chunk_content: t deep dive
  	
  We finetune e5-large-v2 which tops the MTEB benchmark using PEFT-LoRA. 
  AutoModelForSentenceEmbedding returns the query and product embeddings, and the mean_pooling function pools them across the sequence dimension and normalizes them:
  
  
  	Copied
  class AutoModelForSentenceEmbedding(nn.Module):
      def __init__(self, model_name, tokenizer, normalize=True):
          super(AutoModelForSentenceEmbedding, self).__init__()
  
          self.m