Build a Text Classification Model using LONGFORMER in Python

  • Status: Pending
  • Præmier: $25
  • Modtagne indlæg: 3

Konkurrence Instruktioner

I am doing some research and want to see how longformer model works. How the attention_mask, global_attention_mask works. lets say I have a dataset with 50000 research papers. I want to classify lets say into 4 categories/labels. General, Management,Technical and Operational. It has to go through compelte article.
Note : I dont need any workaround. Dont need LSTM, BERT; Roberta or any other transformer.

# Please write Some explanation Documenatation may be few lines for the functions used as :
why you choose sliding window.. If the document is greater than 5000 words what can be done. why this performance matrix, why this obtimizer, Role of Global_attention as that makes it different from Bert

#The input parameters shouldnt just be one .
Lets say we have 4 features, Title of the article, date of the article, Author, URL of the article.

# It is Multi Class Text Classification NOT Multi Label

#It has to be LONGFORMER.

# End result should be a working model. You can take any test dataset and show the output. TRAIN/EVAL/TEST

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  • Longformer0076
    Konkurrenceafholder
    • 1 uge siden

    I can push little bit to 30

    • 1 uge siden
  • meghraj9
    meghraj9
    • 2 uger siden

    Skilled freelancers won't start working on this until the prize is increased significantly.

    • 2 uger siden
  • Longformer0076
    Konkurrenceafholder
    • 2 uger siden

    it was 15, i increased to 25

    • 2 uger siden
  • rawatpankaj9876
    rawatpankaj9876
    • 3 uger siden

    #increaseprize

    • 3 uger siden
    1. Longformer0076
      Konkurrenceafholder
      • 2 uger siden

      it was 15, i increased to 25

      • 2 uger siden
  • meghraj9
    meghraj9
    • 3 uger siden

    #increaseprize

    • 3 uger siden
    1. Longformer0076
      Konkurrenceafholder
      • 3 uger siden

      I can think about it. But have u worked on it. Can you show me some code related to it ?

      • 3 uger siden

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