如果一切顺利,最后一层 feed-forward 输出中的最后一个向量(the very last vector in the sequence), 就已经包含了句子的核心意义(essential meaning of the passage)。对这个向量进行 unembedding 操作(也是一次性矩阵运算), 得到的就是下一个单词的备选列表及其概率:图:原始输入为 "To date, the cleverest thi...
A year later, another Google team tried processing text sequences both forward and backward with a transformer. That helped capture more relationships among words, improving the model’s ability to understand the meaning of a sentence. Their Bidirectional Encoder Representations from Transformers (BERT)...
A year later, another Google team tried processing text sequences both forward and backward with a transformer. That helped capture more relationships among words, improving the model’s ability to understand the meaning of a sentence. Their Bidirectional Encoder Representations from Transformers (BERT)...
"Luftmensch," literally meaning "air person," is the Yiddish way of describing someone who is a bit of a dreamer. Did You Know? The word "infant" comes from the Latin word "infans" which literally means "unable to speak; speechless." ...
Understand what a transformer model is and its role in AI, revolutionizing natural language processing and machine learning tasks.
Self-Attention:With the help of the self-attention mechanism, the encoder assesses the input sequence, determining how each word relates to others in the context. It allows the model to understand the overall sentence structure and meaning better. ...
Biological sequences, such as deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or protein sequences, are relatively similar to natural languages. In the same way that characters in a natural language construct meaningful words, phrases, or sentences to convey some meaning, the building blocks...
We know “it” refers to the cup, while in the sentence: She poured water from the pitcher to the cup until it was empty. We know “it” refers to the pitcher. “Meaning is a result of relationships between things, and self-attention is a general way of learning relationships,” said...
There are two things that transformer architecture does very well. First, it does a really good job of learning how to apply context. For example, when processing a sentence, the meaning of a word or a phrase can completely change based on the context in which it is being used. Often ho...