What are the sequence labeling modules in Torch?

In Torch, there are several types of sequence labeling modules.

1. nn.CTCLoss is a module used for calculating the Connectionist Temporal Classification loss, typically used in sequence labeling tasks.
2. nn.Transformer: The Transformer model can be used for sequence labeling tasks, capturing long-range dependencies in sequences through self-attention mechanisms.
3. nn.LSTM: Long Short-Term Memory (LSTM) is a type of recurrent neural network structure commonly used in sequence labeling tasks, able to effectively handle long-term dependencies.
4. nn.GRU is another type of recurrent neural network structure known as Gated Recurrent Unit (GRU), which is similar to LSTM and can also be used for sequence labeling tasks.
5. CRF: Conditional Random Field (CRF) is a classic sequence labeling model that is often used in conjunction with neural networks to learn global labeling distributions.

These modules can be flexibly combined to choose the appropriate model structure based on specific task requirements.