11 citations · 15 across the 5 of their papers we have counts for
5 papers
jina-embeddings-v5-omni: Geometry-preserving Embeddings via Locked Aligned Towers
Florian Hönicke, Michael Günther, Andreas Koukounas +3
In this work, we introduce GELATO (Geometry-preserving Embeddings via Locked Aligned TOwers), a novel approach to multimodal embedding models. We build on the VLM-style architectur…
Efficient Code Embeddings from Code Generation Models
Daria Kryvosheieva, Saba Sturua, Michael Günther +1
jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically…
jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval
Michael Günther, Saba Sturua, Mohammad Kalim Akram +8
We introduce jina-embeddings-v4, a 3.8 billion parameter multimodal embedding model that unifies text and image representations through a novel architecture supporting both single-…
Jina CLIP: Your CLIP Model Is Also Your Text Retriever
Andreas Koukounas, Georgios Mastrapas, Michael Günther +11
Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These mod…
Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Michael Günther, Jackmin Ong, Isabelle Mohr +10
Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information. While these models are essen…