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support

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Description

The Gemini Embedding's inaugural text model, known as gemini-embedding-001, is now officially available through the Gemini API and Vertex AI, having maintained its leading position on the Massive Text Embedding Benchmark Multilingual leaderboard since its experimental introduction in March, attributed to its outstanding capabilities in retrieval, classification, and various embedding tasks, surpassing both traditional Google models and those from external companies. This highly adaptable model accommodates more than 100 languages and has a maximum input capacity of 2,048 tokens, utilizing the innovative Matryoshka Representation Learning (MRL) method, which allows developers to select output dimensions of 3072, 1536, or 768 to ensure the best balance of quality, performance, and storage efficiency. Developers are able to utilize it via the familiar embed_content endpoint in the Gemini API, and although the older experimental versions will be phased out by 2025, transitioning to the new model does not necessitate re-embedding of previously stored content. This seamless migration process is designed to enhance user experience without disrupting existing workflows.

Description

The Universal Sentence Encoder (USE) transforms text into high-dimensional vectors that are useful for a range of applications, including text classification, semantic similarity, and clustering. It provides two distinct model types: one leveraging the Transformer architecture and another utilizing a Deep Averaging Network (DAN), which helps to balance accuracy and computational efficiency effectively. The Transformer-based variant generates context-sensitive embeddings by analyzing the entire input sequence at once, while the DAN variant creates embeddings by averaging the individual word embeddings, which are then processed through a feedforward neural network. These generated embeddings not only support rapid semantic similarity assessments but also improve the performance of various downstream tasks, even with limited supervised training data. Additionally, the USE can be easily accessed through TensorFlow Hub, making it simple to incorporate into diverse applications. This accessibility enhances its appeal to developers looking to implement advanced natural language processing techniques seamlessly.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Gemini
Google AI Studio
Google Colab
Python
TensorFlow
Vertex AI

Integrations

Gemini
Google AI Studio
Google Colab
Python
TensorFlow
Vertex AI

Pricing Details

$0.15 per 1M input tokens
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

developers.googleblog.com/en/gemini-embedding-available-gemini-api/

Vendor Details

Company Name

Tensorflow

Founded

2015

Country

United States

Website

www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder

Product Features

Product Features

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Alternatives

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