Based on https://github.com/kevinlu1211/pytorch-unet-resnet-50-encoder/blob/master/u_net_resnet_50_encoder.py Helper module that consists of

Based on https://github.com/kevinlu1211/pytorch-unet-resnet-50-encoder/blob/master/u_net_resnet_50_encoder.py Up block that encapsulates one

Track a series of values and provide access to smoothed values over a window or the global series average.

Track a series of values and provide access to smoothed values over a window or the global series average.

Implements Adafactor algorithm. This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` (see https://

Implements Adafactor algorithm. This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` (see https://

Implements Adafactor algorithm. This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` (see https://

Implements the AdaHessian algorithm from "ADAHESSIAN: An Adaptive Second OrderOptimizer for Machine Learning" Arguments: params (ite

Implements the AdaHessian algorithm from "ADAHESSIAN: An Adaptive Second OrderOptimizer for Machine Learning" Arguments: params (ite

Implements the AdaHessian algorithm from "ADAHESSIAN: An Adaptive Second OrderOptimizer for Machine Learning" Arguments: params (ite

Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (:obj:`

Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (:obj:`

Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (:obj:`

Based on https://github.com/kevinlu1211/pytorch-unet-resnet-50-encoder/blob/master/u_net_resnet_50_encoder.py

Cosine decay with restarts. This is described in the paper https://arxiv.org/abs/1608.03983. Inspiration from https://github.com/all

Cosine decay with restarts. This is described in the paper https://arxiv.org/abs/1608.03983. Inspiration from https://github.com/all

Cosine decay with restarts. This is described in the paper https://arxiv.org/abs/1608.03983. Inspiration from https://github.com/all

Implements Nadam algorithm (a variant of Adam based on Nesterov momentum). It has been proposed in `Incorporating Nesterov Momentum into Adam`__.

Implements Nadam algorithm (a variant of Adam based on Nesterov momentum). It has been proposed in `Incorporating Nesterov Momentum into Adam`__.

Implements Nadam algorithm (a variant of Adam based on Nesterov momentum). It has been proposed in `Incorporating Nesterov Momentum into Adam`__.

Implements Novograd algorithm. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter g

Implements Novograd algorithm. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter g

Implements Novograd algorithm. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter g

Decay the LR by a factor every time the validation loss plateaus.

Decay the LR by a factor every time the validation loss plateaus.

Decay the LR by a factor every time the validation loss plateaus.

Implements RMSprop algorithm (TensorFlow style epsilon) NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt

Implements RMSprop algorithm (TensorFlow style epsilon) NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt

Implements RMSprop algorithm (TensorFlow style epsilon) NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt

Hyberbolic-Tangent decay with restarts. This is described in the paper https://arxiv.org/abs/1806.01593

Hyberbolic-Tangent decay with restarts. This is described in the paper https://arxiv.org/abs/1806.01593

Hyberbolic-Tangent decay with restarts. This is described in the paper https://arxiv.org/abs/1806.01593

Runs WordPiece tokenization.

Runs WordPiece tokenization.

Runs WordPiece tokenization.

r"""Implements AdamW algorithm. The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. The AdamW variant

r"""Implements AdamW algorithm. The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. The AdamW variant

r"""Implements AdamW algorithm. The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. The AdamW variant

r""" Construct a BERT tokenizer. Based on WordPiece. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains mo

r""" Construct a BERT tokenizer. Based on WordPiece. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains mo

r""" Construct a BERT tokenizer. Based on WordPiece. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains mo

Parameter Scheduler Base Class A scheduler base class that can be used to schedule any optimizer parameter groups. Unlike the builtin PyTorc

Parameter Scheduler Base Class A scheduler base class that can be used to schedule any optimizer parameter groups. Unlike the builtin PyTorc

Parameter Scheduler Base Class A scheduler base class that can be used to schedule any optimizer parameter groups. Unlike the builtin PyTorc