stella-pl-retrieval-8k

Model overview

This is an embedding model based on stella_en_1.5B_v5 and further fine-tuned for retrieval tasks in Polish. It transforms texts into 1024-dimensional vectors. The model training consisted of two stages: - In the first stage, we adapted the model to support the Polish language using the multilingual knowledge distillation method, leveraging a diverse corpus of 20 million Polish-English text pairs. - The original Stella model and the output of the first stage were limited to a short context of 512 tokens. In the second stage, we extended the context to 8192 tokens and then fine-tuned the model using contrastive loss on a dataset comprising 1.5 million queries. Positive and negative passages for each query have been selected with the help of BAAI/bge-reranker-v2.5-gemma2-lightweight reranker. The model was trained for three epochs with a batch size of 1024 queries. For retrieval, queries should be prefixed with **"Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "**. For symmetric tasks such as semantic similarity, both texts should be prefixed with **"Instruct: Retrieve semantically similar text.\nQuery: "**.

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

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

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。