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Custom Layers and Utilities
This page lists all the custom layers used by the library, as well as the utility functions and classes it provides for modeling.
Most of those are only useful if you are studying the code of the models in the library.
Attention Functions
autodoc AttentionInterface - register
Pytorch custom modules
autodoc pytorch_utils.Conv1D
autodoc modeling_utils.PoolerStartLogits - forward
autodoc modeling_utils.PoolerEndLogits - forward
autodoc modeling_utils.PoolerAnswerClass - forward
autodoc modeling_utils.SquadHeadOutput
autodoc modeling_utils.SQuADHead - forward
autodoc modeling_utils.SequenceSummary - forward
PyTorch Helper Functions
autodoc pytorch_utils.apply_chunking_to_forward
autodoc pytorch_utils.find_pruneable_heads_and_indices
autodoc pytorch_utils.prune_layer
autodoc pytorch_utils.prune_conv1d_layer
autodoc pytorch_utils.prune_linear_layer
TensorFlow custom layers
autodoc modeling_tf_utils.TFConv1D
autodoc modeling_tf_utils.TFSequenceSummary
TensorFlow loss functions
autodoc modeling_tf_utils.TFCausalLanguageModelingLoss
autodoc modeling_tf_utils.TFMaskedLanguageModelingLoss
autodoc modeling_tf_utils.TFMultipleChoiceLoss
autodoc modeling_tf_utils.TFQuestionAnsweringLoss
autodoc modeling_tf_utils.TFSequenceClassificationLoss
autodoc modeling_tf_utils.TFTokenClassificationLoss
TensorFlow Helper Functions
autodoc modeling_tf_utils.get_initializer
autodoc modeling_tf_utils.keras_serializable
autodoc modeling_tf_utils.shape_list