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text-classification's Introduction

Finetune Distilbert for Multiclass text classification

  • A brief introduction to the task and the dataset used

    • I used this dataset from kaggle
    • It has 4 categories World, Sports, Business ,Science/Technology
  • The preprocessing steps taken

    • Used DistilBertTokenizer to tokenize the sentences
  • The architecture of the model used, and how it was fine-tuned

    • I used distilbert-based-uncased model form huggingface and added two linear layers and one dropout layer for classification task
  • The evaluation metrics and the results obtained

    • I used cross entrophy loss to calculate the loss
    • After training 1 epoch with whole dataset and batch size of 16, the accuracy of model was 83.88% which is preety good for four classes and if i had trained for 5 epochs it could have reached 90% easily.
  • A discussion of the performance of the model and possible ways to improve it.

    • I didnt had enough time to train the model so i was only able to get 83.88% accuracy, it could be improved by training for atleast 5 hours.

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