MODEL PROFILE

bce-embedding-base_v1

maidalun1020 · bce-embedding-base_v1

- 中英双语,以及中英跨语种能力(Bilingual and Crosslingual capability in English and Chinese); - RAG优化,适配更多真实业务场景(RAG adaptation for more domains, including Education, Law, Finance, Medical, Literature, FAQ, Textbook, Wikipedia, etc.); - 方便集成进langchain和llamaindex(Easy integrations for langchain and llamaindex in BCEmbedding )。 - `EmbeddingModel`不需要“精心设计”instruction,尽可能召回有用片段。 (No need for "instruction") - **最佳实践(Best practice)** :embedding召回top50-100片段,reranker对这50-100片段精排,最后取top5-10片段。(1. Get top 50-100 passages with bce-embedding-base_v1 for "`recall`"; 2. Rerank passages with bce-reranker-base_v1 and get top 5-10 for "`precision`" finally. ) - `BCEmbedding`技术博客( **Technical Blog** ): 为RAG而生-BCEmbedding技术报告 - Related link for **RerankerModel** : bce-reranker-base_v1 - RAG applications: QAnything, ragflow, HuixiangDou, ChatPDF. - Efficient inference framework: ChatLLM.cpp, Xinference, mindnlp (Huawei GPU, 华为GPU).

Embeddings & similaritylicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Embedding
Model weightsNot verified
Profile updated2026/11/2 03:57:03
OVERVIEW

Model overview

- 中英双语,以及中英跨语种能力(Bilingual and Crosslingual capability in English and Chinese); - RAG优化,适配更多真实业务场景(RAG adaptation for more domains, including Education, Law, Finance, Medical, Literature, FAQ, Textbook, Wikipedia, etc.); - 方便集成进langchain和llamaindex(Easy integrations for langchain and llamaindex in BCEmbedding )。 - `EmbeddingModel`不需要“精心设计”instruction,尽可能召回有用片段。 (No need for "instruction") - **最佳实践(Best practice)** :embedding召回top50-100片段,reranker对这50-100片段精排,最后取top5-10片段。(1. Get top 50-100 passages with bce-embedding-base_v1 for "`recall`"; 2. Rerank passages with bce-reranker-base_v1 and get top 5-10 for "`precision`" finally. ) - `BCEmbedding`技术博客( **Technical Blog** ): 为RAG而生-BCEmbedding技术报告 - Related link for **RerankerModel** : bce-reranker-base_v1 - RAG applications: QAnything, ragflow, HuixiangDou, ChatPDF. - Efficient inference framework: ChatLLM.cpp, Xinference, mindnlp (Huawei GPU, 华为GPU).

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.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

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。

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Frequently asked questions

Do open weights permit free commercial use?

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Sources & traceability

许可证:apache-2.0。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。