{"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":"model-de-incorporare-embeddinggemma-shareai-300m","status":"publish","type":"post","link":"https:\/\/shareai.now\/ro\/blog\/stiri\/model-de-incorporare-embeddinggemma-shareai-300m\/","title":{"rendered":"EmbeddingGemma pe ShareAI: 300M \u00cencapsul\u0103ri Multilingve"},"content":{"rendered":"<h1 class=\"wp-block-heading\">EmbeddingGemma este acum pe ShareAI<\/h1>\n\n\n\n<p>Anun\u021b\u0103m c\u0103 <strong>\u00cencorporareGemma<\/strong>, modelul compact de embedding deschis al Google, este acum disponibil pe ShareAI.<\/p>\n\n\n\n<p>La <strong>300 de milioane de parametri<\/strong>, EmbeddingGemma ofer\u0103 performan\u021b\u0103 de ultim\u0103 genera\u021bie pentru dimensiunea sa. Este construit pe baza <strong>Gemma 3<\/strong> cu <strong>ini\u021bializ\u0103rii T5Gemma<\/strong> \u0219i folose\u0219te aceea\u0219i cercetare \u0219i tehnologie din spatele modelelor <strong>Gemeni<\/strong> . Modelul produce reprezent\u0103ri vectoriale ale textului, ceea ce \u00eel face potrivit pentru sarcini de c\u0103utare \u0219i reg\u0103sire, inclusiv <strong>clasificare<\/strong>, <strong>grupare<\/strong>, \u0219i <strong>similaritate semantic\u0103<\/strong>. A fost antrenat cu date \u00een <strong>Peste 100 de limbi vorbite<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">De ce este important<\/h2>\n\n\n\n<p>Dimensiunea mic\u0103 a modelului \u0219i concentrarea pe dispozitive \u00eel fac practic de implementat \u00een medii cu resurse limitate\u2014<strong>telefoane mobile, laptopuri sau desktopuri<\/strong>\u2014democratiz\u00e2nd accesul la modele AI de ultim\u0103 genera\u021bie \u0219i \u00eencuraj\u00e2nd inova\u021bia pentru toat\u0103 lumea.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Reper<\/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\">Set de date de antrenament<\/h2>\n\n\n\n<p>EmbeddingGemma a fost antrenat cu date \u00een peste 100 de limbi vorbite.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Documente web<\/strong><br>O colec\u021bie divers\u0103 de texte web asigur\u0103 expunerea la stiluri lingvistice, subiecte \u0219i vocabular variate. Setul de date include con\u021binut \u00een <strong>Peste 100 de limbi<\/strong>.<\/li>\n\n\n\n<li><strong>Cod \u0219i documente tehnice<\/strong><br>Includerea limbajelor de programare \u0219i a con\u021binutului \u0219tiin\u021bific specializat ajut\u0103 modelul s\u0103 \u00eenve\u021be structura \u0219i tiparele care \u00eembun\u0103t\u0103\u021besc \u00een\u021belegerea codului \u0219i a \u00eentreb\u0103rilor tehnice.<\/li>\n\n\n\n<li><strong>Date sintetice \u0219i specifice sarcinilor<\/strong><br>Datele sintetice selectate \u00eenva\u021b\u0103 abilit\u0103\u021bi specifice pentru reg\u0103sirea informa\u021biilor, clasificare \u0219i analiz\u0103 a sentimentelor, optimiz\u00e2nd performan\u021ba pentru aplica\u021biile comune de \u00eencorporare.<\/li>\n<\/ul>\n\n\n\n<p>Aceast\u0103 combina\u021bie de surse diverse este crucial\u0103 pentru un model puternic de \u00eencorporare multilingv care poate gestiona o gam\u0103 larg\u0103 de sarcini \u0219i formate de date.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Ce po\u021bi construi<\/h2>\n\n\n\n<p>Utilizeaz\u0103 EmbeddingGemma pentru <strong>c\u0103utare \u0219i reg\u0103sire<\/strong>, <strong>similaritate semantic\u0103<\/strong>, <strong>fluxuri de clasificare<\/strong>, \u0219i <strong>grupare<\/strong>\u2014mai ales c\u00e2nd ai nevoie de \u00eencorpor\u0103ri de \u00eenalt\u0103 calitate care pot rula pe dispozitive cu resurse limitate.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">Referin\u021b\u0103<\/h3>\n\n\n\n<p><a href=\"https:\/\/ai.google.dev\/gemma\/docs\/embeddinggemma\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Documenta\u021bia<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Disponibil acum pe ShareAI.<\/strong><\/h2>\n\n\n\n<p>Ruleaz\u0103-l. Testeaz\u0103-l. Livreaz\u0103-l.<\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>EmbeddingGemma este acum pe ShareAI Anun\u021b\u0103m c\u0103 EmbeddingGemma, modelul compact de \u00eencorporare deschis de la Google, este acum disponibil pe ShareAI.<\/p>","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\/ro\/api\/wp\/v2\/posts\/616","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/comments?post=616"}],"version-history":[{"count":3,"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/posts\/616\/revisions"}],"predecessor-version":[{"id":2207,"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/posts\/616\/revisions\/2207"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/media\/617"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/media?parent=616"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/categories?post=616"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/ro\/api\/wp\/v2\/tags?post=616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}