Fine-Tuning of Distil-BERT for Continual Learning in Text Classification: An Experimental Analysis

Continual learning (CL) with bidirectional encoder representation from transformer (BERT) and its variant Distil-BERT, have shown remarkable performance in various natural language processing (NLP) tasks, such as text classification (TC). However, the model degrading factors like catastrophic forgetting (CF), accuracy, task dependent architecture ruined its popularity for complex and intelligent tasks. This research article proposes an innovative approach to address the challenges of CL in TC tasks. The objectives are to enable the model to learn continuously without forgetting previously acquired knowledge and perfectly avoid CF. To achieve this, a task-independent model architecture is introduced, allowing training of multiple tasks on the same model, thereby improving overall performance in CL scenarios. The framework incorporates two auxiliary tasks, namely next sentence prediction and task identifier prediction, to capture both the task-generic and task-specific contextual information. The Distil-BERT model, enhanced with two linear layers, categorizes the output representation into a task-generic space and a task-specific space. The proposed methodology is evaluated on diverse sets of TC tasks, including Yahoo, Yelp, Amazon, DB-Pedia, and AG-News. The experimental results demonstrate impressive performance across multiple tasks in terms of F1 score, model accuracy, model evaluation loss, learning rate, and training loss of the model. For the Yahoo task, the proposed model achieved an F1 score of 96.84 %, accuracy of 95.85 %, evaluation loss of 0.06, learning rate of 0.00003144. In the Yelp task, our model achieved an F1 score of 96.66 %, accuracy of 97.66 %, evaluation loss of 0.06, and similarly minimized training losses by achieving the learning rate of 0.00003189. For the Amazon task, the F1 score was 95.82 %, the observed accuracy is 97.83 %, evaluation loss was 0.06, and training losses were effectively minimized by securing the learning rate of 0.00003144. In the DB-Pedia task, we achieved an F1 score of 96.20 %, accuracy of 95.21 %, evaluation loss of 0.08, with learning rate 0.0001972 and rapidly minimized training losses due to the limited number of epochs and instances. In the AG-News task, our model obtained an F1 score of 94.78 %, accuracy of 92.76 %, evaluation loss of 0.06, and fixed the learning rate to 0.0001511. These results highlight the exceptional performance of our model in various TC tasks, with gradual reduction in training losses over time, indicating effective learning and retention of knowledge.