--- language: - zh - en base_model: openbmb/MiniCPM-2B-sft-bf16 pipeline_tag: text-classification library_name: transformers --- ## MiniCPM-Reranker **MiniCPM-Reranker** 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。 MiniCPM-Reranker 基于 [MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-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 600 万条训练数据。 欢迎关注 RAG 套件系列： - 检索模型：[MiniCPM-Embedding](https://huggingface.co/openbmb/MiniCPM-Embedding) - 重排模型：[MiniCPM-Reranker](https://huggingface.co/openbmb/MiniCPM-Reranker) - 面向 RAG 场景的 LoRA 插件：[MiniCPM3-RAG-LoRA](https://huggingface.co/openbmb/MiniCPM3-RAG-LoRA) **MiniCPM-Reranker** 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. MiniCPM-Reranker is trained based on [MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-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 RAG toolkit series: - Retrieval Model: [MiniCPM-Embedding](https://huggingface.co/openbmb/MiniCPM-Embedding) - Re-ranking Model: [MiniCPM-Reranker](https://huggingface.co/openbmb/MiniCPM-Reranker) - LoRA Plugin for RAG scenarios: [MiniCPM3-RAG-LoRA](https://huggingface.co/openbmb/MiniCPM3-RAG-LoRA) ## 模型信息 Model Information - 模型大小：2.4B - 最大输入token数：1024 - Model Size: 2.4B - Max Input Tokens: 1024 ## 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 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 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 flash-attn>2.3.5 ``` ### 示例脚本 Demo #### Huggingface Transformers ```python from transformers import AutoModel, LlamaTokenizer, AutoModelForSequenceClassification import torch import numpy as np # from https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py class MiniCPMRerankerLLamaTokenizer(LlamaTokenizer): def build_inputs_with_special_tokens( self, token_ids_0, token_ids_1 = None ): """ - single sequence: ` X ` - pair of sequences: ` A B` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return super().build_inputs_with_special_tokens(token_ids_0) bos = [self.bos_token_id] sep = [self.eos_token_id] return bos + token_ids_0 + sep + token_ids_1 model_name = "openbmb/MiniCPM-Reranker" tokenizer = MiniCPMRerankerLLamaTokenizer.from_pretrained(model_name, trust_remote_code=True) tokenizer.padding_side = "right" model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True,attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda") model.eval() @torch.no_grad() def rerank(input_query, input_docs): tokenized_inputs = tokenizer([[input_query, input_doc] for input_doc in input_docs], return_tensors="pt", padding=True, truncation=True, max_length=1024) for k in tokenized_inputs: tokenized_inputs [k] = tokenized_inputs[k].to("cuda") outputs = model(**tokenized_inputs) score = outputs.logits return score.float().detach().cpu().numpy() queries = ["中国的首都是哪里？"] passages = [["beijing", "shanghai"]] INSTRUCTION = "Query: " queries = [INSTRUCTION + query for query in queries] scores = [] for i in range(len(queries)): print(queries[i]) scores.append(rerank(queries[i],passages[i])) print(np.array(scores)) # [[[-4.7460938][-8.8515625]]] ``` #### Sentence Transformer ```python from sentence_transformers import CrossEncoder from transformers import LlamaTokenizer import torch # from https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py class MiniCPMRerankerLLamaTokenizer(LlamaTokenizer): def build_inputs_with_special_tokens( self, token_ids_0, token_ids_1 = None ): """ - single sequence: ` X ` - pair of sequences: ` A B` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return super().build_inputs_with_special_tokens(token_ids_0) bos = [self.bos_token_id] sep = [self.eos_token_id] return bos + token_ids_0 + sep + token_ids_1 model_name = "openbmb/MiniCPM-Reranker" model = CrossEncoder(model_name,max_length=1024,trust_remote_code=True, automodel_args={"attn_implementation":"flash_attention_2","torch_dtype": torch.float16}) model.tokenizer = MiniCPMRerankerLLamaTokenizer.from_pretrained(model_name, trust_remote_code=True) model.tokenizer.padding_side = "right" query = "中国的首都是哪里？" passages = [["beijing", "shanghai"]] 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) # [0.0087432861328125, 0.00020503997802734375] for ranking in rankings: print(f"Score: {ranking['score']:.4f}, Corpus: {ranking['text']}") # ID: 0, Score: 0.0087, Text: beijing # ID: 1, Score: 0.0002, Text: shanghai ``` ## 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 | ### 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** | ## 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 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 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 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 are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-Reranker weights are also available for free commercial use.