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MiniCPM-Embedding

MiniCPM-Embedding 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:

MiniCPM-Embedding is a bilingual & cross-lingual text embedding model developed by ModelBest Inc. , THUNLP and NEUIR , 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:

[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()

# 由于在 `model.forward` 中缩放了最终隐层表示,此处的 mean pooling 实际上起到了 weighted mean pooling 的作用
# As we scale hidden states in `model.forward`, mean pooling here actually works as weighted mean pooling
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})

queries = ["中国的首都是哪里?"]
passages = ["beijing", "shanghai"]

INSTRUCTION = "Query: "

embeddings_query = model.encode(queries, prompt=INSTRUCTION)
embeddings_doc = model.encode(passages)

scores = (embeddings_query @ embeddings_doc.T)
print(scores.tolist())  # [[0.35365450382232666, 0.18592746555805206]]

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.
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