metadata
license: mit
base_model: BAAI/bge-reranker-v2-m3
tags:
- generated_from_trainer
- transformers
library_name: sentence-transformers
pipeline_tag: text-ranking
model-index:
- name: bge_reranker
results: []
inference:
parameters:
normalize: true
widget:
- inputs:
source_sentence: Hello, world!
sentences:
- Hello! How are you?
- Cats and dogs
- The sky is blue
Reranker model
Brief information
This repository contains reranker model bge-reranker-v2-m3 which you can run on HuggingFace Inference Endpoints.
- Base model: BAAI/bge-reranker-v2-m3 with no any fine tune.
- Commit: 953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e
More details please refer to the repo of bse model.
Supporting architectures
- Apple Silicon MPS
- Nvidia GPU
- HuggingFace Inference Endpoints (AWS)
- CPU (Intel Sapphire Rapids, 4 vCPU, 8 Gb)
- GPU (Nvidia T4)
- Infernia 2 (2 cores, 32 Gb RAM)
Example usage
HuggingFace Inference Endpoints
⚠️ When you will deploy this model in HuggingFace Inference endpoints plese select Settings -> Advanced settings -> Task: Sentence Similarity
curl "https://xxxxxxx.us-east-1.aws.endpoints.huggingface.cloud" \
-X POST \
-H "Accept: application/json" \
-H "Authorization: Bearer hf_yyyyyyy" \
-H "Content-Type: application/json" \
-d '{
"inputs": {
"source_sentence": "Hello, world!",
"sentences": [
"Hello! How are you?",
"Cats and dogs",
"The sky is blue"
]
},
"normalize": true
}'
Local inference
from FlagEmbedding import FlagReranker
class RerankRequest(BaseModel):
query: str
documents: list[str]
# Prepare array
arr = []
for element in request.documents:
arr.append([request.query, element])
print(arr)
# Inference
reranker = FlagReranker('netandreus/bge-reranker-v2-m3', use_fp16=True)
scores = reranker.compute_score(arr, normalize=True)
if not isinstance(scores, list):
scores = [scores]
print(scores) # [-8.1875, 5.26171875]