Development of Scalable Deep Learning Frameworks Utilizing Advanced Hybrid Models like Transformer-CNN for Distributed Computing Environments

Over the past few years especially in the context of communication and information processing the importance of Natural language processing which demands efficient deep learning models has rapidly increased with the complexity of language data and the performance requirements in the distributed computing environment. Popular classical machine learning models fail to capture the contextual relations between words in language data deteriorating their performance in complex tasks. Previous approaches have improved significantly when the use of transformers was adopted in the feature extraction of context but still proposes a problem when it comes to feature extraction at scale in a distributed environment. Therefore, in this research, a new model utilizing both the deep contextual understanding of BERT and the hierarchical feature extraction capabilities of CNN is presented to overcome these difficulties. This combined approach is proposed to improve the model’s efficiency in the classification of text data with focus on scalability when applied in distributed systems. To address the issue, the suggested model was put into practice and examined using Python; the computed outcomes revealed a 99.1% accuracy level. The combination of BERT’s contextual embeddings are fused with CNN’s feature extraction not only increases the efficiency in the predictive accuracy but also the generalization of this model for the various text classification applications. This paper demonstrates the possibility of developing new hybrid models that can help improve the current state of the natural language processing field and suggest areas for future research in the development and utilization of these models in practical applications.