BGE Reranker v2 m3
BGE Reranker v2 m3 is BAAI’s lightweight multilingual reranker in the v2 family. It is built for fast inference and easier deployment while keeping strong multilingual reranking capability, making it the efficiency-oriented option in the v2 lineup.
- Type
- Text
- Weights
- Open
- Downloads
- 17M
- Likes
- 1.2k
- Pipeline
- text classification
- License
- apache-2.0
Model hub
BAAI/bge-reranker-v2-m3
- Repo
- BAAI/bge-reranker-v2-m3
- Library
- sentence-transformers
- Access
- Public
Technical tags and languages
Papers
2
More from Beijing Academy of Artificial Intelligence (BAAI)
12
-
412 DL
AquilaChat
Text
AI model by Beijing Academy of Artificial Intelligence (BAAI)
-
BGE base
Text
BGE base is BAAI’s mid-size English BGE v1 embedding model. It is an encoder-only BERT-based text embedder fo…
-
BGE Base v1.5
Text
BGE Base v1.5 is BAAI’s mid-size English BGE v1.5 embedding model. It belongs to the Sep-2023 v1.5 update tha…
-
BGE Code v1
Multimodal
BGE Code v1 is BAAI’s LLM-based code embedding model for code and text retrieval. It supports natural-languag…
-
BGE ICL
Text
BGE ICL is BAAI’s English embedding model with in-context learning. It is designed to improve retrieval on ne…
-
9.9M DL
BGE large
Text
BGE large is BAAI’s flagship English model in the original BGE v1 series. It is an encoder-only BERT-based em…
-
BGE large v1.5
Text
BGE large v1.5 is BAAI’s top English BGE v1.5 embedding model. It is the large-scale Sep-2023 update to bge-l…
-
36M DL
BGE M3
Text
BGE M3 is BAAI’s multilingual retrieval embedding model built around three strengths: multi-functionality, mu…
About Beijing Academy of Artificial Intelligence (BAAI)
1
Beijing Academy of Artificial Intelligence (BAAI), also known as Zhiyuan Institute, is a Chinese non-profit AI research laboratory established in November 2018. BAAI conducts fundamental AI research, develops open-source models (including WuDao, BGE embeddings, Emu, and RoboBrain), and fosters collaboration between academia and industry. Known for creating the BGE (BAAI General Embedding) series of embedding models used in RAG systems worldwide.