README.md · openbmb/MiniCPM-Embedding at refs/pr/2

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
  - sentence-transformers
library_name: transformers

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()

# 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.