17 citations · 38 across the 8 of their papers we have counts for
8 papers
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…
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…
Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models
Michael Günther, Louis Milliken, Jonathan Geuter +3
Jina Embeddings constitutes a set of high-performance sentence embedding models adept at translating textual inputs into numerical representations, capturing the semantics of the t…