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Description

Nomic Embed is a comprehensive collection of open-source, high-performance embedding models tailored for a range of uses, such as multilingual text processing, multimodal content integration, and code analysis. Among its offerings, Nomic Embed Text v2 employs a Mixture-of-Experts (MoE) architecture that efficiently supports more than 100 languages with a remarkable 305 million active parameters, ensuring fast inference. Meanwhile, Nomic Embed Text v1.5 introduces flexible embedding dimensions ranging from 64 to 768 via Matryoshka Representation Learning, allowing developers to optimize for both performance and storage requirements. In the realm of multimodal applications, Nomic Embed Vision v1.5 works in conjunction with its text counterparts to create a cohesive latent space for both text and image data, enhancing the capability for seamless multimodal searches. Furthermore, Nomic Embed Code excels in embedding performance across various programming languages, making it an invaluable tool for developers. This versatile suite of models not only streamlines workflows but also empowers developers to tackle a diverse array of challenges in innovative ways.

Description

TILDE (Term Independent Likelihood moDEl) serves as a framework for passage re-ranking and expansion, utilizing BERT to boost retrieval effectiveness by merging sparse term matching with advanced contextual representations. The initial version of TILDE calculates term weights across the full BERT vocabulary, which can result in significantly large index sizes. To optimize this, TILDEv2 offers a more streamlined method by determining term weights solely for words found in expanded passages, leading to indexes that are 99% smaller compared to those generated by the original TILDE. This increased efficiency is made possible by employing TILDE as a model for passage expansion, where passages are augmented with top-k terms (such as the top 200) to enhance their overall content. Additionally, it includes scripts that facilitate the indexing of collections, the re-ranking of BM25 results, and the training of models on datasets like MS MARCO, thereby providing a comprehensive toolkit for improving information retrieval tasks. Ultimately, TILDEv2 represents a significant advancement in managing and optimizing passage retrieval systems.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Python Yes 
Baseten Yes 
Go Yes 
Hugging Face No 
Java Yes 
JavaScript Yes 
PHP Yes 
Ruby Yes 

Integrations

Python Yes 
Baseten No 
Go No 
Hugging Face Yes 
Java No 
JavaScript No 
PHP No 
Ruby No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

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 No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

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

Customer Support

Business Hours No 
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 No 

Vendor Details

Company Name

Nomic

Country

United States

Website

www.nomic.ai/embed

Vendor Details

Company Name

ielab

Country

United States

Website

github.com/ielab/TILDE/tree/main

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