17 citations · 53 across the 11 of their papers we have counts for
12 papers
What Transfers from a VLM Teacher? Comparing Supervision Signals for Visual Document Retrieval
Saba Sturua, Han Xiao
Visual document retrievers are trained contrastively: each query is matched to one page labelled relevant - the positive - and pushed away from negatives, pages presumed irrelevant…
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…
jina-embeddings-v5-text: Task-Targeted Embedding Distillation
Mohammad Kalim Akram, Saba Sturua, Nastia Havriushenko +4
Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpose models are typically trained w…
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-…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
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…
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…
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…
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…
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…
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…