We have this sort of contrast of loss that we're training the model of the MLP with. And so that allows again for the cosign similarities to work out, similar to how the clip or the original clip models between. So then we can get the sort of embeddings that work well with this sort of retrieval task. The next step is doing this sort of aligning. It's hard if you do like end to end, it would be hard to get the best performance for both both both tasks,. dividing up into these two process processes, these two pipelines.

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