E5 mistral 7b instruct Improving Text Embeddings with Large Language Models. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 32 layers and the embedding size is 4096. Usage Below is an example to encode queries and passages from the MS MARCO passage ranking dataset. Sentence Transformers Have a look at config sentence transformers.json for the prompts that are pre configured, such as web search query , sts query , and summarization query . Additionally, check out unilm/e5/utils.py for prompts we used for evaluation. You can use these via e.g. model.encode(queries, prompt="Instruct: Given a claim, find documents that refute the claim\nQuery: ") . Transformers Supported Languages This model is initialized from Mistral 7B v0.1 and fine tuned on a mixture of multilingual datasets. As a result, it has some multilingual capability. However, since Mistral 7B v0.1 is mainly trained on English data, we recommend using this model for English only. For multilingual use cases, please refer to multilingual e5 large. MTEB Benchmark Evaluation Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark. FAQ 1. Do I need…
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