BaseModelAI/cleora
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
This project helps data scientists and machine learning engineers transform complex, interconnected data, like customer interactions or product relationships, into numerical representations called embeddings. You input raw relational data, such as a list of customer-product purchases, and it outputs stable, high-quality entity embeddings that can be used for tasks like recommendation systems or fraud detection. It's designed for anyone working with graph-like data who needs efficient and accurate ways to understand relationships and similarities between entities.
514 stars. No commits in the last 6 months.
Use this if you need to generate high-quality, reproducible embeddings for entities in large, complex datasets quickly and without specialized GPU hardware.
Not ideal if your data is primarily unstructured text or images and does not have a clear relational structure.
Stars
514
Forks
53
Language
Jupyter Notebook
License
—
Category
Last pushed
Nov 28, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/BaseModelAI/cleora"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
MilaNLProc/contextualized-topic-models
A python package to run contextualized topic modeling. CTMs combine contextualized embeddings...
vinid/cade
Compass-aligned Distributional Embeddings. Align embeddings from different corpora
spcl/ncc
Neural Code Comprehension: A Learnable Representation of Code Semantics
criteo-research/CausE
Code for the Recsys 2018 paper entitled Causal Embeddings for Recommandation.
vintasoftware/entity-embed
PyTorch library for transforming entities like companies, products, etc. into vectors to support...