NV-Retriever: Improving text embedding models with effective hard-negative mining
NV-Retriever: Improving text embedding models with effective hard-negative mining
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
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 trai...
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NV-Retriever: Improving text embedding models with effective hard-negative mining
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TN_cdi_proquest_journals_3083765487
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3083765487
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E-ISSN
2331-8422
