- 1 How does Transformer handle variable length input?
- 2 What is sequence length in Transformers?
- 3 How does the Transformer architecture capture sequence information?
- 4 What is an example of a transformer?
- 5 What is masking in transformer?
- 6 How does the attention from the transformer work?
- 7 How to calculate input and output length in NLP?
- 8 How to deal with problem of varying output length?
How does Transformer handle variable length input?
A transformer model handles variable-sized input using stacks of self-attention layers instead of RNNs or CNNs. Distant items can affect each other’s output without passing through many RNN-steps, or convolution layers (see Scene Memory Transformer for example). It can learn long-range dependencies.
What is sequence length in Transformers?
Here, d (or d_model) is the representation dimension or embedding dimension of a word (usually in the range 128–512), n is the sequence length (usually in the range 40–70), k is the kernel size of the convolution and r is the attention window-size for restricted self-attention.
How does the Transformer architecture capture sequence information?
While encoder-decoder architecture has been relying on recurrent neural networks (RNNs) to extract sequential information, the Transformer doesn’t use RNN. RNNs work like a feed-forward neural network that unrolls the input over its sequence, one after another.
What is an example of a transformer?
The definition of a transformer is a person or thing that changes, or a device with two or more coils of wire that transfer alternating current energy from one coil to another at the same frequency but with changed voltage. An example of a transformer is a fictional creature that changes from a person into a dog.
What is masking in transformer?
Samuel Kierszbaum. Jan 27, 2020 · 4 min read. Masking is needed to prevent the attention mechanism of a transformer from “cheating” in the decoder when training (on a translating task for instance).
How does the attention from the transformer work?
In the code above, result should have value [1, 2]. The attention from the transformer works in a similar way, but instead of having hard matches, it has soft maches: it gives you a combination of the values weighting them according to how similar their associated key is to the query.
How to calculate input and output length in NLP?
From what I understand, when we are passing the output from the encoder to the decoder (say 3 × 10 in this case), we do so via a Multi-Head Attention layer, which takes in 3 inputs: A Value (from decoder), of dimension L 0 × k 1, where L 0 refers to the number of words in the (masked) output sentence.
How to deal with problem of varying output length?
Given that the Multi-Head Attention should take in 3 matrices which have the same number of rows (or at least this is what I have understood from its architecture), how do we deal with the problem of varying output lengths?