Transformer-based language models are a type of neural network framework based on attention-based mechanisms. They are designed to process text, generate useful outputs, and improve natural language understanding (NLU). These models are based on the idea of “transformers,” which are combinations of basic “blocks” (embedding layers, self-attention layers, and fully connected layers). They are considered by many to be the state of the art for natural language understanding.
Transformer-based models are unique because they do not rely on the traditional recurrent neural networks (RNNs) used in many previous NLU tasks. Instead, they convert natural language inputs into sequences of representations called embeddings. These embeddings are then passed to a self-attention layer where “attention” weights are assigned to each relationship between the tokens. This self-attention layer helps the model capture contextual relationships that are not necessarily obvious from the individual words.
The self-attention layer is followed by a fully connected layer, which is used to further refine the input and generate a more accurate output. Finally, the output layer produces a set of parameters which can be used to generate more predefined outputs like labels, categories, and classes.
Transformers are ideal for natural language understanding due to their ability to capture complex relationships that are nonlinear and contextual. This makes them better at predicting the meaning of words and sentences, even in contexts with multiple meanings. As a result, transformer-based models often perform better than traditional RNNs when it comes to NLU tasks such as sentiment analysis, question answering, machine translation, and more.
Overall, transformer models are an increasingly popular approach for natural language understanding due to their robust ability to capture complex, contextual relationships. By combining embedding layers, self-attention layers, and densely connected layers, they are able to learn the meaning of words and sentences quickly and accurately. As transformer-models become more powerful and efficient, they are likely to be used even more for NLU tasks.