{"id":104547,"date":"2025-05-30T07:07:12","date_gmt":"2025-05-30T07:07:12","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%85%d8%a7%d8%aa%d8%b1%db%8c%d9%88%d8%b4%da%a9%d8%a7-%d8%a2%d8%af%d8%a7%d9%be%d8%aa%d9%88%d8%b1-%d8%aa\/"},"modified":"2025-05-30T07:07:12","modified_gmt":"2025-05-30T07:07:12","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%85%d8%a7%d8%aa%d8%b1%db%8c%d9%88%d8%b4%da%a9%d8%a7-%d8%a2%d8%af%d8%a7%d9%be%d8%aa%d9%88%d8%b1-%d8%aa","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%85%d8%a7%d8%aa%d8%b1%db%8c%d9%88%d8%b4%da%a9%d8%a7-%d8%a2%d8%af%d8%a7%d9%be%d8%aa%d9%88%d8%b1-%d8%aa\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0645\u0627\u062a\u0631\u06cc\u0648\u0634\u06a9\u0627-\u0622\u062f\u0627\u067e\u062a\u0648\u0631: \u062a\u0646\u0638\u06cc\u0645 \u0628\u062f\u0648\u0646 \u0646\u0638\u0627\u0631\u062a \u0648 \u0646\u0638\u0627\u0631\u062a\u200c\u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0627\u0628\u0639\u0627\u062f \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0648\u0686\u06a9\u200c\u062a\u0631"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nMatryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0645\u0627\u062a\u0631\u06cc\u0648\u0634\u06a9\u0627-\u0622\u062f\u0627\u067e\u062a\u0648\u0631: \u062a\u0646\u0638\u06cc\u0645 \u0628\u062f\u0648\u0646 \u0646\u0638\u0627\u0631\u062a \u0648 \u0646\u0638\u0627\u0631\u062a\u200c\u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0627\u0628\u0639\u0627\u062f \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0648\u0686\u06a9\u200c\u062a\u0631<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nJinsung Yoon, Raj Sinha, Sercan O Arik, Tomas Pfister<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPDF<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u062d\u0648\u06cc\u0644\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n
\u0646\u062d\u0648\u0647 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 <\/td>\n\u062f\u0648 \u062a\u0627 \u0633\u0647 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 (\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc)<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n19<\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nComputation and Language,Machine Learning,\u0645\u062d\u0627\u0633\u0628\u0647 \u0648 \u0632\u0628\u0627\u0646 , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 17 July, 2024; originally announced July 2024.<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 17 \u0698\u0648\u0626\u06cc\u0647 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0698\u0648\u0626\u06cc\u0647 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u06af\u0632\u06cc\u0646\u0647\u200c\u0647\u0627\u06cc \u062e\u0631\u06cc\u062f<\/h3>\n
\n

\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647 \u0627\u0635\u0644 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/h4>\n

\u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0627\u06cc\u0646 \u06af\u0632\u06cc\u0646\u0647\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0641\u0627\u06cc\u0644 PDF \u0645\u0642\u0627\u0644\u0647 \u0627\u0635\u0644\u06cc \u0631\u0627 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u0646\u06cc\u062f.<\/p>\n

\u0642\u06cc\u0645\u062a: 19,000 \u062a\u0648\u0645\u0627\u0646<\/p>\n<\/p><\/div>\n

\n

\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647 \u0627\u0635\u0644 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc + \u062e\u0644\u0627\u0635\u0647 \u062f\u0648 \u0635\u0641\u062d\u0647 \u0627\u06cc \u0645\u0642\u0627\u0644\u0647 + \u067e\u0627\u062f\u06a9\u0633\u062a \u0635\u0648\u062a\u06cc \u0641\u0627\u0631\u0633\u06cc \u062e\u0644\u0627\u0635\u0647 \u0645\u0642\u0627\u0644\u0647<\/h4>\n

\u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0627\u06cc\u0646 \u06af\u0632\u06cc\u0646\u0647\u060c \u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u062f\u0631\u06cc\u0627\u0641\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0635\u0644\u06cc\u060c \u06cc\u06a9 \u062e\u0644\u0627\u0635\u0647 \u062f\u0648 \u0635\u0641\u062d\u0647\u200c\u0627\u06cc \u0641\u0627\u0631\u0633\u06cc \u0648 \u067e\u0627\u062f\u06a9\u0633\u062a \u0635\u0648\u062a\u06cc \u0641\u0627\u0631\u0633\u06cc \u062e\u0644\u0627\u0635\u0647 \u0645\u0642\u0627\u0644\u0647 \u0631\u0627 \u0646\u06cc\u0632 \u062f\u0631\u06cc\u0627\u0641\u062a \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f.<\/p>\n

\u0642\u06cc\u0645\u062a: 99,000 \u062a\u0648\u0645\u0627\u0646<\/p>\n<\/p><\/div>\n

