Model Card for Provence reranker Provence is a lightweight context pruning model for retrieval augmented generation, particularly optimized for question answering . Given a user question and a retrieved passage, Provence removes sentences from the passage that are not relevant to the user question . This speeds up generation and reduces context noise , in a plug and play manner for any LLM . Paper : https://arxiv.org/abs/2501.16214, accepted to ICLR 2025 Blogpost : https://huggingface.co/blog/nadiinchi/provence Developed by : Naver Labs Europe License : Provence is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 license [CC BY NC ND 4.0 license]. License file Model : provence reranker debertav3 v1 (Provence for Pruning and Reranking Of retrieVEd relevaNt ContExt) Backbone model : DeBERTav3 reranker (trained from DeBERTa v3 large) Model size : 430 million parameters Context length : 512 tokens NEW! The multilingual version of the model, based on BGE reranker v2 m3, is available here. Training and evaluation code & data are available in the Bergen repo Usage Provence uses nltk : Pruning a single context for a single question: You can also pass a list of q…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy