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Description

EmbeddingGemma 2 is a versatile and lightweight multimodal embedding model that facilitates the mapping of text, code, images, video, and audio into a unified embedding space, which is useful for applications such as search, retrieval, classification, routing, and RAG. It is constructed on the Gemma 4 framework and distributed under the Apache 2.0 license, featuring an impressive 740 million parameters while being fine-tuned for efficient on-device inference. Its flexible architecture allows for the use of only 270 million parameters for text-centric tasks, and it includes additional vision and audio encoders for comprehensive multimodal capabilities. Furthermore, the innovative Matryoshka Representation Learning technique enables developers to compress output vectors from 768 dimensions down to 512, 256, or even 128 dimensions, effectively reducing the storage and memory demands for local vector databases. The model is equipped with an 8K-token context window, providing the capability to handle up to 5.5 minutes of audio, 29 images, 58 video frames, or various combinations of these inputs seamlessly on local hardware. This adaptability makes it particularly valuable for developers seeking to enhance their applications with rich multimedia integration.

Description

GloVe, which stands for Global Vectors for Word Representation, is an unsupervised learning method introduced by the Stanford NLP Group aimed at creating vector representations for words. By examining the global co-occurrence statistics of words in a specific corpus, it generates word embeddings that form vector spaces where geometric relationships indicate semantic similarities and distinctions between words. One of GloVe's key strengths lies in its capability to identify linear substructures in the word vector space, allowing for vector arithmetic that effectively communicates relationships. The training process utilizes the non-zero entries of a global word-word co-occurrence matrix, which tracks the frequency with which pairs of words are found together in a given text. This technique makes effective use of statistical data by concentrating on significant co-occurrences, ultimately resulting in rich and meaningful word representations. Additionally, pre-trained word vectors can be accessed for a range of corpora, such as the 2014 edition of Wikipedia, enhancing the model's utility and applicability across different contexts. This adaptability makes GloVe a valuable tool for various natural language processing tasks.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/

Vendor Details

Company Name

Stanford NLP

Country

United States

Website

nlp.stanford.edu/projects/glove/

Product Features

Product Features

Alternatives

No Alternatives

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