\n

\u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 + \u062e\u0644\u0627\u0635\u0647 \u062f\u0648 \u0635\u0641\u062d\u0647 \u0627\u06cc \u0645\u0642\u0627\u0644\u0647 + \u067e\u0627\u062f\u06a9\u0633\u062a \u0635\u0648\u062a\u06cc \u0641\u0627\u0631\u0633\u06cc \u062e\u0644\u0627\u0635\u0647 \u0645\u0642\u0627\u0644\u0647<\/h4>\n

\u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0627\u06cc\u0646 \u06af\u0632\u06cc\u0646\u0647\u060c \u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u062f\u0631\u06cc\u0627\u0641\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0635\u0644\u06cc \u0648 \u062a\u0631\u062c\u0645\u0647 \u06a9\u0627\u0645\u0644 \u0622\u0646\u060c \u06cc\u06a9 \u062e\u0644\u0627\u0635\u0647 \u062f\u0648 \u0635\u0641\u062d\u0647\u200c\u0627\u06cc \u0641\u0627\u0631\u0633\u06cc \u0648 \u067e\u0627\u062f\u06a9\u0633\u062a \u0635\u0648\u062a\u06cc \u0641\u0627\u0631\u0633\u06cc \u062e\u0644\u0627\u0635\u0647 \u0645\u0642\u0627\u0644\u0647 \u0631\u0627 \u0646\u06cc\u0632 \u062f\u0631\u06cc\u0627\u0641\u062a \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f.<\/p>\n

\u0642\u06cc\u0645\u062a: 760,000 \u062a\u0648\u0645\u0627\u0646<\/p>\n

\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644: 2 \u062a\u0627 3 \u0631\u0648\u0632 \u06a9\u0627\u0631\u06cc<\/p>\n<\/p><\/div>\n<\/p><\/div>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

Embeddings from Large Language Models (LLMs) have emerged as critical components in various applications, particularly for information retrieval. While high-dimensional embeddings generally demonstrate superior performance as they contain more salient information, their practical application is frequently hindered by elevated computational latency and the associated higher cost. To address these challenges, we propose Matryoshka-Adaptor, a novel tuning framework designed for the customization of LLM embeddings. Matryoshka-Adaptor facilitates substantial dimensionality reduction while maintaining comparable performance levels, thereby achieving a significant enhancement in computational efficiency and cost-effectiveness. Our framework directly modifies the embeddings from pre-trained LLMs which is designed to be seamlessly integrated with any LLM architecture, encompassing those accessible exclusively through black-box APIs. Also, it exhibits efficacy in both unsupervised and supervised learning settings. A rigorous evaluation conducted across a diverse corpus of English, multilingual, and multimodal datasets consistently reveals substantial gains with Matryoshka-Adaptor. Notably, with Google and OpenAI Embedding APIs, Matryoshka-Adaptor achieves a reduction in dimensionality ranging from two- to twelve-fold without compromising performance across multiple BEIR datasets.<\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n

