CNNs vs. Transformers: Which is Best for Text Classification in Resource-Constrained Environments?

In the current AI landscape, transformer models such as BERT, RoBERTa, and GPT have become the go-to choice for natural language processing (NLP) tasks. These models excel in a variety of applications, including chatbots, document summarization, and more. However, with their rise to prominence, a pertinent question arises: have Convolutional Neural Networks (CNNs), once popular for text classification, become obsolete? This article delves into the comparison between CNNs and transformers to determine their relevance in 2025, particularly in the context of text classification using the AG News dataset. The AG News Dataset The AG News dataset, available on Kaggle, consists of over 120,000 news articles categorized into four major classes: World, Sports, Business, and Sci/Tech. Each entry includes a title and description. This dataset is well-balanced, clean, and covers a diverse range of topics, making it ideal for evaluating different text classification models. Approach 1: CNN Trained from Scratch Step-by-Step Implementation Import Libraries: Essential packages for data handling, preprocessing, and modeling include pandas, numpy, matplotlib, re, nltk, scikit-learn, and TensorFlow/Keras. Load Dataset: The dataset is loaded from a CSV file, with columns renamed for clarity. Map Labels: Numerical class indices are mapped to readable category names (optional but helpful). Clean the Text: Preprocessing involves converting text to lowercase, removing special characters, numbers, and stopwords. The title and description are concatenated into a single text field. Tokenize & Pad: Text is tokenized to sequences of integers, and these sequences are padded to a fixed length. Train-Test Split: The dataset is divided into training and validation sets, ensuring no missing values and consistent indices. Build the CNN Model: A simple yet effective CNN model is constructed using an embedding layer, a convolutional layer, global max pooling, a dense layer, dropout, and a final output layer for classification. Compile and Train: The model is compiled with sparse categorical cross-entropy loss and the Adam optimizer. Early stopping is used to prevent overfitting, and the model is trained for several epochs. Visualize Accuracy: Training and validation accuracy are plotted to monitor the model's performance. Evaluate Model Performance: The final validation accuracy is checked and printed. Approach 2: DistilBERT Fine-Tuning with HuggingFace Step-by-Step Implementation Install Required Libraries: The transformers library, along with datasets and TensorFlow, are installed. Load and Prepare the Dataset: Similar to the CNN approach, the dataset is loaded and the text fields are concatenated. The dataset is then split into training and validation sets. Tokenization with DistilBERT Tokenizer: The DistilBERT tokenizer is used to convert text into token encodings, ensuring truncation and padding for consistency. Prepare TensorFlow Datasets: The encodings and labels are converted into TensorFlow datasets, which are shuffled and batched. Load and Fine-Tune DistilBERT: A pre-trained DistilBERT model is loaded and fine-tuned with the AdamW optimizer and sparse categorical cross-entropy loss. Evaluate Model Performance: The final validation accuracy is checked and printed, showing that DistilBERT achieved a high accuracy of 0.9452. Comparative Analysis: CNN vs. DistilBERT Performance Metrics Accuracy: DistilBERT outperforms the CNN model with a higher validation accuracy (0.9452 vs. 0.85-0.90). Computational Efficiency: CNNs are faster to train and require fewer computational resources. They are more suitable for resource-constrained environments. Complexity: CNNs have simpler architectures, making them easier to understand and implement. DistilBERT, while powerful, is more complex and requires more expertise to fine-tune effectively. Conclusion Transformers like DistilBERT are the clear leaders in terms of accuracy and contextual understanding, which are crucial for many NLP tasks. However, CNNs remain robust and practical options for scenarios where speed, simplicity, and limited computational resources are key considerations. For lightweight applications, educational models, or quick prototyping, CNNs can still deliver impressive results. The choice between CNNs and transformers ultimately depends on the specific requirements of the project rather than just following the latest hype in AI. Industry Insider Evaluation and Company Profiles Industry experts generally agree that while transformers are superior for complex NLP tasks, CNNs are far from being redundant. Companies like Hugging Face, a leading provider of open-source NLP tools, continue to innovate in both areas, ensuring that developers have a range of options to choose from. Hugging Face has also released smaller, more efficient transformer models to address the computational demands of larger ones, which aligns with the ongoing need for balance between performance and resource usage in practical applications. Final Thoughts CNNs are not dead for text classification. Despite the dominance of transformers in benchmark tests, CNNs offer faster, cheaper, and easier solutions, especially when data and resources are limited. Whether you're building a lightweight app or need a quick and efficient model, CNNs are still a viable and valuable option. The key is to select the appropriate model based on your specific needs, not just the latest trends.