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Gimini and deepseek answer Yes, Yes, No
Let's analyze each statement about transformer models:
Statement 1: A transformer model architecture uses self-attention.
Answer: Yes
Explanation: Self-attention is a fundamental and defining characteristic of transformer models. It allows the model to weigh the importance of different parts of the input sequence when processing it, capturing long-range dependencies and relationships within the data.
Statement 2: A transformer model architecture includes an encoder block and a decoder block.
Answer: Yes
Explanation: The typical transformer architecture includes both an encoder and a decoder. The encoder processes the input sequence, and the decoder generates the output sequence. Both blocks are composed of multiple layers, each containing self-attention mechanisms and other sub-layers.
Statement 3: A transformer model architecture includes an encryption block or a decryption block.
Answer: No
Explanation: While transformers are used in some cryptographic applications, encryption and decryption are not inherent components of the standard transformer architecture. Transformers are primarily designed for sequence-to-sequence tasks like natural language processing, not for cryptographic operations. The "encoder" and "decoder" terminology in transformers is related to how they process input and generate output sequences, not to encryption/decryption....