Update README.md

README.md

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 <img src="https://cdn-uploads.huggingface.co/production/uploads/6273df31c3b822dad2d1eef2/4n-bxYfiMPC2LoLM2m7pg.png" alt="image/png" width="600">
 
-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**. More details about the model can be found in the [paper](https://arxiv.org/abs/2501.16214).
+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**. 
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+*Paper*: https://arxiv.org/abs/2501.16214, accepted to ICLR 2025
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+*Blogpost*: https://huggingface.co/blog/nadiinchi/provence
 
 *Developed by*: Naver Labs Europe  
 *License*: [CC BY-NC 4.0.](https://creativecommons.org/licenses/by-nc/4.0/)  
-*Paper*: https://arxiv.org/abs/2501.16214, accepted to ICLR 2025
 * *Model*: `provence-reranker-debertav3-v1` (Provence for Pruning and Reranking Of retrieVEd relevaNt ContExt)  
 * *Backbone model*: [DeBERTav3-reranker](https://huggingface.co/naver/trecdl22-crossencoder-debertav3) (trained from [DeBERTa-v3-large](https://huggingface.co/microsoft/deberta-v3-large))  
 * *Model size*: 430 million parameters