Reads a tab separated value file.

Creates a `truncated_normal_initializer` with the given range.

Returns a list of the shape of tensor, preferring static dimensions. Args: tensor: A tf.Tensor object to find the shape of. expected_rank:

Tokenizes a piece of text.

Converts a sequence of [tokens|ids] using the vocab.

Perform dropout. Args: input_tensor: float Tensor. dropout_prob: Python float. The probability of dropping out a value (NOT of *keepi

Constructs a `BertConfig` from a json file of parameters.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Converts `text` to Unicode (if it's not already), assuming utf-8 input.

Convert a set of `InputExample`s to a TFRecord file.

Creates an `input_fn` closure to be passed to TPUEstimator.

Gets final hidden layer of encoder. Returns: float Tensor of shape [batch_size, seq_length, hidden_size] corresponding to the final h

Creates a random int32 tensor of the shape within the vocab size.

Run layer normalization on the last dimension of the tensor.

Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix).

Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given voca

Runs basic whitespace cleaning and splitting on a piece of text.

Get the n-best logits from a list.

See base class.

Loads a data file into a list of `InputBatch`s.

Converts a single `InputExample` into a single `InputFeatures`.

Gets a collection of `InputExample`s for the dev set.

Gets the list of labels for this data set.

Gets a collection of `InputExample`s for prediction.

Gets a collection of `InputExample`s for the train set.

Creates an `input_fn` closure to be passed to TPUEstimator.

Creates an `input_fn` closure to be passed to TPUEstimator.

Read a SQuAD json file into a list of SquadExample.

Reshapes a rank 2 tensor back to its original rank >= 2 tensor.

Check if this is the 'max context' doc span for the token.

Performs invalid character removal and whitespace cleanup on text.

Compute softmax probability over raw logits.

Decodes a record to a TensorFlow example.

Decodes a record to a TensorFlow example.

Decodes a record to a TensorFlow example.

Whether to use L2 weight decay for `param_name`.

Get the variable name from the tensor name.

Returns tokenized answer spans that better match the annotated answer.

Checks whether CP is the codepoint of a CJK character.

Checks whether `chars` is a control character.

Checks whether `chars` is a punctuation character.

Checks whether `chars` is a whitespace character.

Splits punctuation on a piece of text.

Strips accents from a piece of text.

Adds whitespace around any CJK character.

Truncates a sequence pair in place to the maximum length.

Truncates a sequence pair in place to the maximum length.

Checks that all the tensors in the graph are reachable from outputs.

Raises an exception if the tensor rank is not of the expected rank. Args: tensor: A tf.Tensor to check the rank of. expected_rank: Python i

Performs multi-headed attention from `from_tensor` to `to_tensor`. This is an implementation of multi-headed attention based on "Attention is all

Loads a data file into a list of `InputBatch`s.

Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: in

Creates `TrainingInstance`s for a single document.

Creates the predictions for the masked LM objective.

Creates a classification model.

Creates a classification model.

Creates a classification model.

Get the vocab file and casing info from the Hub module.

Create `TrainingInstance`s from raw text.

Looks up words embeddings for id tensor. Args: input_ids: int32 Tensor of shape [batch_size, seq_length] containing word ids. vocab_s

Performs various post-processing on a word embedding tensor. Args: input_tensor: float Tensor of shape [batch_size, seq_length, embedding

Flattens (potentially nested) a tuple/dictionary/list to a list.

Constructs a `BertConfig` from a Python dictionary of parameters.

Gathers the vectors at the specific positions over a minibatch.

Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`. Args: activation_string: String name of the activation function. Returns:

Gets output of the embedding lookup (i.e., input to the transformer). Returns: float Tensor of shape [batch_size, seq_length, hidden_size]

Project the tokenized prediction back to the original text.

Get loss and log probs for the masked LM.

Get loss and log probs for the next sentence prediction.

Finds all of the tensors in graph that are unreachable from outputs.

Creates an `input_fn` closure to be passed to TPUEstimator.

Runs layer normalization followed by dropout.

Loads a vocabulary file into a dictionary.

Returns `model_fn` closure for TPUEstimator.

Returns `model_fn` closure for TPUEstimator.

Returns `model_fn` closure for TPUEstimator.