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A lexical token is a string with an assigned and thus identified meaning, in contrast to the probabilistic token used in large language models. A lexical token consists of a token name and an optional token value. The token name is a category of a rule-based lexical unit. [2]
400 billion tokens [33] beta Fine-tuned for desirable behavior in conversations. [34] GLaM (Generalist Language Model) December 2021: Google: 1200 [35] 1.6 trillion tokens [35] 5600 [35] Proprietary Sparse mixture of experts model, making it more expensive to train but cheaper to run inference compared to GPT-3. Gopher: December 2021: DeepMind ...
During generation, all input tokens are masked, and the highest-confidence predictions are included for the next iteration, until all tokens are predicted. [111] Phenaki is a text-to-video model. It is a bidirectional masked transformer conditioned on pre-computed text tokens. The generated tokens are then decoded to a video. [110]
Like BERT, the text sequence is bracketed by two special tokens [SOS] and [EOS] ("start of sequence" and "end of sequence"). Take the activations of the highest layer of the transformer on the [EOS], apply LayerNorm, then a final linear map. This is the text encoding of the input sequence.
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Token type: The token type is a standard embedding layer, translating a one-hot vector into a dense vector based on its token type. Position: The position embeddings are based on a token's position in the sequence. BERT uses absolute position embeddings, where each position in sequence is mapped to a real-valued vector.
Over the years, he's dabbled in popular meme tokens like Dogecoin, Pepe, and the Trump and Melania coins. Like Laranja, he also distanced himself from the "typical" image of a meme trader ...