1 citations · 3 across the 8 of their papers we have counts for
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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…
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…
Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5
Arkadeep Acharya, Rudra Murthy, Vishwajeet Kumar +1
Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, comprehensi…
Mistral-SPLADE: LLMs for better Learned Sparse Retrieval
Meet Doshi, Vishwajeet Kumar, Rudra Murthy +2
Learned Sparse Retrievers (LSR) have evolved into an effective retrieval strategy that can bridge the gap between traditional keyword-based sparse retrievers and embedding-based de…
Hindi-BEIR : A Large Scale Retrieval Benchmark in Hindi
Arkadeep Acharya, Rudra Murthy, Vishwajeet Kumar +1
Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, there is a…