\u062a\u0639\u0628\u06cc\u0647 \u0627\u0632 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u0632\u0628\u0627\u0646 (LLMS) \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u0624\u0644\u0641\u0647 \u0647\u0627\u06cc \u0645\u0647\u0645 \u062f\u0631 \u0628\u0631\u0646\u0627\u0645\u0647 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u060c \u0628\u0647 \u0648\u06cc\u0698\u0647 \u0628\u0631\u0627\u06cc \u0628\u0627\u0632\u06cc\u0627\u0628\u06cc \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0638\u0627\u0647\u0631 \u0634\u062f\u0647 \u0627\u0633\u062a.\u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062a\u0639\u0628\u06cc\u0647\u0627\u062a \u0628\u0627 \u0627\u0628\u0639\u0627\u062f \u0628\u0627\u0644\u0627 \u0639\u0645\u0648\u0645\u0627\u064b \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0631\u062a\u0631 \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u0632\u06cc\u0631\u0627 \u062d\u0627\u0648\u06cc \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u0631\u062c\u0633\u062a\u0647 \u062a\u0631 \u0627\u0633\u062a \u060c \u06a9\u0627\u0631\u0628\u0631\u062f \u0639\u0645\u0644\u06cc \u0622\u0646\u0647\u0627 \u063a\u0627\u0644\u0628\u0627\u064b \u0628\u0627 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062a\u0623\u062e\u06cc\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0648 \u0647\u0632\u06cc\u0646\u0647 \u0628\u0627\u0644\u0627\u062a\u0631 \u0645\u0631\u062a\u0628\u0637 \u0645\u06cc \u0634\u0648\u062f.\u0628\u0631\u0627\u06cc \u067e\u0631\u062f\u0627\u062e\u062a\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u0686\u0627\u0644\u0634 \u0647\u0627 \u060c \u0645\u0627 Matryoshka-Adaptor \u0631\u0627 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u060c \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u062a\u0646\u0638\u06cc\u0645 \u062c\u062f\u06cc\u062f \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634\u06cc \u0633\u0627\u0632\u06cc \u062a\u0639\u0628\u06cc\u0647 LLM \u0637\u0631\u0627\u062d\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a.Matryoshka-adaptor \u0636\u0645\u0646 \u062d\u0641\u0638 \u0633\u0637\u062d \u0639\u0645\u0644\u06a9\u0631\u062f \u0642\u0627\u0628\u0644 \u0645\u0642\u0627\u06cc\u0633\u0647 \u060c \u06a9\u0627\u0647\u0634 \u0628\u0639\u062f \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0631\u0627 \u062a\u0633\u0647\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f \u060c \u062f\u0631 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0633\u062a\u06cc\u0627\u0628\u06cc \u0628\u0647 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u062f\u0631 \u0631\u0627\u0646\u062f\u0645\u0627\u0646 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0648 \u0645\u0642\u0631\u0648\u0646 \u0628\u0647 \u0635\u0631\u0641\u0647 \u0628\u0648\u062f\u0646.\u0686\u0627\u0631\u0686\u0648\u0628 \u0645\u0627 \u0645\u0633\u062a\u0642\u06cc\u0645\u0627\u064b \u062a\u0639\u0628\u06cc\u0647 \u0634\u062f\u0647 \u0627\u0632 LLM \u0647\u0627\u06cc \u0627\u0632 \u0642\u0628\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u0628\u0647 \u06af\u0648\u0646\u0647 \u0627\u06cc \u0637\u0631\u0627\u062d\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0627 \u0647\u0631 \u0645\u0639\u0645\u0627\u0631\u06cc LLM \u06cc\u06a9\u067e\u0627\u0631\u0686\u0647 \u0627\u062f\u063a\u0627\u0645 \u0634\u0648\u062f \u060c \u0648 \u0634\u0627\u0645\u0644 \u0645\u0648\u0627\u0631\u062f\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0628\u0647 \u0637\u0648\u0631 \u0627\u0646\u062d\u0635\u0627\u0631\u06cc \u0627\u0632 \u0637\u0631\u06cc\u0642 API \u0647\u0627\u06cc \u062c\u0639\u0628\u0647 \u0633\u06cc\u0627\u0647 \u0642\u0627\u0628\u0644 \u062f\u0633\u062a\u0631\u0633\u06cc \u0627\u0633\u062a.\u0647\u0645\u0686\u0646\u06cc\u0646 \u060c \u062f\u0631 \u0647\u0631 \u062f\u0648 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u062f\u0648\u0646 \u0646\u0638\u0627\u0631\u062a \u0648 \u062a\u062d\u062a \u0646\u0638\u0627\u0631\u062a \u060c \u0627\u062b\u0631\u0628\u062e\u0634\u06cc \u062f\u0627\u0631\u062f.\u06cc\u06a9 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u062f\u0642\u06cc\u0642 \u06a9\u0647 \u062f\u0631 \u0645\u06cc\u0627\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0645\u062a\u0646\u0648\u0639\u06cc \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u060c \u0686\u0646\u062f \u0632\u0628\u0627\u0646\u0647 \u0648 \u0686\u0646\u062f \u062d\u0627\u0644\u062a\u0647 \u0627\u0646\u062c\u0627\u0645 \u0634\u062f\u0647 \u0627\u0633\u062a \u060c \u0628\u0647 \u0637\u0648\u0631 \u0645\u062f\u0627\u0648\u0645 \u062f\u0633\u062a\u0627\u0648\u0631\u062f\u0647\u0627\u06cc \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0628\u0627 Matryoshka-Adaptor \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f.\u0646\u06a9\u062a\u0647 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647 \u060c \u0628\u0627 \u062a\u0648\u062c\u0647 \u0628\u0647 API \u0647\u0627\u06cc Google \u0648 OpenAi \u060c Matryoshka-Adaptor \u0628\u0647 \u06a9\u0627\u0647\u0634 \u0627\u0628\u0639\u0627\u062f \u0627\u0632 \u062f\u0648 \u062a\u0627 \u062f\u0648\u0627\u0632\u062f\u0647 \u0628\u0631\u0627\u0628\u0631 \u0648 \u0628\u062f\u0648\u0646 \u0628\u0647 \u062e\u0637\u0631 \u0627\u0646\u062f\u0627\u062e\u062a\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u062f\u0631 \u0686\u0646\u062f\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 Beir \u062f\u0633\u062a \u0645\u06cc \u06cc\u0627\u0628\u062f.<\/p>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 […]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"closed","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}}},"product_cat":[511,1158],"product_tag":[],"class_list":{"0":"post-104547","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-511","7":"product_cat-1158","8":"pmpro-has-access","9":"desktop-align-left","10":"tablet-align-left","11":"mobile-align-left","13":"first","14":"instock","15":"shipping-taxable","16":"purchasable","17":"product-type-variable"},"yoast_head":"\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0645\u0627\u062a\u0631\u06cc\u0648\u0634\u06a9\u0627-\u0622\u062f\u0627\u067e\u062a\u0648\u0631: \u062a\u0646\u0638\u06cc\u0645 \u0628\u062f\u0648\u0646 \u0646\u0638\u0627\u0631\u062a \u0648 \u0646\u0638\u0627\u0631\u062a\u200c\u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0627\u0628\u0639\u0627\u062f \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0648\u0686\u06a9\u200c\u062a\u0631 - 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