voyage-code-3

voyage-code-3

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 introduces voyage-code-3, a next-generation embedding model optimized for code retrieval. It outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 32 code retrieval datasets, respectively. It supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization options, including float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With a 32 K-token context length, it surpasses OpenAI's 8K and CodeSage Large's 1K context lengths. Voyage-code-3 employs Matryoshka learning to create embeddings with a nested family of various lengths within a single vector. This allows users to vectorize documents into a 2048-dimensional vector and later use shorter versions (e.g., 256, 512, or 1024 dimensions) without re-invoking the embedding model.

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

AI researchers and developers in search of a solution providing an embedding model for 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/2024/12/04/voyage-code-3/

Alternatives

word2vec

word2vec

Google

Alternatives

voyage-3-large

voyage-3-large

Voyage AI
Codestral Embed

Codestral Embed

Mistral AI
Exa

Exa

Exa.ai
txtai

txtai

NeuML

Categories

Categories

Integrations

Elasticsearch
Google Colab
Milvus
Qdrant
TensorFlow
Vespa
Weaviate

Integrations

Elasticsearch
Google Colab
Milvus
Qdrant
TensorFlow
Vespa
Weaviate
Claim Universal Sentence Encoder and update features and information
Claim Universal Sentence Encoder and update features and information
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Claim voyage-code-3 and update features and information