  Text embedding models have been popular for information retrieval
applications such as semantic search and Question-Answering systems based on
Retrieval-Augmented Generation (RAG). Those models are typically Transformer
models that are fine-tuned with contrastive learning objectives. Many papers
introduced new embedding model architectures and training approaches, however,
one of the key ingredients, the process of mining negative passages, remains
poorly explored or described. One of the challenging aspects of fine-tuning
embedding models is the selection of high quality hard-negative passages for
contrastive learning. In this paper we propose a family of positive-aware
mining methods that leverage the positive relevance score for more effective
false negatives removal. We also provide a comprehensive ablation study on
hard-negative mining methods over their configurations, exploring different
teacher and base models. We demonstrate the efficacy of our proposed methods by
introducing the NV-Retriever-v1 model, which scores 60.9 on MTEB Retrieval
(BEIR) benchmark and 0.65 points higher than previous methods. The model placed
1st when it was published to MTEB Retrieval on July 07, 2024.
