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cs.CL2025
Training Sparse Mixture Of Experts Text Embedding Models
Zach Nussbaum, Brandon Duderstadt
Transformer-based text embedding models have improved their performance on benchmarks like MIRACL and BEIR by increasing their parameter counts. However, this scaling approach intr…
cs.CL2025
CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking
Tarun Suresh, Revanth Gangi Reddy, Yifei Xu +4
Effective code retrieval plays a crucial role in advancing code generation, bug fixing, and software maintenance, particularly as software systems increase in complexity. While cur…
cs.CL2025
Nomic Embed: Training a Reproducible Long Context Text Embedder
Zach Nussbaum, John X. Morris, Brandon Duderstadt +1
This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, open-data, 8192 context length English text embedding…