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README.md

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 # SparkEmbedding-300m Model Card
 
 ### Description
-SparkEmbedding-300m is a 300 million parameter multilingual text embedding model developed by the XenArcAI team. Fine-tuned from Google's EmbeddingGemma-300m, it incorporates an additional 1 million curated samples across 119 languages, emphasizing data complexity, linguistic diversity, and deep language understanding. This optimization enhances cross-lingual retrieval, producing embeddings with superior semantic alignment and efficacy in multilingual settings.
+SparkEmbedding-300m is a 300 million parameter multilingual text embedding model with **SoTA cross‑lingual retrieval** developed by the XenArcAI team. Fine-tuned from Google's EmbeddingGemma-300m, it incorporates an additional 1 million curated samples across 119 languages, emphasizing data complexity, linguistic diversity, and deep language understanding. This optimization enhances cross-lingual retrieval, producing embeddings with superior semantic alignment and efficacy in multilingual settings.
 
 The model generates high-dimensional vector representations capturing rich semantic and contextual information, excelling in bridging linguistic gaps for applications like global information retrieval, multilingual question answering, and cross-language semantic search. With a native 2048-token context window, it handles extended inputs (e.g., full articles or documents) while preserving long-range dependencies.