Parameter-efficient transfer learning for NLP involves using adapter modules to achieve high performance with minimal additional parameters. This approach allows for efficient model adaptation to new tasks without retraining the entire model. Adapter modules add only a few trainable parameters per task, enabling the model to handle multiple tasks with a high degree of parameter sharing. This method is particularly effective for tasks like text classification, where it achieves near-state-of-the-art performance with significantly fewer parameters than full fine-tuning. The paper introduces a novel adapter-based tuning strategy for large text models, such as BERT. This method involves inserting small, trainable modules into the pre-trained network, which are trained on the downstream task. These modules modify the network's behavior without altering the original parameters, allowing for efficient and incremental learning. The adapter architecture includes a bottleneck design that limits the number of parameters while maintaining performance. Experiments on the GLUE benchmark and additional text classification tasks show that adapter-based tuning achieves performance comparable to full fine-tuning but with a fraction of the parameters. For example, on GLUE, adapter tuning achieves a mean score of 80.0, compared to 80.4 for full fine-tuning, using only 1.3 times the parameters. On other tasks, adapters also perform well, with results close to full fine-tuning and often better than alternative methods like variable fine-tuning. The adapter method is also effective for tasks beyond text classification, such as SQuAD extractive question answering. Adapters achieve high performance with minimal parameters, demonstrating their versatility. Analysis shows that adapters have a small impact on individual layers but contribute significantly to overall performance. They are also robust to variations in initialization and number of neurons, making them a reliable and efficient solution for transfer learning in NLP. This approach offers a compact, extensible model that can adapt to new tasks incrementally without forgetting previous ones. It is particularly useful in cloud services and other applications where models need to handle a series of tasks efficiently. The adapter-based method provides a balance between performance and parameter efficiency, making it a valuable tool for NLP transfer learning.Parameter-efficient transfer learning for NLP involves using adapter modules to achieve high performance with minimal additional parameters. This approach allows for efficient model adaptation to new tasks without retraining the entire model. Adapter modules add only a few trainable parameters per task, enabling the model to handle multiple tasks with a high degree of parameter sharing. This method is particularly effective for tasks like text classification, where it achieves near-state-of-the-art performance with significantly fewer parameters than full fine-tuning. The paper introduces a novel adapter-based tuning strategy for large text models, such as BERT. This method involves inserting small, trainable modules into the pre-trained network, which are trained on the downstream task. These modules modify the network's behavior without altering the original parameters, allowing for efficient and incremental learning. The adapter architecture includes a bottleneck design that limits the number of parameters while maintaining performance. Experiments on the GLUE benchmark and additional text classification tasks show that adapter-based tuning achieves performance comparable to full fine-tuning but with a fraction of the parameters. For example, on GLUE, adapter tuning achieves a mean score of 80.0, compared to 80.4 for full fine-tuning, using only 1.3 times the parameters. On other tasks, adapters also perform well, with results close to full fine-tuning and often better than alternative methods like variable fine-tuning. The adapter method is also effective for tasks beyond text classification, such as SQuAD extractive question answering. Adapters achieve high performance with minimal parameters, demonstrating their versatility. Analysis shows that adapters have a small impact on individual layers but contribute significantly to overall performance. They are also robust to variations in initialization and number of neurons, making them a reliable and efficient solution for transfer learning in NLP. This approach offers a compact, extensible model that can adapt to new tasks incrementally without forgetting previous ones. It is particularly useful in cloud services and other applications where models need to handle a series of tasks efficiently. The adapter-based method provides a balance between performance and parameter efficiency, making it a valuable tool for NLP transfer learning.