A single training/test example for simple sequence classification. Args: guid: Unique id for the example. text_a: string. Th

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A placeholder to capture an object. This is useful when we need to capture a Python object in the Tensorflow control flow body function and use i

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.Flo

r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.Float

r""" Compute a single vector summary of a sequence hidden states according to various possibilities: Args of the config class: sum

r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.Float

r"""A placeholder identity operator that is argument-insensitive.

A single set of features of data.

A single set of features of data.

A single set of features of data.

A single set of features of data.

A single set of features of data.

A single set of features of data.

A single set of features of data.

A single set of features of data.

Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of examples

r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.Float

Runs basic tokenization (punctuation splitting, lower casing, etc.).

r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.Float

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single training/test example for simple sequence classification.

A single set of features of data.

Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of examples

Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of examples

Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of exampl

Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of exampl

Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of examples

Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of exampl

Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of exampl

custom progress bar Example: >>> pbar = ProgressBar(n_total=30,desc='training') >>> step = 2 >>> pbar(step=step)

r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.Float

A data structure representing the input_fn returned values. This also supports the returned value from input_fn as `Dataset`.

Inputs with `_StopSignals` inserted into the dataset.

Support for `eval_metrics` and `host_call` in TPUEstimatorSpec.

Hook to run host calls when use_tpu=False.

Runs basic tokenization (punctuation splitting, lower casing, etc.).

Configuration class to store the configuration of a `BertModel`.

Writes InputFeature to TF example file.

Writes InputFeature to TF example file.

Writes InputFeature to TF example file.

Writes InputFeature to TF example file.

Writes InputFeature to TF example file.

Writes InputFeature to TF example file.

r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.Float

r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.Float

Compute SQuAD 2.0 answer class from classification and start tokens hidden states.

Compute SQuAD end_logits from sequence hidden states and start token hidden state.

Compute SQuAD start_logits from sequence hidden states.

A Session hook setting up the TPU initialization, infeed, and outfeed. This hook does two major things: 1. initialize and shutdown TPU system.

A single training instance (sentence pair).

A single training instance (sentence pair).

A single training instance (sentence pair).

Tracks references to Tensors defined in TPU replication.

Manages work queue and thread for a infeed/outfeed thread.

ALBert embeddings.

Implements Adam algorithm with weight decay fix. Parameters: lr (float): learning rate. Default 1e-3. betas (tuple of 2 floats):

A basic Adam optimizer that includes "correct" L2 weight decay.

A basic Adam optimizer that includes "correct" L2 weight decay.

A basic Adam optimizer that includes "correct" L2 weight decay.

A basic Adam optimizer that includes "correct" L2 weight decay.