Transformer-based models are a type of deep learning model that is based on the transformer architecture. It is used in various fields such as natural language processing, image recognition, and speech recognition. Transformer-based models have achieved great success in recent years due to their ability to capture long-range dependencies in data.

In TensorFlow 2.x, Transformer-based models can be implemented using the Keras library. Keras provides several pre-trained models for different tasks, such as BERT, RoBERTa, and ALBERT. These models are trained on large datasets and can be fine-tuned for specific tasks.

To implement a Transformer-based model using Keras, you need to define a custom layer that implements the transformer architecture. This layer should take input sequences and output the corresponding output sequence. The output sequence is then passed through a fully connected layer to obtain the final prediction.

Overall, Transformer-based models in TensorFlow 2.x offer powerful capabilities for solving complex problems in various domains.