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stella-pl-retrieval-8k

sdadas

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: "**.

译文排队中 · 当前显示来源原文

向量与相似度

输入文本→ 输出向量

价格待核验多模态与渠道费用须分别核对

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加入对比来源 / 文档 ↗

stella-pl-retrieval-mini-8k

sdadas

This is an embedding model based on stella_en_400M_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 five epochs with a batch size of 1024 queries. Note: The model uses a custom implementation that requires the XFormers library. For XFormers to function correctly, you need a compatible versions of Flash-Attention and PyTorch installed. Before using the model, make sure your XFormers installation is properly configured. For retrieval, queries should be prefixed with **"Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "**.

译文排队中 · 当前显示来源原文

向量与相似度

输入文本→ 输出向量

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题

API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。