Confusion matrix for DistilBERT model.

<div><p>This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural language processing (NLP) applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task—classi...

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Bibliographic Details
Main Author:	Ahmed Akib Jawad Karim (20427740) (author)
Other Authors:	Kazi Hafiz Md. Asad (20685203) (author), Md. Golam Rabiul Alam (14285073) (author)
Published:	2025
Subjects:	Genetics Biotechnology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified social networking platforms natural language processing mental health professionals mental health diagnosis comprising paraphrase identification comprehend material produced classifying severity levels streamlined severity classification 66 million parameters world adhd dataset hence advancing nlp advanced nlp methods adhd severity properly model approximately 73 knowledge distillation approach distilbert slightly outperforming clinicalbert achieving 86 85 %, precision knowledge distillation 85 %, text classification 29 million 87 %, 85 %. xlink "> work focuses useful tool study shows study emphasizes sentiment analysis model creation limited conditions kaggle notebooks furthermore underlined findings highlight considerable decrease classify degrees based model also used
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Confusion matrix for DistilBERT model.

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