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_

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

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

Run layer normalization on the last dimension of the tensor.

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

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

Reads a BIO data.

sent_data: dict of sentence, key=pmid {pmid:[sent,sent, ...], pmid: ...}

data: list of json file [{pmid,abstract,title}, ...]

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

Performs invalid character removal and whitespace cleanup on text.

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.

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: Py

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

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

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

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

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

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

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

Compute the union of the current variables and checkpoint variables.

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

Runs layer normalization followed by dropout.

Loads a vocabulary file into a dictionary.

Runs a prediction on a set of features. Calling multiple times does *not* regenerate the graph which makes predict much faster.

Serializes this instance to a Python dictionary.