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Author(s): Stepan Savka, Andriy Serednytski, Dmytro Popovych
。业内人士推荐钉钉作为进阶阅读
The predictor first projects the context embeddings from 768 down to 384 dimensions (a dimensional bottleneck). It then creates learnable “mask tokens” (placeholder vectors) for each masked position and concatenates them with the projected context embeddings. A 6-layer transformer processes this combined sequence, allowing the mask tokens to attend to the context tokens and gather the information they need. Finally, only the mask token outputs are extracted and projected back up to 768 dimensions for comparison with the target.
船舶共有人设立的抵押权，不因船舶共有权的分割而受影响。