Dive deep into the world of Transformer models and algorithmic understanding in neural networks.
In the spirit of the agenda for , this post investigates the “algorithms” learned by a transformer model for tackling a narrow technical task —a modified version of the “Valid Parentheses” Leetcode problem.
START_TOKEN, START_TOKEN_IDX="", 0 self.PAD_TOKEN, PAD_TOKEN_IDX="", 1 self.END_TOKEN, END_TOKEN_IDX="", 2 util_tokens_t_to_i={self.START_TOKEN: START_TOKEN_IDX, self.PAD_TOKEN: PAD_TOKEN_IDX, self.END_TOKEN: END_TOKEN_IDX} util_tokens_i_to_t={START_TOKEN_IDX: self.START_TOKEN, PAD_TOKEN_IDX: self.PAD_TOKEN, END_TOKEN_IDX: self.END_TOKEN} self.enforce_context=enforce_context self.context_width=context_width self.vocab=vocab self.
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