voyage-3-large

voyage-3-large

Voyage AI
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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.

About

Voyage AI has unveiled voyage-3-large, a cutting-edge general-purpose and multilingual embedding model that leads across eight evaluated domains, including law, finance, and code, outperforming OpenAI-v3-large and Cohere-v3-English by averages of 9.74% and 20.71%, respectively. Enabled by Matryoshka learning and quantization-aware training, it supports embeddings of 2048, 1024, 512, and 256 dimensions, along with multiple quantization options such as 32-bit floating point, signed and unsigned 8-bit integer, and binary precision, significantly reducing vector database costs with minimal impact on retrieval quality. Notably, voyage-3-large offers a 32K-token context length, surpassing OpenAI's 8K and Cohere's 512 tokens. Evaluations across 100 datasets in diverse domains demonstrate its superior performance, with flexible precision and dimensionality options enabling substantial storage savings without compromising quality.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Data scientists and machine learning engineers seeking a tool to optimize their natural language processing models with robust sentence embeddings

Audience

Companies wanting a solution providing cost-effective embedding models for legal document analysis, financial data processing, and code retrieval

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Tensorflow
Founded: 2015
United States
www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder

Company Information

Voyage AI
Founded: 2023
United States
blog.voyageai.com/2025/01/07/voyage-3-large/

Alternatives

word2vec

word2vec

Google

Alternatives

voyage-code-3

voyage-code-3

Voyage AI
Codestral Embed

Codestral Embed

Mistral AI
Exa

Exa

Exa.ai
txtai

txtai

NeuML
LexVec

LexVec

Alexandre Salle

Categories

Categories

Integrations

Cohere
Google Colab
LangChain
OneSignal
PyTorch
Snowflake
TensorFlow
Voyage AI

Integrations

Cohere
Google Colab
LangChain
OneSignal
PyTorch
Snowflake
TensorFlow
Voyage AI
Claim Universal Sentence Encoder and update features and information
Claim Universal Sentence Encoder and update features and information
Claim voyage-3-large and update features and information
Claim voyage-3-large and update features and information