投稿者: Google解説ライター 奥村

この投稿者の執筆数: 4323 記事

Today, we’re making a new experimental Gemini Embedding text model (gemini-embedding-exp-03-07)1 available in the Gemini API.Trained on the Gemini model itself, this embedding model has inherited Gemini’s understanding of language and nuanced context making it applicable for a wide range of uses. This new embedding model surpasses our previous state-of-the-art model (text-embedding-004), achieves the top rank on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard, and comes with new features like longer input token length!We’ve trained our model to be remarkably general, delivering exceptional performance across diverse domains, including finance, science, legal, search, and more. It works effectively out-of-the-box, eliminating the need for extensive fine-tuning for specific tasks.The MTEB (Multilingual) leaderboard ranks text embedding models across diverse tasks such as retrieval and classification to provide a comprehensive benchmark for model comparison. Our Gemini Embedding model achieves a mean (task) score of 68.32–a margin of +5.81 over the next competing model.From building intelligent retrieval augmented generation (RAG) and recommendation systems to text classification, the ability for LLMs to understand the meaning behind text is crucial. Embeddings are often critical for building more efficient systems, reducing cost and latency while also generally providing better results than keyword matching systems. Embeddings capture semantic meaning and context through numerical representations of data. Data with similar semantic meaning have embeddings that are closer together. Embeddings enable a wide range of applications, including:In addition to improved quality across all dimensions, Gemini Embedding also features:Data Science Agent in Colab: The future of data analysis with GeminiCalCam: Transforming Food Tracking with the Gemini APIStart building with Gemini 2.0 Flash and Flash-LiteGemini 2.0 Deep Dive: Code Execution)))” config=”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”]

最新のGemini Embeddingモデルのご紹介 202…

続きを読むToday, we’re making a new experimental Gemini Embedding text model (gemini-embedding-exp-03-07)1 available in the Gemini API.Trained on the Gemini model itself, this embedding model has inherited Gemini’s understanding of language and nuanced context making it applicable for a wide range of uses. This new embedding model surpasses our previous state-of-the-art model (text-embedding-004), achieves the top rank on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard, and comes with new features like longer input token length!We’ve trained our model to be remarkably general, delivering exceptional performance across diverse domains, including finance, science, legal, search, and more. It works effectively out-of-the-box, eliminating the need for extensive fine-tuning for specific tasks.The MTEB (Multilingual) leaderboard ranks text embedding models across diverse tasks such as retrieval and classification to provide a comprehensive benchmark for model comparison. Our Gemini Embedding model achieves a mean (task) score of 68.32–a margin of +5.81 over the next competing model.From building intelligent retrieval augmented generation (RAG) and recommendation systems to text classification, the ability for LLMs to understand the meaning behind text is crucial. Embeddings are often critical for building more efficient systems, reducing cost and latency while also generally providing better results than keyword matching systems. Embeddings capture semantic meaning and context through numerical representations of data. Data with similar semantic meaning have embeddings that are closer together. Embeddings enable a wide range of applications, including:In addition to improved quality across all dimensions, Gemini Embedding also features:Data Science Agent in Colab: The future of data analysis with GeminiCalCam: Transforming Food Tracking with the Gemini APIStart building with Gemini 2.0 Flash and Flash-LiteGemini 2.0 Deep Dive: Code Execution)))” config=”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”]