Question: What neural network modules are included in NeuronBlocks?

Evidence:
raw_evidence: We recognize the following major functional categories of neural network components. Each category covers as many commonly used modules as possible. The Block Zoo is an open framework, and more modules can be added in the future.
Embedding Layer: Word/character embedding and extra handcrafted feature embedding such as pos-tagging are supported.
Neural Network Layers: Block zoo provides common layers like RNN, CNN, QRNN BIBREF2 , Transformer BIBREF3 , Highway network, Encoder Decoder architecture, etc. Furthermore, attention mechanisms are widely used in neural networks. Thus we also support multiple attention layers, such as Linear/Bi-linear Attention, Full Attention BIBREF4 , Bidirectional attention flow BIBREF5 , etc. Meanwhile, regularization layers such as Dropout, Layer Norm, Batch Norm, etc are also supported for improving generalization ability.
Loss Function: Besides of the loss functions built in PyTorch, we offer more options such as Focal Loss BIBREF6 .
Metrics: For classification task, AUC, Accuracy, Precision/Recall, F1 metrics are supported. For sequence labeling task, F1/Accuracy are supported. For knowledge distillation task, MSE/RMSE are supported. For MRC task, ExactMatch/F1 are supported.
raw_evidence: The Neuronblocks is built on PyTorch. The overall framework is illustrated in Figure FIGREF16 . It consists of two layers: the Block Zoo and the Model Zoo. In Block Zoo, the most commonly used components of deep neural networks are categorized into several groups according to their functions. Within each category, several alternative components are encapsulated into standard and reusable blocks with a consistent interface. These blocks serve as basic and exchangeable units to construct complex network architectures for different NLP tasks. In Model Zoo, the most popular NLP tasks are identified. For each task, several end-to-end network templates are provided in the form of JSON configuration files. Users can simply browse these configurations and choose one or more to instantiate. The whole task can be completed without any coding efforts.
We recognize the following major functional categories of neural network components. Each category covers as many commonly used modules as possible. The Block Zoo is an open framework, and more modules can be added in the future.
Embedding Layer: Word/character embedding and extra handcrafted feature embedding such as pos-tagging are supported.
Neural Network Layers: Block zoo provides common layers like RNN, CNN, QRNN BIBREF2 , Transformer BIBREF3 , Highway network, Encoder Decoder architecture, etc. Furthermore, attention mechanisms are widely used in neural networks. Thus we also support multiple attention layers, such as Linear/Bi-linear Attention, Full Attention BIBREF4 , Bidirectional attention flow BIBREF5 , etc. Meanwhile, regularization layers such as Dropout, Layer Norm, Batch Norm, etc are also supported for improving generalization ability.
Loss Function: Besides of the loss functions built in PyTorch, we offer more options such as Focal Loss BIBREF6 .
Metrics: For classification task, AUC, Accuracy, Precision/Recall, F1 metrics are supported. For sequence labeling task, F1/Accuracy are supported. For knowledge distillation task, MSE/RMSE are supported. For MRC task, ExactMatch/F1 are supported.

Answers:
Embedding Layer, Neural Network Layers, Loss Function, Metrics
Embedding Layer, Neural Network Layers, Loss Function, Metrics