E5 small v2 Text Embeddings by Weakly Supervised Contrastive Pre training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 12 layers and the embedding size is 384. Usage Below is an example to encode queries and passages from the MS MARCO passage ranking dataset. Training Details Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf. Benchmark Evaluation Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark. Support for Sentence Transformers Below is an example for usage with sentence transformers. Package requirements pip install sentence transformers~=2.2.2 Contributors: michaelfeil FAQ 1. Do I need to add the prefix "query: " and "passage: " to input texts? Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad hoc information retrieval. Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. Use "query: " prefix if you want to use embeddings as features…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy