voyage-code-3MongoDB
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word2vecGoogle
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Related Products
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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.
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About
Word2Vec is a neural network-based technique for learning word embeddings, developed by researchers at Google. It transforms words into continuous vector representations in a multi-dimensional space, capturing semantic relationships based on context. Word2Vec uses two main architectures: Skip-gram, which predicts surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which predicts a target word based on surrounding words. By training on large text corpora, Word2Vec generates word embeddings where similar words are positioned closely, enabling tasks like semantic similarity, analogy solving, and text clustering. The model was influential in advancing NLP by introducing efficient training techniques such as hierarchical softmax and negative sampling. Though newer embedding models like BERT and Transformer-based methods have surpassed it in complexity and performance, Word2Vec remains a foundational method in natural language processing and machine learning research.
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Platforms Supported
Windows
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Mac
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Linux
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Cloud
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On-Premises
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iPhone
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iPad
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Android
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Chromebook
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
AI researchers and developers in search of a solution providing an embedding model for code retrieval
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Audience
Researchers, data scientists, and developers working in natural language processing (NLP) and machine learning who need efficient word embeddings for text analysis and semantic understanding
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Support
Phone Support
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24/7 Live Support
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Online
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Support
Phone Support
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24/7 Live Support
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Online
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API
Offers API
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API
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Screenshots and Videos |
Screenshots and VideosNo images available
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Pricing
No information available.
Free Version
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Free Trial
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Pricing
Free
Open source
Free Version
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Free Trial
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Reviews/
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Training
Documentation
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Webinars
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Live Online
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In Person
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Training
Documentation
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Webinars
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In Person
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Company InformationMongoDB
Founded: 2007
United States
blog.voyageai.com/2024/12/04/voyage-code-3/
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Company InformationGoogle
Founded: 1998
United States
code.google.com/archive/p/word2vec/
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Integrations
Elasticsearch
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Gensim
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Milvus
Supported
Qdrant
Supported
Vespa
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Integrations
Elasticsearch
Not Supported
Gensim
Supported
Milvus
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Qdrant
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