Questions tagged [masking]
7 questions
2
votes
1 answer
Decoder Transformer feedforward
I have a question about the decoder transformer feed forward during training.
Let's pick an example: input data "i love the sun" traduction i want to predict (italian traduction) "io amo il sole".
Now i feed the encoder with the input "i love the…
erre4
- 95
- 7
2
votes
1 answer
Why shouldn't we mask [CLS] and [SEP] in preparing inputs for a MLM?
I know that MLM is trained for predicting the index of MASK token in the vocabulary list, and I also know that [CLS] stands for the beginning of the sentence and [SEP] telling the model the end of the sentence or another sentence will come soon, but…
Jie
- 21
- 1
1
vote
1 answer
Dealing with high frequency tokens during masked Language modelling?
Suppose I am working with a Masked Language Model to pre-train on a specific dataset. In that dataset, most sequences have a particular token of a high frequency
Sample Sequence:-
, , , , , ---> here tok4 is very…
neel g
- 207
- 4
- 11
1
vote
1 answer
Anonymize continuous variable for masking purposes
I am about to kick off a large hackathon event.
We have a dataset that is comprised of one continuous variable with high precision, and a number of categorical variables qualifying these data 3-levels deep.
Data provider wants to 'mask' the data…
HEITZ
- 911
- 4
- 7
0
votes
1 answer
There could be a problem with the linear layer after the attention inside a transformer?
My question regards this image:
It seems that after the multi head attention there is a linear layer as they mention also from here:
the linearity is given by the weights W^{o}. my quesion is: for the decoder, doesn't this linear layer mess up…
erre4
- 95
- 7
0
votes
0 answers
Keras masking with MultiHeadAttention
I am following keras example to classify time series using transformers. Timeseries classification with a Transformer model
The creation of the model is presented in the following code snippet:
def transformer_encoder(inputs):
# Normalization and…
0
votes
0 answers
Embedding: Can i use it in a time series problem?
I'm trying to do feature extraction in some discretized time series with a variable length, doing that i'm creating an RNN auto encoder. My main problem is to find a way to let the model train with variable length time sequences. I read in TF guides…
Nathaldien
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