word2vec

word2vec

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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.

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.

Platforms Supported

Windows Not Supported
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Cloud Supported
On-Premises Not Supported
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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

Audience

AI researchers and developers in search of a solution providing an embedding model for code retrieval

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

Support

Phone Support Not Supported
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Online Supported

Support

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API

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API

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Screenshots and Videos

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Pricing

No information available.
Free Version Not Supported
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Pricing

Free
Open source
Free Version Supported
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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:

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Training

Documentation Supported
Webinars Not Supported
Live Online Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

MongoDB
Founded: 2007
United States
blog.voyageai.com/2024/12/04/voyage-code-3/

Company Information

Google
Founded: 1998
United States
code.google.com/archive/p/word2vec/

Alternatives

Alternatives

Gensim

Gensim

Radim Řehůřek
Voyage AI

Voyage AI

MongoDB
GloVe

GloVe

Stanford NLP
voyage-4-large

voyage-4-large

Voyage AI
Codestral Embed

Codestral Embed

Mistral AI

Categories

Embedding Models Supported

Categories

Embedding Models Supported

Integrations

Elasticsearch Supported
Gensim Not Supported
Milvus Supported
Qdrant Supported
Vespa Supported

Integrations

Elasticsearch Not Supported
Gensim Supported
Milvus Not Supported
Qdrant Not Supported
Vespa Not Supported
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