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I'll answer in a couple of stages.
I feel somewhat lost as to what the input for the NN should look like.
Your choices boil down to two options, each with their own multitude of variants:
Vector Representation: Your input is a vector of the same size as your vocabulary where the elements represent the tokens in the input example. The most basic version of ...
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You could sequentially pass in each element of your sequential data and save the hidden and cell states in a separate buffer. In a typical LSTM implementation, you input the entire sequence and the hidden and cell states are propagated internally. In the end, the final hidden and cell states returned as the output. This works if your input is all the same ...
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Actually, there is usually an upper bound for inputs of transformers, due to the inability of handling long-sequence. Usually, the value is set as 512 or 1024 at current stage.
However, if you are asking handling the various input size, adding padding token such as [PAD] in BERT model is a common solution. The position of [PAD] token could be masked in self-...
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You may want to take a look at this article, but I'll summarize. You can use BERT (or some other tool) to make embeddings of every word in every sentence. Then for each word, make a contextualized embedding vector using the rest of the sentence. bert-embedding does all of this itself. Then keep the embedding vector for the important words.
For each important ...
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When it talks to other domains such as image or music, using transformer will always face a problem of sequence length limitation. To the best of my knowledge, the bottleneck of self-attention which uses a $n^2$ matrix quite limits transformer being applied to other domains. For example, a 32x32 pixel image, means a sequence of 1024 tokens.
OpenAI did some ...
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