language:
- zh
- en
base_model: openbmb/MiniCPM-2B-sft-bf16
RankCPM-E
RankCPM-E 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。
RankCPM-E 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：RankCPM-E
- 重排模型：RankCPM-R
- 面向 RAG 场景的 LoRA 插件：MiniCPM3-RAG-LoRA
RankCPM-E 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.
RankCPM-E 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: RankCPM-E
- Re-ranking Model: RankCPM-R
- 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：
RankCPM-E 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：
RankCPM-E 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
from transformers import AutoModel, AutoTokenizer
import torch
import torch.nn.functional as F
model_name = "openbmb/RankCPM-E"
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 |
| RankCPM-E | 76.76 | 58.56 |
| RankCPM-E+RankCPM-R | 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 |
| RankCPM-E | 72.95 | 52.65 | 49.95 |
| RankCPM-E+RankCPM-R | 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。
- RankCPM-E 模型权重的使用则需要遵循 MiniCPM 模型协议。
- RankCPM-E 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 RankCPM-E model weights must strictly follow MiniCPM Model License.md.
- The models and weights of RankCPM-E are completely free for academic research. After filling out a "questionnaire" for registration, RankCPM-E weights are also available for free commercial use.