10 citations · 19 across the 9 of their papers we have counts for
3 papers · 1 filter
Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks
Yauhen Babakhin, Radek Osmulski, Ronay Ak +5
We introduce llama-embed-nemotron-8b, an open-weights text embedding model that achieves state-of-the-art performance on the Multilingual Massive Text Embedding Benchmark (MMTEB) l…
Omni-Embed-Nemotron: A Unified Multimodal Retrieval Model for Text, Image, Audio, and Video
Mengyao Xu, Wenfei Zhou, Yauhen Babakhin +6
We present Omni-Embed-Nemotron, a unified multimodal retrieval embedding model developed to handle the increasing complexity of real-world information needs. While Retrieval-Augmen…
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
Chankyu Lee, Rajarshi Roy, Mengyao Xu +4
Decoder-only LLM-based embedding models are beginning to outperform BERT or T5-based embedding models in general-purpose text embedding tasks, including dense vector-based retrieva…