# Networks

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 or regression (if number of classes is set
to 1) head.

* [`TokenClassification`](token_classification.py) contains a single hidden
layer, and is intended for use as a token 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.

