MODEL PROFILE

polish-reranker-roberta-v3

sdadas · polish-reranker-roberta-v3

This model is designed to serve as a Polish reranker in retrieval-augmented generation (RAG) pipelines. It is a general-purpose reranker that delivers strong performance across various document types and domains. It is the successor to **sdadas/polish-reranker-roberta-v2** and has the following key features: The main difference between **polish-reranker-roberta-v2** and **polish-reranker-roberta-v3** is the support for longer contexts - 16x increase from 512 to 8192 tokens. This has a significant impact on reranking quality for datasets with longer documents. Table 1 presents examples of tasks from the PIRB benchmark where substantial improvements were observed as a result of the extended context. For short and medium-length texts, the model achieves comparable or slightly better results than the previous version. For tasks involving very short texts (e.g., *onet*, *czy-wiesz-v2*), we observed a slight decrease in quality. The average score on the PIRB benchmark increased from **64.49** to **65.17** when using the sdadas/mmlw-retrieval-roberta-large retriever, or from **65.30** to **66.21** when using BAAI/bge-multilingual-gemma2. Task Improvement using sdadas/mmlw-retrieval-roberta-large retriever Improvement using BAAI/bge-multilingual-gemma2 retriever

license:gemma
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:15:44
OVERVIEW

Model overview

This model is designed to serve as a Polish reranker in retrieval-augmented generation (RAG) pipelines. It is a general-purpose reranker that delivers strong performance across various document types and domains. It is the successor to **sdadas/polish-reranker-roberta-v2** and has the following key features: The main difference between **polish-reranker-roberta-v2** and **polish-reranker-roberta-v3** is the support for longer contexts - 16x increase from 512 to 8192 tokens. This has a significant impact on reranking quality for datasets with longer documents. Table 1 presents examples of tasks from the PIRB benchmark where substantial improvements were observed as a result of the extended context. For short and medium-length texts, the model achieves comparable or slightly better results than the previous version. For tasks involving very short texts (e.g., *onet*, *czy-wiesz-v2*), we observed a slight decrease in quality. The average score on the PIRB benchmark increased from **64.49** to **65.17** when using the sdadas/mmlw-retrieval-roberta-large retriever, or from **65.30** to **66.21** when using BAAI/bge-multilingual-gemma2. Task Improvement using sdadas/mmlw-retrieval-roberta-large retriever Improvement using BAAI/bge-multilingual-gemma2 retriever

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:gemma。模型介绍与第三方评测均以原来源的具体版本和方法为准。