🤔 Which task do you think is more complex for ML models? 🔹 Multi-label classification 🔹 Entity recognition / Object detection (e.g., in computer vision) Vote and share your thoughts! 👇

An advanced version of Gemini with Deep Think has officially achieved gold medal-level performance at the International Mathematical Olympiad. 🥇 It solved 5️⃣ out of 6️⃣ exceptionally difficult problems, involving algebra, combinatorics, geometry and number theory. Here’s how 🧵

🚀 Introducing GLiClass‑V3 – a leap forward in zero-shot classification! Matches or beats cross-encoder accuracy, while being up to 50× faster. Real-time inference is now possible on edge hardware. huggingface.co/collections/kn… #TextClassification #NLP #ZeroShot #GLiClass

GLiClass-V3: A family of encoder-only models that match or exceed DeBERTa-v3-Large in zero-shot accuracy, while delivering up to 50× faster inference. Core Design: - Single-pass inference: No cross-encoder pairing needed. One forward pass handles all labels. - LoRA adapters:

Seeing ModernBERT and Ettin models being useful is heart warming

Every tech company can and should train their own deepseek R1, Llama or GPT5, just like every tech company writes their own code (and AI is no more than software 2.0). This is why we're releasing the Ultra-Scale Playbook. 200 pages to master: - 5D parallelism (DP, TP, PP, EP,

🚀 Our largest study on zero-shot text classification is out! 📄 arxiv.org/pdf/2508.07662 We surpass cross-encoders while being much faster, especially for large label sets. Check out all the research results 👇

🚀 GLiNER x SmolLM: a new joint encoder-decoder architecture 🚀 We are excited to release a new kind of GLiNER model built with the mantra "you do the same things only once." Built on top of DeBERTa + Hugging Face SmolLM2 — full details below 👇

Join us for a deep dive into Zero-Shot Named Entity Recognition with GLiNeR presented by Ihor Stepanov on Tuesday, August 26th. Thanks to our Retrieval and Search program leads Mayank Rakesh and Avinab Neogy for organizing this session ✨ Learn more: cohere.com/events/Cohere-…

Don't forget to tune in tomorrow, August 26th as Ihor Stepanov discusses Zero-Shot Named Entity Recognition with GLiNeR. Learn more: cohere.com/events/Cohere-…

🚀 Introducing GLiNER-PII 🔐 — a new open-source collection of high-performance models trained for sensitive data detection. 🔍 Explore the model collection here: huggingface.co/collections/kn…

New state-of-the-art Personally Identifiable Information extraction that can run extremely efficiently, even on CPUs. In my opinion, PII is one of the most important entity recognition tasks, and the GLiNER architecture is absolute best option for it right now.

Open-source works best with community input ❤️ If you’ve used GLiNER, GLiClass, or Comprehend-it, we want to hear from you. 👍 What worked for you? 🛠️ What needs improvement? 💭 What should we build next? Feedback form: docs.google.com/forms/d/e/1FAI…