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Open Source Model Profile · BAAI

bge-large-zh-v1.5

bge-large-zh-v1.5 is a Chinese BERT-family embedding model from BAAI. According to the model card, it belongs to the BGE family and uses a Chinese retrieval instruction for relevant-passage search.

Publisher
BAAI
Task
feature-extraction
Model type
bert
License
mit
Library
sentence-transformers
Publication status
Approved for indexing

Model overview

bge-large-zh-v1.5 is published by BAAI as a feature-extraction model. The captured configuration identifies BertModel with model type bert. According to the model card, it is the Chinese large BGE release used for embedding queries and passages, with related reranker models suggested for top-k refinement.

Recorded capabilities

Chinese retrieval instruction

According to the model card, queries for BAAI/bge-large-zh use a Chinese instruction for generating representations for relevant-article retrieval.

Multiple framework examples

According to the model card, usage covers FlagEmbedding, Sentence-Transformers, LangChain, and Hugging Face Transformers with CLS pooling and normalization.

Reranker pairing guidance

According to the model card, BGE rerankers can re-rank top-k documents returned by embedding models, with hard negatives needed for reranker fine-tuning.

Use cases in the source record

  • Chinese passage retrieval in which queries carry a Chinese retrieval instruction and passages are encoded for similarity search.
  • Retrieve-then-rerank pipelines where a BGE embedding model returns candidates for cross-encoder reranking.

Limitations and unknowns

  • No parameter count was extracted from this record.
  • Rank and benchmark-table figures are publisher claims and were not independently verified.
  • Provider state is historical snapshot data, not independently refreshed current availability.

Source and provenance

Source: BAAI/bge-large-zh-v1.5

Captured: Unknown. Processed: 2026-09-07T19:34:29.748644+00:00.

FlagEmbedding Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License For more details please refer to our Github: FlagEmbedding . If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3 . English | 中文 FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: Long-Context LLM : Activation Beacon Fine-tuning of LM : LM-Cocktail Dense Retrieval : BGE-M3 , LLM Embedder , BGE Embedding Reranker Model : BGE Reranker Benchmark : C-MTEB News 1/30/2024: Release BGE-M3 , a new member to BGE model series! M3 s…

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