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

Reads a tab separated value file.

Creates a `truncated_normal_initializer` with the given range.

Reads a tab separated value file.

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

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

Creates a `truncated_normal_initializer` with the given range.

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:

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

Reads a tab separated value file.

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

Run layer normalization on the last dimension of the tensor.

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

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 *keepin

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

See base class.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

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.

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

A dense layer with 2D kernel. Args: input_tensor: Float tensor with rank 3. output_size: The size of output dimension. initializer: Kern

A dense layer with 2D kernel. Args: input_tensor: Float tensor with rank 3. output_size: The size of output dimension. initializer: Kern

A dense layer with 3D kernel. Args: input_tensor: float Tensor of shape [batch, seq_length, hidden_size]. num_attention_heads: Number of att

A dense layer with 3D kernel. Args: input_tensor: float Tensor of shape [batch, seq_length, hidden_size]. num_attention_heads: Number of att

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

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

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

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

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

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

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 hi

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

Run layer normalization on the last dimension of the tensor.

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.

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

Truncates a sequence pair in place to the maximum length.

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

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

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

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

Creates the predictions for the masked LM objective.

Creates the predictions for the masked LM objective.

Creates a classification model.

Looks up words embeddings for id tensor, but in a factorized style followed by albert. it is used to reduce much percentage of parameters previous exi

turn sentences into word pieces.

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

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

输入一句话，返回一句经过处理的话: 为了支持中文全称mask，将被分开的词，将上特殊标记("#")，使得后续处理模块，能够知道哪些字是属于同一个词的。 :param segment: 一句话 :return: 一句处理过的话

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.

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

Returns text encoded in a way suitable for print or `tf.logging`.

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

Multi-headed, multi-layer Transformer from "Attention is All You Need". This is almost an exact implementation of the original Transformer encoder.

Truncates a pair of sequences to a maximum sequence length.

Constructs a LAMBOptimizer.

Performs invalid character removal and whitespace cleanup on text.

Performs invalid character removal and whitespace cleanup on text.

Decodes a record to a TensorFlow example.

Decodes a record to a TensorFlow example.

Decodes a record to a TensorFlow example.

Decodes a record to a TensorFlow example.

Decodes a record to a TensorFlow example.