--- model-index: - name: no_model_name_available results: - dataset: config: default name: MTEB AFQMC (default) revision: b44c3b011063adb25877c13823db83bb193913c4 split: validation type: C-MTEB/AFQMC metrics: - type: cosine_pearson value: 31.60219205269865 - type: cosine_spearman value: 32.26566089398552 - type: euclidean_pearson value: 31.38659295608159 - type: euclidean_spearman value: 32.265680997074284 - type: main_score value: 32.26566089398552 - type: manhattan_pearson value: 31.012318343485934 - type: manhattan_spearman value: 31.881347232593882 - type: pearson value: 31.60219205269865 - type: spearman value: 32.26566089398552 task: type: STS - dataset: config: default name: MTEB ATEC (default) revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 split: test type: C-MTEB/ATEC metrics: - type: cosine_pearson value: 40.89963324512739 - type: cosine_spearman value: 40.342262626966686 - type: euclidean_pearson value: 43.26579075620696 - type: euclidean_spearman value: 40.34226375259283 - 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type: accuracy value: 85.77999999999999 - type: ap value: 68.2216414015039 - type: ap_weighted value: 68.2216414015039 - type: f1 value: 83.82399258910729 - type: f1_weighted value: 85.78923735869199 - type: main_score value: 85.77999999999999 task: type: Classification pipeline_tag: feature-extraction tags: - mteb - sentence-transformers library_name: transformers --- ## UltraRAG-Embedding **UltraRAG-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。 - 支持长文本（最长8192token）。 - 提供稠密向量与token级别的稀疏向量。 - 可变的稠密向量维度（套娃表征）。 UltraRAG-Embedding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 260M 条训练数据。 欢迎关注 UltraRAG 系列： - 检索模型：[UltraRAG-Embedding](https://huggingface.co/openbmb/UltraRAG-Embedding) - 重排模型：[UltraRAG-Reranker](https://huggingface.co/openbmb/UltraRAG-Reranker) - 领域自适应RAG框架：[UltraRAG](https://github.com/openbmb/UltraRAG) **UltraRAG-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. - Long-text support (up to 8192 tokens). - Dense vectors and token-level sparse vectors. - Variable dense vector dimensions (Matryoshka representation). UltraRAG-Embedding incorporates bidirectional attention and Weighted Mean Pooling [1] in its architecture. The model underwent multi-stage training using approximately 260 million training examples, including open-source, synthetic, and proprietary data. We also invite you to explore the UltraRAG series: - Retrieval Model: [UltraRAG-Embedding](https://huggingface.co/openbmb/UltraRAG-Embedding) - Re-ranking Model: [UltraRAG-Reranker](https://huggingface.co/openbmb/UltraRAG-Reranker) - Domain Adaptive RAG Framework: [UltraRAG](https://github.com/openbmb/UltraRAG) [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：440M - 嵌入维度：1024 - 最大输入token数：8192 - Model Size: 440M - Embedding Dimension: 1024 - Max Input Tokens: 8192 ## 使用方法 Usage ### 输入格式 Input Format 本模型支持 query 侧指令，格式如下： UltraRAG-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： UltraRAG-Embedding also works in instruction-free mode in the following format: ``` Query: {{ query }} ``` ### 环境要求 Requirements ``` transformers==4.37.2 ``` ### 示例脚本 Demo #### Huggingface Transformers ```python from transformers import AutoModel import torch model_name = "OpenBMB/UltraRAG-Embedding" model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16).to("cuda") # you can use flash_attention_2 for faster inference # model = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda") model.eval() queries = ["MiniCPM-o 2.6 A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone"] passages = ["MiniCPM-o 2.6 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for real-time speech conversation and multimodal live streaming."] embeddings_query_dense, embeddings_query_sparse = model.encode_query(queries, return_sparse_vectors=True) embeddings_doc_dense, embeddings_doc_sparse = model.encode_corpus(passages, return_sparse_vectors=True) dense_scores = (embeddings_query_dense @ embeddings_doc_dense.T) print(dense_scores.tolist()) # [[0.6512398719787598]] print(model.compute_sparse_score_dicts(embeddings_query_sparse, embeddings_doc_sparse)) # [[0.27202296]] dense_scores, sparse_scores, mixed_scores = model.compute_score(queries, passages) print(dense_scores) # [[0.65123993]] print(sparse_scores) # [[0.27202296]] print(mixed_scores) # [[0.73284686]] ``` #### Sentence Transformers ```python import torch from sentence_transformers import SentenceTransformer model_name = "openbmb/UltraRAG-Embedding" model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"torch_dtype": torch.float16}) # you can use flash_attention_2 for faster inference # 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？"] # "What is the capital of China?" passages = ["beijing", "shanghai"] # "北京", "上海" INSTRUCTION = "Query: " embeddings_query = model.encode(queries, prompt=INSTRUCTION) embeddings_doc = model.encode(passages) scores = (embeddings_query @ embeddings_doc.T) print(scores.tolist()) # [[0.40356746315956116, 0.36183440685272217]] ``` #### Infinity ```python import asyncio from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine import numpy as np array = AsyncEngineArray.from_args([ EngineArgs(model_name_or_path = "OpenBMB/UltraRAG-Embedding", engine="torch", dtype="float16", bettertransformer=False, pooling_method="mean", trust_remote_code=True), ]) queries = ["中国的首都是哪里？"] # "What is the capital of China?" passages = ["beijing", "shanghai"] # "北京", "上海" INSTRUCTION = "Query:" queries = [f"{INSTRUCTION} {query}" for query in queries] async def embed_text(engine: AsyncEmbeddingEngine,sentences): async with engine: embeddings, usage = await engine.embed(sentences=sentences) return embeddings queries_embedding = asyncio.run(embed_text(array[0],queries)) passages_embedding = asyncio.run(embed_text(array[0],passages)) scores = (np.array(queries_embedding) @ np.array(passages_embedding).T) print(scores.tolist()) # [[0.40356746315956116, 0.36183443665504456]] ``` #### FlagEmbedding ```python from FlagEmbedding import FlagModel model = FlagModel("OpenBMB/UltraRAG-Embedding", query_instruction_for_retrieval="Query: ", pooling_method="mean", trust_remote_code=True, normalize_embeddings=True, use_fp16=True) # You can hack the __init__() method of the FlagEmbedding BaseEmbedder class to use flash_attention_2 for faster inference # self.model = AutoModel.from_pretrained( # model_name_or_path, # trust_remote_code=trust_remote_code, # cache_dir=cache_dir, # # torch_dtype=torch.float16, # we need to add this line to use fp16 # # attn_implementation="flash_attention_2", # we need to add this line to use flash_attention_2 # ) queries = ["中国的首都是哪里？"] # "What is the capital of China?" passages = ["beijing", "shanghai"] # "北京", "上海" embeddings_query = model.encode_queries(queries) embeddings_doc = model.encode_corpus(passages) scores = (embeddings_query @ embeddings_doc.T) print(scores.tolist()) # [[0.40356746315956116, 0.36183440685272217]] ``` ## 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 | - | | Conan-embedding-v1 | 76.67 | | | bge-large-en-v1.5 | - | 54.29 | | modernbert-embed-large | - | 54.36 | | snowflake-arctic-embed-l | - | 55.98 | | gte-en-large-v1.5 | - | 57.91 | | me5-large | 63.66 | 51.43 | | bge-m3(Dense) | 65.43 | 48.82 | | gte-multilingual-base(Dense) | 71.95 | 51.08 | | jina-embeddings-v3 | 68.60 | 53.88 | | gte-Qwen2-1.5B-instruct | 71.86 | 58.29 | | MiniCPM-Embedding | 76.76 | 58.56 | | UltraRAG-Embedding(Dense) | 72.71 | 55.27 | | UltraRAG-Embedding(Dense+Sparse) | 73.13 | 56.31 | | UltraRAG-Embedding(Dense+Sparse)+UltraRAG-Reranker | 76.34 | 61.49 | ### 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 | | MiniCPM-Embedding | 72.95 | 52.65 | 49.95 | | UltraRAG-Embedding(Dense) | 68.29 | 41.17 | 45.83 | | UltraRAG-Embedding(Dense)+UltraRAG-Reranker | 71.86 | 54.32 | 56.50 | ## 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。 - UltraRAG-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)。 - UltraRAG-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 UltraRAG-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 UltraRAG-Embedding are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, UltraRAG-Embedding weights are also available for free commercial use.