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What is voyage-code-3?

Voyage AI has introduced voyage-code-3, a cutting-edge embedding model meticulously crafted to improve code retrieval performance. This groundbreaking model consistently outperforms OpenAI-v3-large and CodeSage-large by impressive margins of 13.80% and 16.81%, respectively, across a wide array of 32 distinct code retrieval datasets. It supports embeddings in several dimensions, including 2048, 1024, 512, and 256, while offering multiple quantization options such as float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With an extended context length of 32 K tokens, voyage-code-3 surpasses the limitations imposed by OpenAI's 8K and CodeSage Large's 1K context lengths, granting users enhanced flexibility. This model employs an innovative Matryoshka learning technique, allowing it to create embeddings with a layered structure of varying lengths within a single vector. As a result, users can convert documents into a 2048-dimensional vector and later retrieve shorter dimensional representations (such as 256, 512, or 1024 dimensions) without having to re-execute the embedding model, significantly boosting efficiency in code retrieval tasks. Furthermore, voyage-code-3 stands out as a powerful tool for developers aiming to optimize their coding processes and streamline workflows effectively. This advancement promises to reshape the landscape of code retrieval, making it a vital resource for software development.

What is BERT?

BERT stands out as a crucial language model that employs a method for pre-training language representations. This initial pre-training stage encompasses extensive exposure to large text corpora, such as Wikipedia and other diverse sources. Once this foundational training is complete, the knowledge acquired can be applied to a wide array of Natural Language Processing (NLP) tasks, including question answering, sentiment analysis, and more. Utilizing BERT in conjunction with AI Platform Training enables the development of various NLP models in a highly efficient manner, often taking as little as thirty minutes. This efficiency and versatility render BERT an invaluable resource for swiftly responding to a multitude of language processing needs. Its adaptability allows developers to explore new NLP solutions in a fraction of the time traditionally required.

Media

Media

Integrations Supported

AWS Marketplace
Alpaca
Amazon SageMaker Model Training
Elasticsearch
Gopher
Haystack
Milvus
PostgresML
Qdrant
Spark NLP
Vespa
Weaviate

Integrations Supported

AWS Marketplace
Alpaca
Amazon SageMaker Model Training
Elasticsearch
Gopher
Haystack
Milvus
PostgresML
Qdrant
Spark NLP
Vespa
Weaviate

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Voyage AI

Date Founded

2023

Company Location

United States

Company Website

blog.voyageai.com/2024/12/04/voyage-code-3/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

cloud.google.com/ai-platform/training/docs/algorithms/bert-start

Categories and Features

Categories and Features

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

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