Isabelle Mohr @isabelle_mohr
MLE @JinaAI_ 🤖 Interested in all things Machine Learning! Joined April 2022-
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2025 could be the year of Deep(Re)Search. Test-time compute and reasoning model are transforming search systems now. With <think>, users have been educated to accept delayed gratification—longer waiting times in exchange for higher-quality, actionable results, much like the…
Great work from MMTEB team! We have 3 contributors from @JinaAI_ ! @michael_g_u @jupyterjazz @isabelle_mohr
Our submission to ECIR 2025 on jina-embeddings-v3 has been accepted! 🎉 At the ECIR Industry Day @jupyterjazz takes the stage to share how we train the latest version of our text embedding model. More details: ecir2025.eu
Stop paying the OpenAI tax. The best AI devtools are actually open-source, free to use, and give you full control over your data and privacy. While proprietary AI dominated early headlines, the true revolution is happening in open source - where a flourishing ecosystem of…
I'm excited to attend @weaviate_io's AI Hack Night tonight!🚀#hacknightberlin
Join our next ML reading group featuring VisRAG on Nov 29th:
Join our next ML reading group featuring VisRAG on Nov 29th:
We extended our priprint about late chunking, a novel method to make embeddings of chunks context-aware. We added: - Algorithm for long documents - Training method to make late chunking more effective - Comparison to Anthropic's contextual embedding arxiv.org/abs/2409.04701
Want to learn more about Embeddings, Rerankers and ColBERT? Come to my talk on Thursday at @qdrant_engine's Vector Space Event 😎 more info here -> lu.ma/88rdjnhg
With @isabelle_mohr at #bbuzz Thank you for being there for my session. 💙
Got my Weaviate👕 here ;) thanks @femke_plantinga and @philipvollet ! This afternoon my colleague @isabelle_mohr and @saahil will hold two presentations at Berlin Buzzwords, I’ll brief Jina CLIP and upcoming models @JinaAI_ . Come and join our presentation!
Got my Weaviate👕 here ;) thanks @femke_plantinga and @philipvollet ! This afternoon my colleague @isabelle_mohr and @saahil will hold two presentations at Berlin Buzzwords, I’ll brief Jina CLIP and upcoming models @JinaAI_ . Come and join our presentation!
Clip your schedules for next week because Andreas and I will present our latest text&image embedding model with advanced text capabilities 😉 Paper: arxiv.org/abs/2405.20204 🤗: huggingface.co/jinaai/jina-cl… API: jina.ai/embeddings/
Clip your schedules for next week because Andreas and I will present our latest text&image embedding model with advanced text capabilities 😉 Paper: arxiv.org/abs/2405.20204 🤗: huggingface.co/jinaai/jina-cl… API: jina.ai/embeddings/
This week I explored chunking methods: the Semantic Chunker from @llama_index on jinaai/wikisections dataset on Hugging Face. Varying the buffer size had pretty much no effect, while increasing the breakpoint percentile threshold increased chunking precision by a lot! @JinaAI_
Last night I had the pleasure of giving a talk together with @jupyterjazz at the Data Meetup Berlin hosted by Netlight! Love the knowledge sharing, and most importantly, to connect with so many passionate and interested people in the field. See you at the next one! #embeddings
I'll be giving a talk together with @jupyterjazz next week in Berlin about our German-English bilingual embedding model. If you wanna know how we trained this model and how to use it in a RAG pipeline, you better RSVP and attend! See ya there 🚀 meetup.com/data-meetup-be… @JinaAI_
A ColBERT variant, but support a bit longer context :) huggingface.co/jinaai/jina-co… cc @jobergum @lateinteraction @bclavie
Saw @JinaAI_'s excellent long context (8192!) ColBERT earlier today? Eager to give long-document ColBERT a shot? New joint🫅colbert-ai and🪤RAGatouille release now supports any maximum length the underlying model can handle (& dynamically adjusts maxlen when encoding in-memory)
A few days ago, @JinaAI_ released two new bilingual embedding models (German-English & Chinese-English), each supporting a max sequence length of 8K tokens! 🤯 ... and now you can use them with 🤗 Transformers.js, for cross-language retrieval, clustering, and so much more! 👇
A few days ago, @JinaAI_ released two new bilingual embedding models (German-English & Chinese-English), each supporting a max sequence length of 8K tokens! 🤯 ... and now you can use them with 🤗 Transformers.js, for cross-language retrieval, clustering, and so much more! 👇 https://t.co/75vlCg30Vh
We’re finally here with 2 new models, we call it bilingual embedding models, it allows you to perform monolingual and cross-lingual retrieval tasks, the future models are always X+EN, X is the main language and EN as the bridging language. Here are the first two: German-English…
Our German-English and Chinese English embedding models are open-source now 🚀 huggingface.co/jinaai/jina-em… huggingface.co/jinaai/jina-em…
Our German-English and Chinese English embedding models are open-source now 🚀 huggingface.co/jinaai/jina-em… huggingface.co/jinaai/jina-em… https://t.co/nhT5m2kJ6W
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