| --- |
| language: |
| - zh |
| - en |
| base_model: openbmb/MiniCPM-2B-sft-bf16 |
| pipeline_tag: text-ranking |
| library_name: transformers |
| tags: |
| - sentence-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: |
|
|
| ``` |
| <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 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 |
| flash-attn>2.3.5 |
| ``` |
|
|
| ### 示例脚本 Demo |
|
|
| #### Huggingface Transformers |
|
|
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| import torch |
| import numpy as np |
| |
| |
| |
| model_name = "openbmb/MiniCPM-Reranker" |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
| tokenizer.padding_side = "right" |
| |
| 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() |
| |
| @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 |
| import torch |
| |
| # |
| model_name = "openbmb/MiniCPM-Reranker" |
| 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？" |
| 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. |
