🪆 Introduction to Matryoshka Embedding Models

New and improved embedding model

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.

Welcome EmbeddingGemma, Google's new efficient embedding model

Getting Started With Embeddings

New embedding models and API updates

Deploy Embedding Models with Hugging Face Inference Endpoints

Concentration bounds on response-based vector embeddings of black-box generative models

arXiv:2511. 08307v2 Announce Type: replace-cross Abstract: Generative models, such as large language models or text-to-image diffusion models, can generate relevant responses to user-given queries.

Low-rank Distributional Matrix Completion

arXiv:2606. 04176v1 Announce Type: new Abstract: We study a distributional generalization of the matrix completion problem in which each entry of the target matrix is a probability distribution rather than a scalar.

XLSCOUT Unveils ParaEmbed 2.0: a Powerful Embedding Model Tailored for Patents and IP with Expert Support from Hugging Face

RAMP: Recognition parametrisation by Amortised Message Passing

arXiv:2607. 18883v1 Announce Type: cross Abstract: A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations.

Train 400x faster Static Embedding Models with Sentence Transformers

The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model

arXiv:2607. 13660v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyperspherical geometry.

Build a Domain-Specific Embedding Model in Under a Day