chunk_content: ct_embs, axis=1)
  
  
  def get_loss(cosine_score, labels):
      return torch.mean(torch.square(labels * (1 - cosine_score) + torch.clamp((1 - labels) * cosine_score, min=0.0)))
  The get_cosine_embeddings function computes the cosine similarity and the get_loss function computes the loss. The loss enables the model to learn that a cosine score of 1 for query and product pairs is relevant, and a cosine score of 0 or below is irrelevant.
  Define the Peft