metadata
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 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
- 重排模型：MiniCPM-Reranker
- 面向 RAG 场景的 LoRA 插件：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 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
- Re-ranking Model: MiniCPM-Reranker
- LoRA Plugin for RAG scenarios: 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
Huggingface Transformers
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()
# 事实上我们用的是weighted mean pooling，但为了部署方便，我们将一部分pooling步骤集成在model.forward中
# In fact, we will use weighted mean pooling, but we will integrate some pooling steps into model.forward for deployment convenience
def mean_pooling(hidden,attention_mask):
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 = 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]]
Sentence Transformers
import torch
from sentence_transformers import SentenceTransformer
model_name = "openbmb/MiniCPM-Embedding"
model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"attn_implementation":"flash_attention_2", "torch_dtype":torch.float16})
model.max_seq_length = 512
model.tokenizer.padding_side="right"
queries = ["中国的首都是哪里？"]
passages = ["beijing", "shanghai"]
INSTRUCTION = "Query: "
embeddings_query = model.encode(queries, prompt=INSTRUCTION, normalize_embeddings=True)
embeddings_doc = model.encode(passages, normalize_embeddings=True)
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 协议开源。
- MiniCPM-Embedding 模型权重的使用则需要遵循 MiniCPM 模型协议。
- 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。
- The code in this repo is released under the Apache-2.0 License.
- The usage of MiniCPM-Embedding model weights must strictly follow MiniCPM Model License.md.
- The models and weights of MiniCPM-Embedding are completely free for academic research. After filling out a "questionnaire" for registration, MiniCPM-Embedding weights are also available for free commercial use.