Body: <p>Embedding is necessary to convert text in vectors that are more tractable by machine learning models.</p>
  <p>Can embedding be carried out (efficiently) using the MapReduce approach?</p>
  <p>I suspect so: projecting text in another space shouldn't be much harder than relational algebra operations such as joins, which can be done efficiently using MapReduce.</p>
  <p>Is there any source on this?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-06-05T14:02:44.710Z
Comments.Text: it's literally just a map, but you need to share the dictionary across workers, which might be expensive
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-06-05T18:51:30.183Z
Comments.Text: @Alberto I'm aware that MR is essentially Turing-complete, so anything can be done using MR. My question is whether or not a distributed MR-style embedding is advantageous.
Comments.UserDisplayName: A. Darwin
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-06-05T21:15:41.840Z
Comments.Text: I see, you are probably looking for this paper https://arxiv.org/pdf/1606.08495
Comments.UserDisplayName: Alberto
Tags: <embeddings>
Title: Can embedding be tackled efficiently using MapReduce?
UserDisplayName: A. Darwin
ThreadText: # Can embedding be tackled efficiently using MapReduce?
  
  (Asked by: username_0 on 2024-06-04)
  
  Embedding is necessary to convert text in vectors that are more tractable by machine learning models.
  
  Can embedding be carried out (efficiently) using the MapReduce approach?
  
  I suspect so: projecting text in another space shouldn't be much harder than relational algebra operations such as joins, which can be done efficiently using MapReduce.
  
  Is there any source on this?
  
  **Tags:** embeddings
  > **Comments:**
  > **username_1**: it's literally just a map, but you need to share the dictionary across workers, which might be expensive  
  > **username_0**: @Alberto I'm aware that MR is essentially Turing-complete, so anything can be done using MR. My question is whether or not a distributed MR-style embedding is advantageous.  
  > **username_1**: I see, you are probably looking for this paper https://arxiv.org/pdf/1606.08495  
  
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  ## 0 answers
  
  