sentence-transformers safetensors xlm-roberta text-classification transformers text-embeddings-inference feature-extraction ko en zh arxiv:2309.07597 license:mit autotrain_compatible endpoints_compatible region:us
---
language:
- ko
- en
- zh
license: mit
pipeline_tag: feature-extraction
tags:
- transformers
- sentence-transformers
- text-embeddings-inference
---



# upskyy/ko-reranker

**ko-reranker**는 [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) 모델에 [한국어 데이터](https://huggingface.co/datasets/upskyy/ko-wiki-reranking)를 finetuning 한 model 입니다.

## Usage
## Using FlagEmbedding
```
pip install -U FlagEmbedding
```

Get relevance scores (higher scores indicate more relevance):

```python
from FlagEmbedding import FlagReranker


reranker = FlagReranker('upskyy/ko-reranker', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

score = reranker.compute_score(['query', 'passage'])
print(score) # -1.861328125

# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
score = reranker.compute_score(['query', 'passage'], normalize=True)
print(score) # 0.13454832326359276

scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores) # [-7.37109375, 8.5390625]

# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True)
print(scores) # [0.0006287840192903181, 0.9998043646624727]
```

## Using Sentence-Transformers

```
pip install -U sentence-transformers
```

Get relevance scores (higher scores indicate more relevance):

```python
from sentence_transformers import SentenceTransformer


sentences_1 = ["경제 전문가가 금리 인하에 대한 예측을 하고 있다.", "주식 시장에서 한 투자자가 주식을 매수한다."]
sentences_2 = ["한 투자자가 비트코인을 매수한다.", "금융 거래소에서 새로운 디지털 자산이 상장된다."]

model = SentenceTransformer('upskyy/ko-reranker')

embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T

print(similarity)
```

## Using Huggingface transformers

Get relevance scores (higher scores indicate more relevance):


```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained('upskyy/ko-reranker')
model = AutoModelForSequenceClassification.from_pretrained('upskyy/ko-reranker')
model.eval()

pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]

with torch.no_grad():
    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
    print(scores)
```



## Citation

```bibtex
@misc{bge_embedding,
      title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, 
      author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
      year={2023},
      eprint={2309.07597},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## License

FlagEmbedding is licensed under the MIT License. The released models can be used for commercial purposes free of charge.

## Reference

- [Dongjin-kr/ko-reranker](https://huggingface.co/Dongjin-kr/ko-reranker)
- [reranker-kr](https://github.com/aws-samples/aws-ai-ml-workshop-kr/tree/master/genai/aws-gen-ai-kr/30_fine_tune/reranker-kr)
- [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)
