Newsroom

jina-embeddings-v3 is a frontier multilingual text embedding model with 570M parameters and 8192 token-length, outperforming the latest proprietary embeddings from OpenAI and Cohere on MTEB.

Reader-LM-0.5B and Reader-LM-1.5B are two novel small language models inspired by Jina Reader, designed to convert raw, noisy HTML from the open web into clean markdown.

Jina ColBERT v2 supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.

You can't just use a CLIP model to retrieve text and images and sort the results by score. Why? Because of the modality gap. What is it, and where does it come from?

Chunking long documents while preserving contextual information is challenging. We introduce the "Late Chunking" that leverages long-context embedding models to generate contextual chunk embeddings for better retrieval applications.

What does it mean for LLMs when the web has been strip-mined clean, content providers have locked their doors, and there’s barely a trickle of new data to scrape?

We had a blast at ICML 2024 in Vienna, and we want to share with you everything we said, saw, and learned.

When using embedding models for zero-shot classification, rephrasing the class label to "This is seriously about 'LABEL'" gives higher accuracy vs. using LABEL alone. But how, and why?

A lot of LLMs can't figure out that 9.11 is actually smaller than 9.9. Can our embedding and reranker models do any better?

Are AI boyfriends and girlfriends GenAI's killer app? AI romance is no Jane Austen novel, but "social chatbots" are one of the few generative AI businesses with a clear path to profit. Take an up-close and personal look with us.

But if you work in SEO, it could be interesting to see things from the other side of the table; understand how embeddings and rerankers play their roles in modern search systems.

From Punk Einstein to Turbo Pigeons: Use PromptPerfect Interactive to reverse engineer prompts from pictures and generate Midjourney-style images with real-time feedback.