@@ -6,8 +6,6 @@ The [official models](official) are a collection of example models that use Tens
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@@ -6,8 +6,6 @@ The [official models](official) are a collection of example models that use Tens
The [research models](https://github.com/tensorflow/models/tree/master/research) are a large collection of models implemented in TensorFlow by researchers. They are not officially supported or available in release branches; it is up to the individual researchers to maintain the models and/or provide support on issues and pull requests.
The [research models](https://github.com/tensorflow/models/tree/master/research) are a large collection of models implemented in TensorFlow by researchers. They are not officially supported or available in release branches; it is up to the individual researchers to maintain the models and/or provide support on issues and pull requests.
The [samples folder](samples) contains code snippets and smaller models that demonstrate features of TensorFlow, including code presented in various blog posts.
The [tutorials folder](tutorials) is a collection of models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
The [tutorials folder](tutorials) is a collection of models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
This libary provides a set of Keras primitives (Layers, Networks, and Models)
that can be assembled into transformer-based models. They are
flexible, validated, interoperable, and both TF1 and TF2 compatible.
*[`layers`](layers) are the fundamental building blocks for NLP models.
They can be used to assemble new layers, networks, or models.
*[`networks`](networks) are combinations of layers (and possibly other networks). They are sub-units of models that would not be trained alone. They
encapsulate common network structures like a classification head
or a transformer encoder into an easily handled object with a
standardized configuration.
*[`models`](models) are combinations of layers and networks that would be trained. Pre-built canned models are provided as both convenience functions and canonical examples.
*[`losses`](losses) contains common loss computation used in NLP tasks.
Besides the pre-defined primitives, it also provides scaffold classes to allow
easy experimentation with noval achitectures, e.g., you don’t need to fork a whole Transformer object to try a different kind of attention primitive, for instance.
*[`TransformerScaffold`](layers/transformer_scaffold.py) implements the
Transformer from ["Attention Is All You Need"]
(https://arxiv.org/abs/1706.03762), with a customizable attention layer
option. Users can pass a class to `attention_cls` and associated config to
`attention_cfg`, in which case the scaffold will instantiate the class with
the config, or pass a class instance to `attention_cls`.
*[`EncoderScaffold`](networks/encoder_scaffold.py) implements the transformer
encoder from ["BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding"](https://arxiv.org/abs/1810.04805), with customizable
embedding subnetwork (which will replace the standard embedding logic) and/or a
custom hidden layer (which will replace the Transformer instantiation in the
encoder).
BERT and ALBERT models in this repo are implemented using this library. Code examples can be found in the corresponding model folder.
Layers are the fundamental building blocks for NLP models. They can be used to
assemble new layers, networks, or models.
*[DenseEinsum](dense_einsum.py) implements a feedforward network using tf.einsum. This layer contains the einsum op, the associated weight, and the
logic required to generate the einsum expression for the given initialization
parameters.
*[Attention](attention.py) implements an optionally masked attention between two tensors, from_tensor and to_tensor, as described in ["Attention Is All You Need"](https://arxiv.org/abs/1706.03762). If `from_tensor` and `to_tensor` are the same, then this is self-attention.
*[CachedAttention](attention.py) implements an attention layer with cache used
for auto-agressive decoding.
*[Transformer](transformer.py) implements an optionally masked transformer as
described in ["Attention Is All You Need"](https://arxiv.org/abs/1706.03762).
*[OnDeviceEmbedding](on_device_embedding.py) implements efficient embedding lookups designed for TPU-based models.
*[PositionalEmbedding](position_embedding.py) creates a positional embedding
as described in ["BERT: Pre-training
of Deep Bidirectional Transformers for Language Understanding"]
(https://arxiv.org/abs/1810.04805).
*[SelfAttentionMask](self_attention_mask.py) creates a 3D attention mask from a 2D tensor mask.
*[MaskedSoftmax](masked_softmax.py) implements a softmax with an optional masking input. If no mask is provided to this layer, it performs a standard softmax; however, if a mask tensor is applied (which should be 1 in positions where the data should be allowed through, and 0 where the data should be masked), the output will have masked positions set to approximately zero.
Networks are combinations of layers (and possibly other networks). They are sub-units of models that would not be trained alone. It
encapsulates common network structures like a classification head
or a transformer encoder into an easily handled object with a
standardized configuration.
*[`TransformerEncoder`](transformer_encoder.py) implements a bi-directional
Transformer-based encoder as described in ["BERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805). It includes the embedding lookups,
transformer layers and pooling layer.
*[`AlbertTransformerEncoder`](albert_transformer_encoder.py) implements a
Transformer-encoder described in the paper ["ALBERT: A Lite BERT for
Self-supervised Learning of Language Representations]
(https://arxiv.org/abs/1909.11942). Compared with [BERT](https://arxiv.org/abs/1810.04805), ALBERT refactorizes embedding parameters
into two smaller matrices and shares parameters across layers.
*[`MaskedLM`](masked_lm.py) implements a masked language model for BERT pretraining. It assumes that the network being passed has a `get_embedding_table()` method.
*[`Classification`](classification.py) contains a single hidden layer, and is intended for use as a classification head.
*[`SpanLabeling`](span_labeling.py) implements a single-span labeler (that is, a prediction head that can predict one start and end index per batch item) based on a single dense hidden layer. It can be used in the SQuAD task.
