关于Transformer, QKV的意义表示其更像是一个可学习的查询系统,或许以前搜索引擎的算法就与此有关或者某个分支的搜索算法与此类似。
Can anyone help me to understand this image? - #2 by J_Johnson - nlp - PyTorch Forums
Embeddings - these are learnable weights where each token(token could be a word, sentence piece, subword, character, etc) are converted into a vector, say, with 500 values between 0 and 1 that are trainable.
Positional Encoding - for each token, we want to inform the model where it’s located, orderwise. This is because linear layers are not ideal for handling sequential information. So we manually pass this in by adding a vector of sine and cosine values on the first 2 elements in the embedding vector.
This sequence of vectors goes through an attention layer, which basically is like a learnable digitized database search function with keys, queries and values. In this case, we are “searching” for the most likely next token.
The Feed Forward is just a basic linear layer, but is applied across each embedding in the sequence separately(i.e. 3 dim tensor instead of 2 dim).
Then the final Linear layer is where we want to get out our predicted next token in the form of a vector of probabilities, which we apply a softmax to put the values in the range of 0 to 1.
There are two sides because when that diagram was developed, it was being used in language translations. But generative language models for next token prediction just use the Transformer decoder and not the encoder.
Here is a PyTorch tutorial that might help you go through how it works.文章来源:https://www.toymoban.com/news/detail-637463.html
Language Modeling with nn.Transformer and torchtext — PyTorch Tutorials 2.0.1+cu117 documentation文章来源地址https://www.toymoban.com/news/detail-637463.html
到了这里,关于关于Transformer的理解的文章就介绍完了。如果您还想了解更多内容,请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章,希望大家以后多多支持TOY模板网!