Answers.Body: <p>To transform your logits <code>[-5,1,2]</code> into probabilities you can use the <a href="https://en.wikipedia.org/wiki/Softmax_function" rel="nofollow noreferrer">softmax function</a>.  This would get you <code>p = [0.000666195, 0.268762254, 0.730571551]</code>.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-10-27T23:40:33.140Z
Answers.LastActivityDate: 2022-10-27T23:40:33.140Z
Answers.UserDisplayName: Snehal Patel
Body: <p>I am trying to figure out the best way to calculate the probability a sentence belongs to some category.
  For simplicity sake, lets assume that the sentence is &quot;yellow fruit&quot;.
  Next, I use the an BERT classifier to get a classification result [-5,1,2] with the categories [apple, orange, banana].</p>
  <p>The array simply tells us how strong is the signal that corresponds to a category. In this case -5 for apple, 1 for orange, and 2 for banana.
  The answer to this classification is &quot;banana&quot; since 2 is the max number in the [-5,1,2] array, and it position corresponds to the category &quot;banana&quot;.
  Therefore, we know that &quot;yellow fruit&quot; is most-likely a banana.</p>
  <p>Next, I have a table of likelihood of purchase for apples, oranges and bananas:</p>
  <p>Apple: 80%</p>
  <p>Orange: 10%</p>
  <p>Banana: 50%</p>
  <p>I need to calculate the likelihood of customer purchase for sentence &quot;yellow fruit&quot;.
  Therefore, I am given the sentence &quot;yellow fruit&quot;, the classification vector [-5,1,2] the classification categories [apple, orange, banana] and the likelihood table for purchase [0.8,0.1,0.5]</p>
  <p>How do I calculate the the likelihood of purchase for the sentence &quot;yellow fruit&quot; ?</p>
  
Tags: <natural-language-processing><classification><bert><probability>
Title: How to classify data into organised groups by using a resulting classification vector and a set of probabilities?
UserDisplayName: Damir Olejar
ThreadText: # How to classify data into organised groups by using a resulting classification vector and a set of probabilities?
  
  (Asked by: username_0 on 2022-10-27)
  
  I am trying to figure out the best way to calculate the probability a sentence belongs to some category.
  For simplicity sake, lets assume that the sentence is "yellow fruit".
  Next, I use the an BERT classifier to get a classification result [-5,1,2] with the categories [apple, orange, banana].
  
  The array simply tells us how strong is the signal that corresponds to a category. In this case -5 for apple, 1 for orange, and 2 for banana.
  The answer to this classification is "banana" since 2 is the max number in the [-5,1,2] array, and it position corresponds to the category "banana".
  Therefore, we know that "yellow fruit" is most-likely a banana.
  
  Next, I have a table of likelihood of purchase for apples, oranges and bananas:
  
  Apple: 80%
  
  Orange: 10%
  
  Banana: 50%
  
  I need to calculate the likelihood of customer purchase for sentence "yellow fruit".
  Therefore, I am given the sentence "yellow fruit", the classification vector [-5,1,2] the classification categories [apple, orange, banana] and the likelihood table for purchase [0.8,0.1,0.5]
  
  How do I calculate the the likelihood of purchase for the sentence "yellow fruit" ?
  
  **Tags:** natural-language-processing, classification, bert, probability
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-10-27. Score: 2 | Accepted answer
  
  To transform your logits `[-5,1,2]` into probabilities you can use the [softmax function](https://en.wikipedia.org/wiki/Softmax_function). This would get you `p = [0.000666195, 0.268762254, 0.730571551]`.
  
  
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