---
license: apache-2.0
language:
- ko
- en
metrics:
- accuracy
base_model:
- BAAI/bge-reranker-v2-m3
pipeline_tag: text-classification
library_name: sentence-transformers
---

# Reranker (Cross-Encoder)

Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.

## Model Details
- Base model :  BAAI/bge-reranker-v2-m3
- The multilingual model has been optimized for Korean.

## Usage with Transformers

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

model = AutoModelForSequenceClassification.from_pretrained('dragonkue/bge-reranker-v2-m3-ko')
tokenizer = AutoTokenizer.from_pretrained('dragonkue/bge-reranker-v2-m3-ko')

features = tokenizer(['몇 년도에 지방세외수입법이 시행됐을까?', '실무교육을 통해 ‘지방세외수입법’에 대한 자치단체의 관심을 제고하고 자치단체의 차질 없는 업무 추진을 지원하였다. 이러한 준비과정을 거쳐 2014년 8월 7일부터 ‘지방세외수입법’이 시행되었다.'], 
['몇 년도에 지방세외수입법이 시행됐을까?', '식품의약품안전처는 21일 국내 제약기업 유바이오로직스가 개발 중인 신종 코로나바이러스 감염증(코로나19) 백신 후보물질 ‘유코백-19’의 임상시험 계획을 지난 20일 승인했다고 밝혔다.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    logits = model(**features).logits
    scores = torch.sigmoid(logits)
    print(scores)
# [9.9997962e-01 5.0702977e-07]
```


## Usage with SentenceTransformers
First install the Sentence Transformers library:
```
pip install -U sentence-transformers
```

```python
from sentence_transformers import CrossEncoder

model = CrossEncoder('dragonkue/bge-reranker-v2-m3-ko', default_activation_function=torch.nn.Sigmoid())

scores = model.predict([['몇 년도에 지방세외수입법이 시행됐을까?', '실무교육을 통해 ‘지방세외수입법’에 대한 자치단체의 관심을 제고하고 자치단체의 차질 없는 업무 추진을 지원하였다. 이러한 준비과정을 거쳐 2014년 8월 7일부터 ‘지방세외수입법’이 시행되었다.'], 
['몇 년도에 지방세외수입법이 시행됐을까?', '식품의약품안전처는 21일 국내 제약기업 유바이오로직스가 개발 중인 신종 코로나바이러스 감염증(코로나19) 백신 후보물질 ‘유코백-19’의 임상시험 계획을 지난 20일 승인했다고 밝혔다.']])
print(scores)
# [9.9997962e-01 5.0702977e-07]
```

## Usage with FlagEmbedding
First install the FlagEmbedding library:
```
pip install -U FlagEmbedding
```
```python
from FlagEmbedding import FlagReranker

reranker = FlagReranker('dragonkue/bge-reranker-v2-m3-ko')

