such as legal, jurisprudential, and medical domains.
Key Features
Trained on diverse Arabic datasets
Compatible with LLMs to enable knowledge retrieval in RAG applications
Customizable and fine-tunable for specific domains
Use Cases
Semantic and contextual search systems within organizations
Powering LLMs with knowledge-augmented queries
Content classification
Question-answer matching in intelligent assistants
Document content analysis and indexing
