---
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

- [Reranker model](#reranker-model)
  - [Brief information](#brief-information)
  - [Supporting architectures](#supporting-architectures)
  - [Example usage](#example-usage)
    - [HuggingFace Inference Endpoints](#huggingface-inference-endpoints)
    - [Local inference](#local-inference)



## 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](https://huggingface.co/BAAI/bge-reranker-v2-m3) with no any fine tune.
- Commit: [953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e](https://huggingface.co/BAAI/bge-reranker-v2-m3/commit/953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e)


**More details please refer to the [repo of bse model](https://huggingface.co/BAAI/bge-reranker-v2-m3).**

## 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```

```bash
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

```python
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]
```