{"id":616,"date":"2026-04-09T12:23:28","date_gmt":"2026-04-09T09:23:28","guid":{"rendered":"https:\/\/shareai.now\/?p=616"},"modified":"2026-04-14T03:21:11","modified_gmt":"2026-04-14T00:21:11","slug":"embeddinggemma-shareai-300m-embedding-model","status":"publish","type":"post","link":"https:\/\/shareai.now\/blog\/news\/embeddinggemma-shareai-300m-embedding-model\/","title":{"rendered":"EmbeddingGemma on ShareAI: 300M Multilingual Embeddings"},"content":{"rendered":"\n<h1 class=\"wp-block-heading\">EmbeddingGemma is now on ShareAI<\/h1>\n\n\n\n<p>We\u2019re announcing that <strong>EmbeddingGemma<\/strong>, Google\u2019s compact open embedding model, is now available on ShareAI.<\/p>\n\n\n\n<p>At <strong>300 million parameters<\/strong>, EmbeddingGemma delivers state-of-the-art performance for its size. It\u2019s built from <strong>Gemma 3<\/strong> with <strong>T5Gemma initialization<\/strong> and uses the same research and technology behind the <strong>Gemini<\/strong> models. The model produces vector representations of text, making it well-suited for search and retrieval tasks, including <strong>classification<\/strong>, <strong>clustering<\/strong>, and <strong>semantic similarity<\/strong>. It was trained with data in <strong>100+ spoken languages<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why it matters<\/h2>\n\n\n\n<p>The model\u2019s small size and on-device focus make it practical to deploy in environments with limited resources\u2014<strong>mobile phones, laptops, or desktops<\/strong>\u2014democratizing access to state-of-the-art AI models and fostering innovation for everyone.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Benchmark<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/shareai.now\/wp-content\/uploads\/2025\/09\/embeddinggemma-1024x576.png\" alt=\"\" class=\"wp-image-1547\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Training dataset<\/h2>\n\n\n\n<p>EmbeddingGemma was trained with data in 100+ spoken languages.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Web documents<\/strong><br>A diverse collection of web text ensures exposure to broad linguistic styles, topics, and vocabulary. The dataset includes content in <strong>100+ languages<\/strong>.<\/li>\n\n\n\n<li><strong>Code and technical documents<\/strong><br>Including programming languages and specialized scientific content helps the model learn structure and patterns that improve understanding of code and technical questions.<\/li>\n\n\n\n<li><strong>Synthetic and task-specific data<\/strong><br>Curated synthetic data teaches specific skills for information retrieval, classification, and sentiment analysis, fine-tuning performance for common embedding applications.<\/li>\n<\/ul>\n\n\n\n<p>This combination of diverse sources is crucial for a powerful multilingual embedding model that can handle a wide range of tasks and data formats.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What you can build<\/h2>\n\n\n\n<p>Use EmbeddingGemma for <strong>search and retrieval<\/strong>, <strong>semantic similarity<\/strong>, <strong>classification pipelines<\/strong>, and <strong>clustering<\/strong>\u2014especially when you need high-quality embeddings that can run on constrained devices.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">Reference<\/h3>\n\n\n\n<p><a href=\"https:\/\/ai.google.dev\/gemma\/docs\/embeddinggemma\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Documentation<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Available now on ShareAI.<\/strong><\/h2>\n\n\n\n<p>Run it. Test it. Ship it.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>EmbeddingGemma is now on ShareAI We\u2019re announcing that EmbeddingGemma, Google\u2019s compact open embedding model, is now available on ShareAI. At 300 million parameters, EmbeddingGemma delivers state-of-the-art performance for its size. It\u2019s built from Gemma 3 with T5Gemma initialization and uses the same research and technology behind the Gemini models. The model produces vector representations of [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":617,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Try EmbeddingGemma on ShareAI","cta-description":"Spin up the 300M multilingual embedding model in the ShareAI Playground or integrate it via API for search, similarity, and clustering.","cta-button-text":"Launch Playground","cta-button-link":"","rank_math_title":"EmbeddingGemma on ShareAI: 300M Multilingual Embeddings","rank_math_description":"EmbeddingGemma is now on ShareAI: a 300M open embedding model from Google for search, retrieval, clustering and semantic similarity\u2014multilingual, on-device.","rank_math_focus_keyword":"EmbeddingGemma","footnotes":""},"categories":[7],"tags":[],"class_list":["post-616","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/shareai.now\/api\/wp\/v2\/posts\/616","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/comments?post=616"}],"version-history":[{"count":3,"href":"https:\/\/shareai.now\/api\/wp\/v2\/posts\/616\/revisions"}],"predecessor-version":[{"id":2207,"href":"https:\/\/shareai.now\/api\/wp\/v2\/posts\/616\/revisions\/2207"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/media\/617"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/api\/wp\/v2\/media?parent=616"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/categories?post=616"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/tags?post=616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}