Definition: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: 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.
Question: What neural network modules are included in NeuronBlocks?
Output:
Embedding Layer Neural Network Layers Loss Function Metrics