Jina AI

With the new grounding API g.jina.ai in Jina Reader, you can easily ground statements to reduce LLM hallucinations or improve the integrity of human-written content. Learn more about the API and its limitations: https://lnkd.in/e4JsrmWT

Multilingual models often face a "language gap," where similar phrases in different languages don't align. We show how contrastive learning can bridge this gap, enhancing cross-language performance in jina-embeddings-v3. Read more: https://lnkd.in/ddZYuBXh

Part 2 of our exploration of Late Chunking, a deep dive into why it is the best method for chunk embeddings and improving search/RAG performance. https://lnkd.in/ewVspN-u

We collected some tips to help you migrate from Jina Embeddings v2 to v3. https://lnkd.in/dRRSy-SK

𝗷𝗶𝗻𝗮-𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀-𝘃𝟯, 𝗿𝗲𝗮𝗱𝗲𝗿-𝗹𝗺-𝟬.𝟱𝗯, 𝗮𝗻𝗱 𝗿𝗲𝗮𝗱𝗲𝗿-𝗹𝗺-𝟭.𝟱𝗯 are now available on AWS SageMaker and Azure Marketplace. Deploy these frontier models within your company’s cloud infrastructure to maintain compliance and full data ownership. Learn more at the link below: 𝗔𝗪𝗦: v3: https://lnkd.in/gjT3Qpnz reader-lm-1.5b: https://lnkd.in/gpVU2kyx reader-lm-0.5b: https://lnkd.in/gqz437p6 𝐀𝐳𝐮𝐫𝐞: v3: https://lnkd.in/gi2jizzF reader-lm-1.5b: https://lnkd.in/g3TUZ5Us reader-lm-0.5b: https://lnkd.in/gjPPi7C6

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. https://lnkd.in/eFGwtvuN

Wikimedia Deutschland e. V. startet in Kooperation mit den Experten von DataStax und Jina AI die Umsetzung eines semantischen Suchkonzepts, das die offen lizenzierten Daten von Wikidata für Entwickler*innen von KI-Anwendungen leichter nutzbar macht. Ziel ist es, den Prozess der Entwicklung von offenen, gemeinnützigen KI-Anwendungen zu vereinfachen und zu einem verlässlicheren Informations-Ökosystem beizutragen. Zur Pressemitteilung: https://lnkd.in/e7ZiyZth

We just released 𝐑𝐞𝐚𝐝𝐞𝐫-𝐋𝐌-𝟎.𝟓𝐁 and 𝐑𝐞𝐚𝐝𝐞𝐫-𝐋𝐌-𝟏.𝟓𝐁, two novel small language models (SLM) inspired by Jina Reader, designed to convert raw, noisy HTML from the open web into clean markdown. Both models are multilingual and support a context length of up to 𝟐𝟓𝟔𝐊 𝐭𝐨𝐤𝐞𝐧𝐬. Learn how we trained the model and how to use them. https://lnkd.in/escU9nQG

Jina ColBERT v2 supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length. https://lnkd.in/eqygMDX8

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? https://lnkd.in/gRjgX2tm