{"id":235,"date":"2024-08-29T11:10:37","date_gmt":"2024-08-29T11:10:37","guid":{"rendered":"https:\/\/www.asm15.fr\/index.php\/2024\/08\/29\/jina-ai-devoile-late-chunking-une-methode-simple-dia-pour-incorporer-de-courts-fragments-en-exploitant-les-modeles-dincrustation-de-long-contexte\/"},"modified":"2024-08-29T11:10:37","modified_gmt":"2024-08-29T11:10:37","slug":"jina-ai-devoile-late-chunking-une-methode-simple-dia-pour-incorporer-de-courts-fragments-en-exploitant-les-modeles-dincrustation-de-long-contexte","status":"publish","type":"post","link":"https:\/\/www.asm15.fr\/index.php\/2024\/08\/29\/jina-ai-devoile-late-chunking-une-methode-simple-dia-pour-incorporer-de-courts-fragments-en-exploitant-les-modeles-dincrustation-de-long-contexte\/","title":{"rendered":"Jina AI d\u00e9voile ‘Late Chunking’ : Une m\u00e9thode simple d’IA pour incorporer de courts fragments en exploitant les mod\u00e8les d’incrustation de long contexte"},"content":{"rendered":"

<\/p>\n

En octobre 2023, une \u00e9tape cruciale a \u00e9t\u00e9 franchie dans le domaine du traitement automatique du langage naturel avec le lancement de \u00ab\u00a0jina-embeddings-v2-base-en\u00a0\u00bb, le premier mod\u00e8le d’encodage open-source avec une capacit\u00e9 de contexte de 8K. Ce d\u00e9veloppement a aliment\u00e9 le d\u00e9bat sur l’efficacit\u00e9 des mod\u00e8les d’encodage de longs contextes. La m\u00e9thode traditionnelle du RAG (g\u00e9n\u00e9ration augment\u00e9e par r\u00e9cup\u00e9ration) rencontre des d\u00e9fis, notamment la perte d\u2019informations contextuelles cruciales lors du d\u00e9coupage de textes en morceaux plus petits.<\/p>\n

Cependant, la m\u00e9thode innovante du \u00ab\u00a0Late Chunking\u00a0\u00bb propose une solution en appliquant une couche de transformer \u00e0 l’ensemble du texte avant de le diviser en segments. Les r\u00e9sultats des tests ont montr\u00e9 que cette approche am\u00e9liore significativement l’efficacit\u00e9 des r\u00e9cup\u00e9rations d’informations, en particulier pour les longs textes. Cette avanc\u00e9e d\u00e9montre l’importance de continuer \u00e0 explorer des mod\u00e8les d’encodage \u00e0 contexte \u00e9tendu pour pr\u00e9server l’int\u00e9grit\u00e9 contextuelle et am\u00e9liorer les r\u00e9sultats en traitement des textes.<\/p>\n","protected":false},"excerpt":{"rendered":"

En octobre 2023, une \u00e9tape cruciale a \u00e9t\u00e9 franchie dans le domaine du traitement automatique du langage naturel avec le lancement de \u00ab\u00a0jina-embeddings-v2-base-en\u00a0\u00bb, le premier mod\u00e8le d’encodage open-source avec une capacit\u00e9 de contexte de 8K. Ce d\u00e9veloppement a aliment\u00e9 le d\u00e9bat sur l’efficacit\u00e9 des mod\u00e8les d’encodage de longs contextes. La m\u00e9thode traditionnelle du RAG (g\u00e9n\u00e9ration […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-235","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nJina AI d\u00e9voile 'Late Chunking' : Une m\u00e9thode simple d'IA pour incorporer de courts fragments en exploitant les mod\u00e8les d'incrustation de long contexte - IA & TECHNOLOGIES<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.asm15.fr\/index.php\/2024\/08\/29\/jina-ai-devoile-late-chunking-une-methode-simple-dia-pour-incorporer-de-courts-fragments-en-exploitant-les-modeles-dincrustation-de-long-contexte\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Jina AI d\u00e9voile 'Late Chunking' : Une m\u00e9thode simple d'IA pour incorporer de courts fragments en exploitant les mod\u00e8les d'incrustation de long contexte - IA & TECHNOLOGIES\" \/>\n<meta property=\"og:description\" content=\"En octobre 2023, une \u00e9tape cruciale a \u00e9t\u00e9 franchie dans le domaine du traitement automatique du langage naturel avec le lancement de \u00ab\u00a0jina-embeddings-v2-base-en\u00a0\u00bb, le premier mod\u00e8le d’encodage open-source avec une capacit\u00e9 de contexte de 8K. 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