Add ColPali with double-head architecture #22
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ManuelFay
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Aug 28, 2024
| loss_rowwise = self.matryoshka_loss(scores, torch.arange(scores.shape[0], device=scores.device)) | ||
| return loss_rowwise | ||
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| def multi_vector_loss(self, query_embeddings, doc_embeddings) -> torch.Tensor: |
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this will eventually have to be rebased for the mined negatives to work
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Objective
Create a variant of ColPali that can output both dense and multi-vector embeddings.