FlagEmbedding
Cross-encoder reranking via BAAI FlagEmbedding.
Overview
| Field |
Value |
| Type |
CrossEncoder |
| Library |
FlagEmbedding |
| Default Model |
BAAI/bge-reranker-large |
Installation
pip install "autorag-research[gpu]"
# or
uv add "autorag-research[gpu]"
Configuration
_target_: autorag_research.rerankers.flag_embedding.FlagEmbeddingReranker
model_name: BAAI/bge-reranker-large
Options
| Option |
Type |
Default |
Description |
| model_name |
str |
BAAI/bge-reranker-large |
FlagEmbedding model name |
| use_fp16 |
bool |
False |
Use FP16 for inference |
| batch_size |
int |
64 |
Batch size for multiple queries |
Models
| Model |
Description |
| BAAI/bge-reranker-large |
Best quality |
| BAAI/bge-reranker-base |
Balanced |
| BAAI/bge-reranker-v2-m3 |
Multilingual |
Usage
from autorag_research.rerankers import FlagEmbeddingReranker
reranker = FlagEmbeddingReranker()
results = reranker.rerank("What is RAG?", ["doc1", "doc2", "doc3"], top_k=2)