scores = reranker.compute_score([['몇 년도에 지방세외수입법이 시행됐을까?', '실무교육을 통해 ‘지방세외수입법’에 대한 자치단체의 관심을 제고하고 자치단체의 차질 없는 업무 추진을 지원하였다. 이러한 준비과정을 거쳐 2014년 8월 7일부터 ‘지방세외수입법’이 시행되었다.'], 
['몇 년도에 지방세외수입법이 시행됐을까?', '식품의약품안전처는 21일 국내 제약기업 유바이오로직스가 개발 중인 신종 코로나바이러스 감염증(코로나19) 백신 후보물질 ‘유코백-19’의 임상시험 계획을 지난 20일 승인했다고 밝혔다.']], normalize=True)
print(scores)
# [9.9997962e-01 5.0702977e-07]
```

## Fine-tune
Refer to https://github.com/FlagOpen/FlagEmbedding 


## Evaluation


### Bi-encoder and Cross-encoder 

Bi-Encoders convert texts into fixed-size vectors and efficiently calculate similarities between them. They are fast and ideal for tasks like semantic search and classification, making them suitable for processing large datasets quickly.

Cross-Encoders directly compare pairs of texts to compute similarity scores, providing more accurate results. While they are slower due to needing to process each pair, they excel in re-ranking top results and are important in Advanced RAG techniques for enhancing text generation.


### Korean Embedding Benchmark with AutoRAG
(https://github.com/Marker-Inc-Korea/AutoRAG-example-korean-embedding-benchmark)

This is a Korean embedding benchmark for the financial sector. 


**Top-k 1**

Bi-Encoder (Sentence Transformer)

| Model name                            | F1         | Recall     | Precision  |
|---------------------------------------|------------|------------|------------|
| paraphrase-multilingual-mpnet-base-v2 | 0.3596     | 0.3596     | 0.3596     |
| KoSimCSE-roberta                      | 0.4298     | 0.4298     | 0.4298     |
| Cohere embed-multilingual-v3.0        | 0.3596     | 0.3596     | 0.3596     |
| openai ada 002                        | 0.4737     | 0.4737     | 0.4737     |
| multilingual-e5-large-instruct        | 0.4649     | 0.4649     | 0.4649     |
| Upstage Embedding                     | 0.6579     | 0.6579     | 0.6579     |
| paraphrase-multilingual-MiniLM-L12-v2 | 0.2982     | 0.2982     | 0.2982     |
| openai_embed_3_small                  | 0.5439     | 0.5439     | 0.5439     |
| ko-sroberta-multitask                 | 0.4211     | 0.4211     | 0.4211     |
| openai_embed_3_large                  | 0.6053     | 0.6053     | 0.6053     |
| KU-HIAI-ONTHEIT-large-v1              | 0.7105     | 0.7105     | 0.7105     |
| KU-HIAI-ONTHEIT-large-v1.1            | 0.7193     | 0.7193     | 0.7193     |
| kf-deberta-multitask                  | 0.4561     | 0.4561     | 0.4561     |
| gte-multilingual-base                 | 0.5877     | 0.5877     | 0.5877     |
| BGE-m3                                | 0.6578     | 0.6578     | 0.6578     |
| bge-m3-korean                         | 0.5351     | 0.5351     | 0.5351     |
| **BGE-m3-ko**                         | **0.7456** | **0.7456** | **0.7456** |


Cross-Encoder (Reranker)

| Model name                            | F1         | Recall     | Precision  |
|---------------------------------------|------------|------------|------------|
| gte-multilingual-reranker-base | 0.7281 | 0.7281     | 0.7281     |
| jina-reranker-v2-base-multilingual | 0.8070  | 0.8070     | 0.8070     |
| bge-reranker-v2-m3               | 0.8772     | 0.8772     | 0.8772     |
| **bge-reranker-v2-m3-ko**             | **0.9123** | **0.9123** | **0.9123** |


**Top-k 3**

Bi-Encoder (Sentence Transformer)

| Model name                            | F1         | Recall     | Precision  |
|---------------------------------------|------------|------------|------------|
| paraphrase-multilingual-mpnet-base-v2 | 0.2368     | 0.4737     | 0.1579     |
| KoSimCSE-roberta                      | 0.3026     | 0.6053     | 0.2018     |
| Cohere embed-multilingual-v3.0        | 0.2851     | 0.5702     | 0.1901     |
| openai ada 002                        | 0.3553     | 0.7105     | 0.2368     |
| multilingual-e5-large-instruct        | 0.3333     | 0.6667     | 0.2222     |
| Upstage Embedding                     | 0.4211     | 0.8421     | 0.2807     |
| paraphrase-multilingual-MiniLM-L12-v2 | 0.2061     | 0.4123     | 0.1374     |
| openai_embed_3_small                  | 0.3640     | 0.7281     | 0.2427     |
| ko-sroberta-multitask                 | 0.2939     | 0.5877     | 0.1959     |
| openai_embed_3_large                  | 0.3947     | 0.7895     | 0.2632     |
| KU-HIAI-ONTHEIT-large-v1              | 0.4386     | 0.8772     | 0.2924     |
| KU-HIAI-ONTHEIT-large-v1.1            | 0.4430     | 0.8860     | 0.2953     |
| kf-deberta-multitask                  | 0.3158     | 0.6316     | 0.2105     |
| gte-multilingual-base                 | 0.4035     | 0.8070     | 0.2690     |
| BGE-m3                                | 0.4254     | 0.8508     | 0.2836     |
| bge-m3-korean                         | 0.3684     | 0.7368     | 0.2456     |
| **BGE-m3-ko**                         | **0.4517** | **0.9035** | **0.3011** |

Cross-Encoder (Reranker)

| Model name                            | F1         | Recall     | Precision  |
|---------------------------------------|------------|------------|------------|
| gte-multilingual-reranker-base | 0.4605 | 0.9211     | 0.3070     |
| jina-reranker-v2-base-multilingual | 0.4649  | 0.9298     | 0.3099     |
| bge-reranker-v2-m3               | 0.4781     | 0.9561     | 0.3187     |
| **bge-reranker-v2-m3-ko**             | **0.4825** | **0.9649** | **0.3216** |


**Top-k 5**

Bi-Encoder (Sentence Transformer)

| Model name                            | F1         | Recall     | Precision  |
|---------------------------------------|------------|------------|------------|
| paraphrase-multilingual-mpnet-base-v2 | 0.1813     | 0.5439     | 0.1088     |
| KoSimCSE-roberta                      | 0.2164     | 0.6491     | 0.1298     |
| Cohere embed-multilingual-v3.0        | 0.2076     | 0.6228     | 0.1246     |
| openai ada 002                        | 0.2602     | 0.7807     | 0.1561     |
| multilingual-e5-large-instruct        | 0.2544     | 0.7632     | 0.1526     |
| Upstage Embedding                     | 0.2982     | 0.8947     | 0.1789     |
| paraphrase-multilingual-MiniLM-L12-v2 | 0.1637     | 0.4912     | 0.0982     |
| openai_embed_3_small                  | 0.2690     | 0.8070     | 0.1614     |
| ko-sroberta-multitask                 | 0.2164     | 0.6491     | 0.1298     |
| openai_embed_3_large                  | 0.2807     | 0.8421     | 0.1684     |
| KU-HIAI-ONTHEIT-large-v1              | 0.3041     | 0.9123     | 0.1825     |
| KU-HIAI-ONTHEIT-large-v1.1            | **0.3099** | **0.9298** | **0.1860** |
| kf-deberta-multitask                  | 0.2281     | 0.6842     | 0.1368     |
| gte-multilingual-base                 | 0.2865     | 0.8596     | 0.1719     |
| BGE-m3                                | 0.3041     | 0.9123     | 0.1825     |
| bge-m3-korean                         | 0.2661     | 0.7982     | 0.1596     |
| **BGE-m3-ko**                         | **0.3099** | **0.9298** | **0.1860** |

Cross-Encoder (Reranker)

| Model name                            | F1         | Recall     | Precision  |
|---------------------------------------|------------|------------|------------|
| gte-multilingual-reranker-base | 0.3158 | 0.9474     | 0.1895     |
| jina-reranker-v2-base-multilingual | 0.3129  | 0.9386     | 0.1877     |
| bge-reranker-v2-m3               | **0.3216** | **0.9649** | **0.1930** |
| **bge-reranker-v2-m3-ko**             | **0.3216** | **0.9649** | **0.1930** |