Gemini Embedding 2Google
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Universal Sentence EncoderTensorflow
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About
Gemini Embedding models, including the newer Gemini Embedding 2, are part of Google’s Gemini AI ecosystem and are designed to convert text, phrases, sentences, and code into numerical vector representations that capture their semantic meaning. Unlike generative models that produce new content, the embedding model transforms input data into dense vectors that represent meaning in a mathematical format, allowing computers to compare and analyze information based on conceptual similarity rather than exact wording. These embeddings enable applications such as semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation pipelines. The model can process input in more than 100 languages and supports up to 2048 tokens per request, allowing it to embed longer pieces of text or code while maintaining strong contextual understanding.
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About
The Universal Sentence Encoder (USE) encodes text into high-dimensional vectors that can be utilized for tasks such as text classification, semantic similarity, and clustering. It offers two model variants: one based on the Transformer architecture and another on Deep Averaging Network (DAN), allowing a balance between accuracy and computational efficiency. The Transformer-based model captures context-sensitive embeddings by processing the entire input sequence simultaneously, while the DAN-based model computes embeddings by averaging word embeddings, followed by a feedforward neural network. These embeddings facilitate efficient semantic similarity calculations and enhance performance on downstream tasks with minimal supervised training data. The USE is accessible via TensorFlow Hub, enabling seamless integration into various applications.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
AI developers and data engineers who need a high-performance embedding model to convert text or code into semantic vectors for search, retrieval, and AI applications
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Audience
Data scientists and machine learning engineers seeking a tool to optimize their natural language processing models with robust sentence embeddings
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
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Pricing
Free
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationGoogle
Founded: 1998
United States
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/
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Company InformationTensorflow
Founded: 2015
United States
www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder
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Categories |
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Integrations
Gemini
Gemini Enterprise
Google AI Studio
Google Colab
Python
TensorFlow
Vertex AI
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Integrations
Gemini
Gemini Enterprise
Google AI Studio
Google Colab
Python
TensorFlow
Vertex AI
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