--- 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: ``` Instruction: {{ instruction }} Query: {{ query }}{{ document }} ``` 例如： For example: ``` Instruction: 为这个医学问题检索相关回答。Query: 咽喉癌的成因是什么？（文档省略） ``` ``` 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.(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: ``` Query: {{ query }}{{ 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.