README.md

---
language:
- zh
- en
base_model: openbmb/MiniCPM-1B-sft-bf16
pipeline_tag: text-classification
tags:
- sentence-transformers
library_name: transformers
---
## MiniCPM-Reranker-Light

**MiniCPM-Reranker-Light** 是面壁智能与清华大学自然语言处理实验室（THUNLP）、东北大学信息检索小组（NEUIR）共同开发的中英双语言文本重排序模型，有如下特点：
- 出色的中文、英文重排序能力。
- 出色的中英跨语言重排序能力。
- 支持长文本（最长8192token）。

MiniCPM-Reranker-Light 基于 [MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16) 训练，结构上采取双向注意力。采取多阶段训练方式，共使用包括开源数据、机造数据、闭源数据在内的约 500 万条训练数据。

欢迎关注 UltraRAG 系列：

- 检索模型：[MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)
- 重排模型：[MiniCPM-Reranker-Light](https://huggingface.co/openbmb/MiniCPM-Reranker-Light)
- 领域自适应RAG框架：[UltraRAG](https://github.com/openbmb/UltraRAG)

**MiniCPM-Reranker-Light** is a bilingual & cross-lingual text re-ranking model developed by ModelBest Inc. , THUNLP and NEUIR , featuring:

- Exceptional Chinese and English re-ranking capabilities.
- Outstanding cross-lingual re-ranking capabilities between Chinese and English.
- Long-text support (up to 8192 tokens).

MiniCPM-Reranker-Light is trained based on [MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16) and incorporates bidirectional attention in its architecture. The model underwent multi-stage training using approximately 6 million training examples, including open-source, synthetic, and proprietary data.

We also invite you to explore the UltraRAG series:

- Retrieval Model: [MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)
- Re-ranking Model: [MiniCPM-Reranker-Light](https://huggingface.co/openbmb/MiniCPM-Reranker-Light)
- Domain Adaptive RAG Framework: [UltraRAG](https://github.com/openbmb/UltraRAG)


## 模型信息 Model Information

- 模型大小：1.2B
- 最大输入token数：8192

- Model Size: 1.2B
- Max Input Tokens: 8192

## 使用方法 Usage

### 输入格式 Input Format

本模型支持指令，输入格式如下：

MiniCPM-Reranker-Light supports instructions in the following format:

```
<s>Instruction: {{ instruction }} Query: {{ query }}</s>{{ document }}
```

例如：

For example:

```
<s>Instruction: 为这个医学问题检索相关回答。Query: 咽喉癌的成因是什么？</s>（文档省略）
```

```
<s>Instruction: Given a claim about climate change, retrieve documents that support or refute the claim. Query: However the warming trend is slower than most climate models have forecast.</s>(document omitted)
```

也可以不提供指令，即采取如下格式：

MiniCPM-Reranker-Light also works in instruction-free mode in the following format:

```
<s>Query: {{ query }}</s>{{ document }}
```

我们在BEIR与C-MTEB/Retrieval上测试时使用的指令见 `instructions.json`，其他测试不使用指令。

When running evaluation on BEIR and C-MTEB/Retrieval, we use instructions in `instructions.json`. For other evaluations, we do not use instructions. 

### 环境要求 Requirements

```
transformers==4.37.2
```

### 示例脚本 Demo

#### Huggingface Transformers

```python
from transformers import AutoModelForSequenceClassification
import torch

model_name = "OpenBMB/MiniCPM-Reranker-Light"
model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16).to("cuda")
# You can also use the following code to use flash_attention_2
# model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True,attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda")
model.eval()

query = "中国的首都是哪里？" # "Where is the capital of China?"
passages = ["beijing", "shanghai"] # 北京，上海

rerank_score = model.rerank(query, passages,query_instruction="Query:", batch_size=32, max_length=1024)
print(rerank_score) #[0.01791382 0.00024533]


sentence_pairs = [[f"Query: {query}", doc] for doc in passages]
scores = model.compute_score(sentence_pairs, batch_size=32, max_length=1024)
print(scores) #[0.01791382 0.00024533]
```

#### Sentence Transformer

```python
from sentence_transformers import CrossEncoder
from transformers import LlamaTokenizer
import torch

model_name = "OpenBMB/MiniCPM-Reranker-Light"
model = CrossEncoder(model_name,max_length=1024,trust_remote_code=True, automodel_args={"torch_dtype": torch.float16})
# You can also use the following code to use flash_attention_2
#model = CrossEncoder(model_name,max_length=1024,trust_remote_code=True, automodel_args={"attn_implementation":"flash_attention_2","torch_dtype": torch.float16})
model.tokenizer.padding_side = "right"

query = "中国的首都是哪里？" # "Where is the capital of China?"
passages = ["beijing", "shanghai"] # 北京，上海

INSTRUCTION = "Query: "
query = INSTRUCTION + query

sentence_pairs = [[query, doc] for doc in passages]

scores = model.predict(sentence_pairs, convert_to_tensor=True).tolist()
rankings = model.rank(query, passages, return_documents=True, convert_to_tensor=True)

print(scores) # [0.017913818359375, 0.0002453327178955078]
for ranking in rankings:
    print(f"Score: {ranking['score']:.4f}, Corpus: {ranking['text']}")
  
# Score: 0.0179, Corpus: beijing
# Score: 0.0002, Corpus: shanghai
```

