diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -1,3 +1,12212 @@ ---- -license: apache-2.0 ---- +--- +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 + - type: main_score + value: 40.342262626966686 + - type: manhattan_pearson + value: 43.09428997760782 + - type: manhattan_spearman + value: 40.132604575720485 + - type: pearson + value: 40.89963324512739 + - type: spearman + value: 40.342262626966686 + task: + type: STS + - dataset: + config: default + name: MTEB ATEC (default) + revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 + split: validation + type: C-MTEB/ATEC + metrics: + - type: cosine_pearson + value: 40.97674579633659 + - type: cosine_spearman + value: 41.15073385665892 + - type: euclidean_pearson + value: 43.12674145119401 + - type: euclidean_spearman + value: 41.15073497290901 + - type: main_score + value: 41.15073385665892 + - type: manhattan_pearson + value: 43.016332350517416 + - type: manhattan_spearman + value: 40.99128368771293 + - type: pearson + value: 40.97674579633659 + - type: spearman + value: 41.15073385665892 + task: + type: STS + - dataset: + config: en + name: MTEB AmazonCounterfactualClassification (en) + revision: e8379541af4e31359cca9fbcf4b00f2671dba205 + split: test + type: mteb/amazon_counterfactual + metrics: + - type: accuracy + value: 74.1492537313433 + - type: ap + value: 36.58820102143676 + - type: ap_weighted + value: 36.58820102143676 + - type: f1 + value: 67.93641050300623 + - type: f1_weighted + value: 76.47946936836382 + - type: main_score + value: 74.1492537313433 + task: + type: Classification + - dataset: + config: default + name: MTEB AmazonPolarityClassification (default) + revision: e2d317d38cd51312af73b3d32a06d1a08b442046 + split: test + type: mteb/amazon_polarity + metrics: + - type: accuracy + value: 92.57937499999998 + - type: ap + value: 89.09881932276382 + - type: ap_weighted + value: 89.09881932276382 + - type: f1 + value: 92.57389464257594 + - type: f1_weighted + value: 92.57389464257594 + - 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dataset: + config: default + name: MTEB AskUbuntuDupQuestions (default) + revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 + split: test + type: mteb/askubuntudupquestions-reranking + metrics: + - type: main_score + value: 60.45095169935259 + - type: map + value: 60.45095169935259 + - type: mrr + value: 73.43567251461988 + - type: nAUC_map_diff1 + value: 15.357222913791704 + - type: nAUC_map_max + value: 24.301239659848346 + - type: nAUC_map_std + value: 18.26732583044278 + - type: nAUC_mrr_diff1 + value: 24.108010981589057 + - type: nAUC_mrr_max + value: 34.90261214387396 + - type: nAUC_mrr_std + value: 20.350034497982126 + task: + type: Reranking + - dataset: + config: default + name: MTEB BIOSSES (default) + revision: d3fb88f8f02e40887cd149695127462bbcf29b4a + split: test + type: mteb/biosses-sts + metrics: + - type: cosine_pearson + value: 90.16604991710759 + - type: cosine_spearman + value: 88.4670760233051 + - type: euclidean_pearson + value: 89.02378164860428 + - type: euclidean_spearman + value: 88.4670760233051 + - 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type: main_score + value: 39.85718105201471 + - type: v_measure + value: 39.85718105201471 + - type: v_measure_std + value: 0.9098592525717781 + task: + type: Clustering + - dataset: + config: default + name: MTEB BiorxivClusteringS2S (default) + revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 + split: test + type: mteb/biorxiv-clustering-s2s + metrics: + - type: main_score + value: 34.901371726743854 + - type: v_measure + value: 34.901371726743854 + - type: v_measure_std + value: 0.49131958662099773 + task: + type: Clustering + - dataset: + config: default + name: MTEB CLSClusteringP2P (default) + revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 + split: test + type: C-MTEB/CLSClusteringP2P + metrics: + - type: main_score + value: 42.580911514601844 + - type: v_measure + value: 42.580911514601844 + - type: v_measure_std + value: 1.3262494874619402 + task: + type: Clustering + - dataset: + config: default + name: MTEB CLSClusteringS2S (default) + revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f + split: test + type: C-MTEB/CLSClusteringS2S + metrics: + - 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type: precision_at_1000 + value: 0.099 + - type: precision_at_20 + value: 4.625 + - type: precision_at_3 + value: 26.367 + - type: precision_at_5 + value: 16.8 + - type: recall_at_1 + value: 63.1 + - type: recall_at_10 + value: 88.8 + - type: recall_at_100 + value: 96.6 + - type: recall_at_1000 + value: 98.5 + - type: recall_at_20 + value: 92.5 + - type: recall_at_3 + value: 79.10000000000001 + - type: recall_at_5 + value: 84.0 + task: + type: Retrieval + - dataset: + config: default + name: MTEB Waimai (default) + revision: 339287def212450dcaa9df8c22bf93e9980c7023 + split: test + type: C-MTEB/waimai-classification + metrics: + - 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 RAG toolkit 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���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.