6 papers
Granite Embedding Multilingual R2 Models
Parul Awasthy, Aashka Trivedi, Yushu Yang +14
We introduce the multilingual Granite Embedding R2 models, a family of encoder-based embedding models for enterprise-scale dense retrieval across 200+ languages. Extending our Engl…
VAREX: A Benchmark for Multi-Modal Structured Extraction from Documents
Udi Barzelay, Ophir Azulai, Inbar Shapira +4
We introduce VAREX (VARied-schema EXtraction), a benchmark for evaluating multimodal foundation models on structured data extraction from government forms. VAREX employs a Reverse…
Granite Embedding R2 Models
Parul Awasthy, Aashka Trivedi, Yulong Li +17
We introduce the Granite Embedding R2 models, a comprehensive family of high-performance English encoder-based embedding models engineered for enterprise-scale dense retrieval appl…
Granite-speech: open-source speech-aware LLMs with strong English ASR capabilities
George Saon, Avihu Dekel, Alexander Brooks +21
Granite-speech LLMs are compact and efficient speech language models specifically designed for English ASR and automatic speech translation (AST). The models were trained by modali…
Granite Embedding Models
Parul Awasthy, Aashka Trivedi, Yulong Li +19
We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, wit…
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…