--- language: - zh - en base_model: openbmb/MiniCPM-2B-sft-bf16 model-index: - name: MiniCPM-Embedding results: - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: ndcg_at_10 value: 64.65 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: ndcg_at_10 value: 46.53 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: ndcg_at_10 value: 35.55 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: ndcg_at_10 value: 47.82 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: ndcg_at_10 value: 90.76 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: ndcg_at_10 value: 56.64 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: ndcg_at_10 value: 78.11 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: ndcg_at_10 value: 43.93 - task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: ndcg_at_10 value: 39.77 - task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: ndcg_at_10 value: 69.29 - task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 89.97 - task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: ndcg_at_10 value: 22.38 - task: type: Retrieval dataset: type: mteb/scifact name: MTEB SciFact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: ndcg_at_10 value: 86.6 - task: type: Retrieval dataset: type: mteb/trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: ndcg_at_10 value: 81.32 - task: type: Retrieval dataset: type: mteb/touche2020 name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: ndcg_at_10 value: 25.08 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics: - type: ndcg_at_10 value: 46.05 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics: - type: ndcg_at_10 value: 92.01 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics: - type: ndcg_at_10 value: 90.98 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics: - type: ndcg_at_10 value: 70.21 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics: - type: ndcg_at_10 value: 85.55 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics: - type: ndcg_at_10 value: 63.91 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: 8731a845f1bf500a4f111cf1070785c793d10e64 metrics: - type: ndcg_at_10 value: 87.33 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics: - type: ndcg_at_10 value: 78.05 pipeline_tag: feature-extraction tags: - mteb --- ## MiniCPM-Embedding **MiniCPM-Embedding** 是面壁智能与清华大学自然语言处理实验室（THUNLP）共同开发的中英双语言文本嵌入模型，有如下特点： - 出色的中文、英文检索能力。 - 出色的中英跨语言检索能力。 MiniCPM-Embedding 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和 Weighted Mean Pooling [1]。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-Embedding** is a bilingual & cross-lingual text embedding model developed by ModelBest Inc. and THUNLP, featuring: - Exceptional Chinese and English retrieval capabilities. - Outstanding cross-lingual retrieval capabilities between Chinese and English. MiniCPM-Embedding is trained based on [MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) and incorporates bidirectional attention and Weighted Mean Pooling [1] 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) [1] Muennighoff, N. (2022). Sgpt: Gpt sentence embeddings for semantic search. arXiv preprint arXiv:2202.08904. ## 模型信息 Model Information - 模型大小：2.4B - 嵌入维度：2304 - 最大输入token数：512 - Model Size: 2.4B - Embedding Dimension: 2304 - Max Input Tokens: 512 ## 使用方法 Usage ### 输入格式 Input Format 本模型支持 query 侧指令，格式如下： MiniCPM-Embedding supports query-side instructions in the following format: ``` Instruction: {{ instruction }} Query: {{ query }} ``` 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？ ``` ``` 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. ``` 也可以不提供指令，即采取如下格式： MiniCPM-Embedding also works in instruction-free mode in the following format: ``` Query: {{ query }} ``` 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。文档侧直接输入文档原文。 When running evaluation on BEIR and C-MTEB/Retrieval, we use instructions in `instructions.json`. For other evaluations, we do not use instructions. On the document side, we directly use the bare document as the input. ### 环境要求 Requirements ``` transformers==4.37.2 flash-attn>2.3.5 ``` ### 示例脚本 Demo ```python from transformers import AutoModel, AutoTokenizer import torch import torch.nn.functional as F model_name = "openbmb/MiniCPM-Embedding" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda") model.eval() def weighted_mean_pooling(hidden, attention_mask): attention_mask_ = attention_mask * attention_mask.cumsum(dim=1) s = torch.sum(hidden * attention_mask_.unsqueeze(-1).float(), dim=1) d = attention_mask_.sum(dim=1, keepdim=True).float() reps = s / d return reps @torch.no_grad() def encode(input_texts): batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt', return_attention_mask=True).to("cuda") outputs = model(**batch_dict) attention_mask = batch_dict["attention_mask"] hidden = outputs.last_hidden_state reps = weighted_mean_pooling(hidden, attention_mask) embeddings = F.normalize(reps, p=2, dim=1).detach().cpu().numpy() return embeddings queries = ["中国的首都是哪里？"] passages = ["beijing", "shanghai"] INSTRUCTION = "Query: " queries = [INSTRUCTION + query for query in queries] embeddings_query = encode(queries) embeddings_doc = encode(passages) scores = (embeddings_query @ embeddings_doc.T) print(scores.tolist()) # [[0.3535913825035095, 0.18596848845481873]] ``` ## 实验结果 Evaluation Results ### 中文与英文检索结果 CN/EN Retrieval Results | 模型 Model | C-MTEB/Retrieval (NDCG@10) | BEIR (NDCG@10) | |------------------------------|-------------------|---------------| | bge-large-zh-v1.5 | 70.46 | - | | gte-large-zh | 72.49 | - | | Zhihui_LLM_Embedding | 76.74 | | | bge-large-en-v1.5 | - | 54.29 | | gte-en-large-v1.5 | - | 57.91 | | NV-Retriever-v1 | - | 60.9 | | bge-en-icl | - | 62.16 | | NV-Embed-v2 | - | 62.65 | | me5-large | 63.66 | 51.43 | | bge-m3(Dense) | 65.43 | 48.82 | | gte-multilingual-base(Dense) | 71.95 | 51.08 | | gte-Qwen2-1.5B-instruct | 71.86 | 58.29 | | gte-Qwen2-7B-instruct | 76.03 | 60.25 | | bge-multilingual-gemma2 | 73.73 | 59.24 | | MiniCPM-Embedding | **76.76** | 58.56 | | MiniCPM-Embedding+MiniCPM-Reranker | 77.08 | 61.61 | ### 中英跨语言检索结果 CN-EN Cross-lingual Retrieval Results | 模型 Model | MKQA En-Zh_CN (Recall@20) | NeuCLIR22 (NDCG@10) | NeuCLIR23 (NDCG@10) | |------------------------------|--------------------|--------------------|--------------------| | me5-large | 44.3 | 9.01 | 25.33 | | bge-m3(Dense) | 66.4 | 30.49 | 41.09 | | gte-multilingual-base(Dense) | 68.2 | 39.46 | 45.86 | | gte-Qwen2-1.5B-instruct | 68.52 | 49.11 | 45.05 | | gte-Qwen2-7B-instruct | 68.27 | 49.14 | 49.6 | | MiniCPM-Embedding | **72.95** | **52.65** | **49.95** | | MiniCPM-Embedding+MiniCPM-Reranker | 74.33 | 53.21 | 54.12 | ## 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-Embedding 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-Embedding 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-Embedding 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-Embedding are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-Embedding weights are also available for free commercial use.