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Description

Enable enterprises and developers to harness advanced neural search, generative AI, and multimodal services by leveraging cutting-edge LMOps, MLOps, and cloud-native technologies. The presence of multimodal data is ubiquitous, ranging from straightforward tweets and Instagram photos to short TikTok videos, audio clips, Zoom recordings, PDFs containing diagrams, and 3D models in gaming. While this data is inherently valuable, its potential is often obscured by various modalities and incompatible formats. To facilitate the development of sophisticated AI applications, it is essential to first address the challenges of search and creation. Neural Search employs artificial intelligence to pinpoint the information you seek, enabling a description of a sunrise to correspond with an image or linking a photograph of a rose to a melody. On the other hand, Generative AI, also known as Creative AI, utilizes AI to produce content that meets user needs, capable of generating images based on descriptions or composing poetry inspired by visuals. The interplay of these technologies is transforming the landscape of information retrieval and creative expression.

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

Azure Marketplace
Jina Search
LiteLLM
Pinecone Rerank v0
Sim Studio

Integrations

Azure Marketplace
Jina Search
LiteLLM
Pinecone Rerank v0
Sim Studio

Pricing Details

No price information available.
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

Jina AI

Founded

2020

Country

Germany

Website

jina.ai/

Vendor Details

Company Name

Alexandre Salle

Country

Brazil

Website

github.com/alexandres/lexvec

Product Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

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