Body: <p>I'm building an AI model using Google's 'Civil-Comments' dataset. It has 7 different labels, each a float than can be anywhere from 0 to 1. Embedding Bags, which I have read about. do not perform well. I've been looking into transformers and recurrent systems (LSTM, GRU, RNN, etc.). What should I do?</p>
  <p>Using PyTorch, if it matters. But I'm not looking for code.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-09-02T08:44:14.983Z
Comments.Text: Just to clarify, the task here is to classify a comment into 7 floats between 0 and 1? What is the meaning of 0 and 1? Why do you have 7 labels?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-09-08T20:34:12.217Z
Comments.Text: @nbro I have seven labels, one for each type of language. The [Civil-Comments dataset](https://huggingface.co/datasets/google/civil_comments) is where I'm getting my data from. 0 means that type of language (eg. threats) are detected, whereas 1 means it is there and it is severe. The closer to1, the more prevalent the language used.
Comments.UserDisplayName: ShadowProgrammer
Tags: <pytorch><regression>
Title: What is the best architecture for multi-target text regression?
UserDisplayName: ShadowProgrammer
ThreadText: # What is the best architecture for multi-target text regression?
  
  (Asked by: username_0 on 2024-08-28)
  
  I'm building an AI model using Google's 'Civil-Comments' dataset. It has 7 different labels, each a float than can be anywhere from 0 to 1. Embedding Bags, which I have read about. do not perform well. I've been looking into transformers and recurrent systems (LSTM, GRU, RNN, etc.). What should I do?
  
  Using PyTorch, if it matters. But I'm not looking for code.
  
  **Tags:** pytorch, regression
  > **Comments:**
  > **username_1**: Just to clarify, the task here is to classify a comment into 7 floats between 0 and 1? What is the meaning of 0 and 1? Why do you have 7 labels?  
  > **username_0**: @nbro I have seven labels, one for each type of language. The [Civil-Comments dataset](https://huggingface.co/datasets/google/civil_comments) is where I'm getting my data from. 0 means that type of language (eg. threats) are detected, whereas 1 means it is there and it is severe. The closer to1, the more prevalent the language used.  
  
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  ## 0 answers
  
  