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Description

The Gemini Embedding's inaugural text model, known as gemini-embedding-001, is now officially available through the Gemini API and Gemini Enterprise Agent Platform, having maintained its leading position on the Massive Text Embedding Benchmark Multilingual leaderboard since its experimental introduction in March, attributed to its outstanding capabilities in retrieval, classification, and various embedding tasks, surpassing both traditional Google models and those from external companies. This highly adaptable model accommodates more than 100 languages and has a maximum input capacity of 2,048 tokens, utilizing the innovative Matryoshka Representation Learning (MRL) method, which allows developers to select output dimensions of 3072, 1536, or 768 to ensure the best balance of quality, performance, and storage efficiency. Developers are able to utilize it via the familiar embed_content endpoint in the Gemini API.

Description

LexVec represents a cutting-edge word embedding technique that excels in various natural language processing applications by factorizing the Positive Pointwise Mutual Information (PPMI) matrix through the use of stochastic gradient descent. This methodology emphasizes greater penalties for mistakes involving frequent co-occurrences while also addressing negative co-occurrences. Users can access pre-trained vectors, which include a massive common crawl dataset featuring 58 billion tokens and 2 million words represented in 300 dimensions, as well as a dataset from English Wikipedia 2015 combined with NewsCrawl, comprising 7 billion tokens and 368,999 words in the same dimensionality. Evaluations indicate that LexVec either matches or surpasses the performance of other models, such as word2vec, particularly in word similarity and analogy assessments. The project's implementation is open-source, licensed under the MIT License, and can be found on GitHub, facilitating broader use and collaboration within the research community. Furthermore, the availability of these resources significantly contributes to advancing the field of natural language processing.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Python

Integrations

Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Python

Pricing Details

$0.15 per 1M input tokens
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

developers.googleblog.com/en/gemini-embedding-available-gemini-api/

Vendor Details

Company Name

Alexandre Salle

Country

Brazil

Website

github.com/alexandres/lexvec

Product Features

Product Features

Alternatives

Alternatives

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word2vec Reviews

word2vec

Google