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Layers are the fundamental building blocks for NLP models.
They can be used to assemble new tf.keras layers or models.
Classes
class BertPackInputs: Packs tokens into model inputs for BERT.
class BertTokenizer: Wraps TF.Text's BertTokenizer with pre-defined vocab as a Keras Layer.
class BigBirdAttention: BigBird, a sparse attention mechanism.
class BigBirdMasks: Creates bigbird attention masks.
class BlockDiagFeedforward: Block diagonal feedforward layer.
class CachedAttention: Attention layer with cache used for autoregressive decoding.
class ClassificationHead: Pooling head for sentence-level classification tasks.
class FactorizedEmbedding: A factorized embeddings layer for supporting larger embeddings.
class FastWordpieceBertTokenizer: A bert tokenizer keras layer using text.FastWordpieceTokenizer.
class FourierTransformLayer: Fourier Transform layer.
class GatedFeedforward: Gated linear feedforward layer.
class GaussianProcessClassificationHead: Gaussian process-based pooling head for sentence classification.
class HartleyTransformLayer: Hartley Transform layer.
class KernelAttention: A variant of efficient transformers which replaces softmax with kernels.
class KernelMask: Creates kernel attention mask.
class LinearTransformLayer: Dense, linear transformation layer.
class MaskedLM: Masked language model network head for BERT modeling.
class MaskedSoftmax: Performs a softmax with optional masking on a tensor.
class MatMulWithMargin: This layer computs a dot product matrix given two encoded inputs.
class MixingMechanism: Determines the type of mixing layer.
class MobileBertEmbedding: Performs an embedding lookup for MobileBERT.
class MobileBertMaskedLM: Masked language model network head for BERT modeling.
class MobileBertTransformer: Transformer block for MobileBERT.
class MultiChannelAttention: Multi-channel Attention layer.
class MultiClsHeads: Pooling heads sharing the same pooling stem.
class MultiHeadRelativeAttention: A multi-head attention layer with relative attention + position encoding.
class OnDeviceEmbedding: Performs an embedding lookup suitable for accelerator devices.
class PackBertEmbeddings: Performs packing tricks for BERT inputs to improve TPU utilization.
class PerDimScaleAttention: Learn scales for individual dims.
class PerQueryDenseHead: Pooling head used for EncT5 style models.
class PositionEmbedding: Creates a positional embedding.
class RandomFeatureGaussianProcess: Gaussian process layer with random feature approximation [1].
class ReZeroTransformer: Transformer layer with ReZero.
class RelativePositionBias: Relative position embedding via per-head bias in T5 style.
class RelativePositionEmbedding: Creates a positional embedding.
class ReuseMultiHeadAttention: MultiHeadAttention layer.
class ReuseTransformer: Transformer layer.
class SelectTopK: Select top-k + random-k tokens according to importance.
class SelfAttentionMask: Create 3D attention mask from a 2D tensor mask.
class SentencepieceTokenizer: Wraps tf_text.SentencepieceTokenizer as a Keras Layer.
class SpectralNormalization: Implements spectral normalization for Dense layer.
class SpectralNormalizationConv2D: Implements spectral normalization for Conv2D layer based on [3].
class StridedTransformerEncoderBlock: Transformer layer for packing optimization to stride over inputs.
class StridedTransformerScaffold: TransformerScaffold for packing optimization to stride over inputs.
class TNTransformerExpandCondense: Transformer layer using tensor network Expand-Condense layer.
class TalkingHeadsAttention: Implements Talking-Heads Attention.
class TokenImportanceWithMovingAvg: Routing based on per-token importance value.
class Transformer: Transformer layer.
class TransformerDecoderBlock: Single transformer layer for decoder.
class TransformerEncoderBlock: TransformerEncoderBlock layer.
class TransformerScaffold: Transformer scaffold layer.
class TransformerXL: Transformer XL.
class TransformerXLBlock: Transformer XL block.
class TwoStreamRelativeAttention: Two-stream relative self-attention for XLNet.
class VotingAttention: Voting Attention layer.
Functions
extract_gp_layer_kwargs(...): Extracts Gaussian process layer configs from a given kwarg.
extract_spec_norm_kwargs(...): Extracts spectral normalization configs from a given kwarg.