Classic NLP Models in TensorFlow

Classic structures of natural language processing models in TensorFlow include:

1. Recurrent Neural Networks (RNNs) are commonly used for processing sequence data, such as text data. By using RNNs, tasks like language modeling, text generation, and machine translation can be achieved.
2. Long Short-Term Memory (LSTM): A special type of recurrent neural network structure that is able to better handle long sequence data and effectively address the issues of vanishing and exploding gradients.
3. Gated Recurrent Unit (GRU): similar to LSTM, it is also a type of neural network structure used for handling sequential data, but compared to LSTM, it has fewer parameters and trains faster.
4. A model structure based on self-attention mechanism, the Transformer is suitable for handling long-range dependencies in sequential data, commonly used for tasks like machine translation and text generation.
5. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained model based on the Transformer architecture, which enhances the performance of natural language processing tasks by learning bidirectional context representations.
6. GPT (Generative Pre-trained Transformer): a type of pre-trained language model based on the Transformer architecture, which enhances the performance of natural language processing tasks through unsupervised pre-training.

Apart from the classic structures mentioned above, TensorFlow also offers many other models and techniques for natural language processing, such as Word2Vec and the Attention Mechanism. These models and techniques have a wide range of applications and can help developers solve various natural language processing tasks.