Jina CLIP: Your CLIP Model Is Also Your Text Retriever

no code implementations • 30 May 2024 • Andreas Koukounas, Georgios Mastrapas, Michael Günther, Bo wang, Scott Martens, Isabelle Mohr, Saba Sturua, Mohammad Kalim Akram, Joan Fontanals Martínez, Saahil Ognawala, Susana Guzman, Maximilian Werk, Nan Wang, Han Xiao

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.

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

no code implementations • 26 Feb 2024 • Isabelle Mohr, Markus Krimmel, Saba Sturua, Mohammad Kalim Akram, Andreas Koukounas, Michael Günther, Georgios Mastrapas, Vinit Ravishankar, Joan Fontanals Martínez, Feng Wang, Qi Liu, Ziniu Yu, Jie Fu, Saahil Ognawala, Susana Guzman, Bo wang, Maximilian Werk, Nan Wang, Han Xiao

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

Contrastive Learning Multi-Task Learning +4

Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents

1 code implementation • 30 Oct 2023 • Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem, Tanguy Abel, Mohammad Kalim Akram, Susana Guzman, Georgios Mastrapas, Saba Sturua, Bo wang, Maximilian Werk, Nan Wang, Han Xiao

Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information.

Information Retrieval Re-Ranking +1

Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models

no code implementations • 20 Jul 2023 • Michael Günther, Louis Milliken, Jonathan Geuter, Georgios Mastrapas, Bo wang, Han Xiao

Jina Embeddings constitutes a set of high-performance sentence embedding models adept at translating textual inputs into numerical representations, capturing the semantics of the text.

Negation Retrieval +5