NV-Embed model accuracy benchmark Now that we have discussed the underlying benchmarks and metrics, let’s see how our new model NV-Embed performed. Figure 1. The top five models on the MTEB benchmark Tracking accuracy across 56 tasks, on average, the NV-Embed model performs best with an...
3. MTEB and C-MTEB leaderboard: 训练 1. pretrain阶段: 2. finetune阶段: 3. reranker阶段: 效果 其他 最近有用到bge embedding,简单记录下学习的内容。向量模型可以将任意文本映射为低维稠密向量,以用于检索、分类、聚类或语义匹配等任务,并可支持为大模型调用外部知识,成为RAG[1]必不可少的一部分。BGE[...
目前的leaderboard中存在的一些用参数规模明显大的模型，这些模型生成的embedding维度更高，从评分来说也确...
在支撑这些大型语言模型应用落地方面,文本向量化模型(Embedding Model)的重要性也不言而喻。 近期,我在浏览huggingface发现,国产自研文本向量化模型acge_text_embedding(以下简称“acge模型”)已经在业界权威的中文语义向量评测基准C-MTEB(Chinese Massive Text Embedding Benchmark)中获得了第一名。今天这篇文章将围绕以下...
在https://huggingface.co/spaces/mteb/leaderboard上可以看到,acge模型已经在目前业界最全面、最权威的中文语义向量评测基准C-MTEB(Chinese Massive Text Embedding Benchmark)的榜单中获得了第一名的成绩。 由上表可以看到,acge_text_embedding模型在“Classification Average (9 datasets)”这一列中,acge_text_embeddi...
evaluation.run(RetrievalModel(encoder), output_folder=args.output_dir, overwrite_results=False)else: evaluation.run(encoder, output_folder=args.output_dir, overwrite_results=False) 在https://huggingface.co/spaces/mteb/leaderboard上可以看到,acge模型已经在目前业界最全面、最权威的中文语义向量评测基准C-...
evaluation.run(RetrievalModel(encoder), output_folder=args.output_dir, overwrite_results=False) else: evaluation.run(encoder, output_folder=args.output_dir, overwrite_results=False) 在https://huggingface.co/spaces/mteb/leaderboard上可以看到,acge模型已经在目前业界最全面、最权威的中文语义向量评测基准C...
effectiveness of our approach is validated by our model’s top-ranking performance on the Chinese leaderboard of the Massive Text Embedding Bench-mark. We hope our method inspires more works to explore new ways of hard negative mining. The model has been uploaded to Huggingface: Conan-embedding-...
选择合适的嵌入模型（embedding model）对于语义检索系统的性能至关重要。以下是一些选择嵌入模型的关键因素...
Let’s look at the Overall tab since it provides a comprehensive summary of each embedding model. However, note that we have sorted the leaderboard by the Retrieval Average column. This is because RAG is a retrieval task and we want to see the best retrieval embedding models at the top. ...