#### Infinity

```python
import asyncio
from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine
query = "中国的首都是哪里？" # "What is the capital of China?"
docs = ["beijing", "shanghai"] # "北京", "上海"

INSTRUCTION = "Query:"
query = f"{INSTRUCTION} {query}"

array = AsyncEngineArray.from_args(
  [EngineArgs(model_name_or_path = "OpenBMB/MiniCPM-Reranker-Light", engine="torch", dtype="float16", bettertransformer=False, trust_remote_code=True, model_warmup=False)]
)

async def rerank(engine: AsyncEmbeddingEngine): 
    async with engine:
        ranking, usage = await engine.rerank(query=query, docs=docs)
        print(list(zip(ranking, docs)))

asyncio.run(rerank(array[0])) # [(RerankReturnType(relevance_score=0.017917344, document='beijing', index=0), 'beijing'), (RerankReturnType(relevance_score=0.00024729347, document='shanghai', index=1), 'shanghai')]
```

#### FlagEmbedding

```python
from FlagEmbedding import FlagReranker
model_name = "OpenBMB/MiniCPM-Reranker-Light"
model = FlagReranker(model_name, use_fp16=True, query_instruction_for_rerank="Query: ", trust_remote_code=True)
# You can hack the __init__() method of the FlagEmbedding BaseReranker class to use flash_attention_2 for faster inference
#  self.model = AutoModelForSequenceClassification.from_pretrained(
#             model_name_or_path,
#             trust_remote_code=trust_remote_code,
#             cache_dir=cache_dir,
#             # torch_dtype=torch.float16, # we need to add this line to use fp16
#             # attn_implementation="flash_attention_2", # we need to add this line to use flash_attention_2
#         )
model.tokenizer.padding_side = "right"

query = "中国的首都是哪里？" # "Where is the capital of China?"
passages = ["beijing", "shanghai"] # 北京，上海

sentence_pairs = [[query, doc] for doc in passages]

scores = model.compute_score(sentence_pairs,normalize=True)
print(scores) # [0.01791734476747132, 0.0002472934613244585]
```

## 实验结果 Evaluation Results

### 中文与英文重排序结果 CN/EN Re-ranking Results

中文对`bge-large-zh-v1.5`检索的top-100进行重排，英文对`bge-large-en-v1.5`检索的top-100进行重排。

We re-rank top-100 docments from `bge-large-zh-v1.5` in C-MTEB/Retrieval and from `bge-large-en-v1.5` in BEIR.


| 模型 Model            | C-MTEB/Retrieval (NDCG@10) | BEIR (NDCG@10) |
|----------------------------|-------------------|---------------|
| bge-large-zh-v1.5（Retriever for Chinese）  | 70.46             | -             |
| bge-large-en-v1.5（Retriever for English）  | -                 | 54.29         |
| bge-reranker-v2-m3         | 71.82             | 55.36         |
| bge-reranker-v2-minicpm-28 | 73.51             | 59.86         |
| bge-reranker-v2-gemma      | 71.74             | 60.71         |
| bge-reranker-v2.5-gemma2   | -                 | 63.67    |
| MiniCPM-Reranker                 | 76.79         | 61.32        |
| MiniCPM-Reranker-Light                | 76.19      |	61.34       |

### 中英跨语言重排序结果 CN-EN Cross-lingual Re-ranking Results

对bge-m3(Dense)检索的top100进行重排。

We re-rank top-100 documents from `bge-m3` (Dense).

| 模型 Model                      | MKQA En-Zh_CN (Recall@20) | NeuCLIR22 (NDCG@10) | NeuCLIR23 (NDCG@10) |
|------------------------------------|--------------------|--------------------|--------------------|
| bge-m3 (Dense)（Retriever）              | 66.4               | 30.49              | 41.09              |
| jina-reranker-v2-base-multilingual | 69.33              | 36.66              | 50.03              |
| bge-reranker-v2-m3                 | 69.75              | 40.98              | 49.67              |
| gte-multilingual-reranker-base     | 68.51              | 38.74              | 45.3              |
| MiniCPM-Reranker                         | 71.73          | 43.65          | 50.59          |
| MiniCPM-Reranker-Light                        | 71.34       | 46.04       | 51.86       |

## 许可证 License

- 本仓库中代码依照 [Apache-2.0 协议](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE)开源。
- MiniCPM-Reranker-Light 模型权重的使用则需要遵循 [MiniCPM 模型协议](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md)。
- MiniCPM-Reranker-Light 模型权重对学术研究完全开放。如需将模型用于商业用途，请填写[此问卷](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g)。

* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
* The usage of MiniCPM-Reranker-Light model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
* The models and weights of MiniCPM-Reranker-Light are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-Reranker-Light weights are also available for free commercial use.