Answers.Body: <p>Positional Encodings in Transformers exist to give the model some information about the position of the embedding. This makes sense in fields like NLP or Time Series Data, since the position(order) matters in this case.</p>
  <p>However, since you say that order of the data is not relevant in your use case, positional encoding would not be necessary.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-11-17T01:35:37.943Z
Answers.LastActivityDate: 2021-11-17T01:35:37.943Z
Answers.UserDisplayName: DKDK
Body: <p>I am trying to make a model that uses a <em>Transformer</em> to see the relationship between several data vectors, but the order of the data is not relevant in this case, so I am not using the <em>Positional Encoding</em>.</p>
  <p>Since the performance of models using Transformers is quite improved with the use of this part, do you think that if I remove that part I am breaking the potential of Transformers or is it correct to do so?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-11-15T15:55:46.823Z
Comments.Text: Maybe you could make a little example of the inputs and how they should be given to the transformer, to make it clear why the order of the data should not be relevant.
Comments.UserDisplayName: dexteritas
Tags: <deep-learning><natural-language-processing><transformer><positional-encoding>
Title: Is Positional Encoding always needed for using Transformer models correctly?
UserDisplayName: Angelo
ThreadText: # Is Positional Encoding always needed for using Transformer models correctly?
  
  (Asked by: username_0 on 2021-11-14)
  
  I am trying to make a model that uses a *Transformer* to see the relationship between several data vectors, but the order of the data is not relevant in this case, so I am not using the *Positional Encoding*.
  
  Since the performance of models using Transformers is quite improved with the use of this part, do you think that if I remove that part I am breaking the potential of Transformers or is it correct to do so?
  
  **Tags:** deep-learning, natural-language-processing, transformer, positional-encoding
  
  > **Comments:**
  > **username_1**: Maybe you could make a little example of the inputs and how they should be given to the transformer, to make it clear why the order of the data should not be relevant.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2021-11-17. Score: 3 | Accepted answer
  
  Positional Encodings in Transformers exist to give the model some information about the position of the embedding. This makes sense in fields like NLP or Time Series Data, since the position(order) matters in this case.
  
  However, since you say that order of the data is not relevant in your use case, positional encoding would not be necessary.
  
  
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