activity
20242026
most citedjina-embeddings-v3: Multilingual Embeddings With Task LoRA

17 citations · 26 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026★ 1 cited

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…

cs.CL2025

jina-vlm: Small Multilingual Vision Language Model

Andreas Koukounas, Georgios Mastrapas, Florian Hönicke +3

We present jina-vlm, a token-efficient 2.4B parameter vision-language model that achieves state-of-the-art multilingual VQA performance among open 2B-scale VLMs. The model couples…

cs.CL2024★ 2 cited

jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images

Andreas Koukounas, Georgios Mastrapas, Sedigheh Eslami +7

Contrastive Language-Image Pretraining (CLIP) has been widely used for crossmodal information retrieval and multimodal understanding tasks. However, CLIP models are mainly optimize…

cs.CL2024★ 17 cited

jina-embeddings-v3: Multilingual Embeddings With Task LoRA

Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram +8

We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tas…

cs.IR2024★ 1 cited

Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever

Rohan Jha, Bo Wang, Michael Günther +7

Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT's late interaction scoring approximates the joint query-document attentio…

cs.CL2024★ 2 cited

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…

cs.CL2024★ 3 cited

Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings

Isabelle Mohr, Markus Krimmel, Saba Sturua +16

We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of process…