Custom Layers and Utilities
This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
Most of those are only useful if you are studying the code of the models in the library.
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