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

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

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

Batch normalization on convolutional maps. Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflo

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

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

See base class.

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

:param words_list: [[w11, w12, ...], [w21, w22, ...], ...] :return:

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).

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

最大长度

最大长度

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

Converts a sequence of tokens into ids using the vocab.

@description: 模型训练 @param {type} @return:

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

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

Loads a vocabulary file into a dictionary.

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

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

Performs invalid character removal and whitespace cleanup on text.

Whether to use L2 weight decay for `param_name`.

Get the variable name from the tensor name.

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.

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

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

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

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

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:

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:

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

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

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

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

@description: 测试集评测 @param {type} @return:

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

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

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

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

使用LCQMC数据集，并将其转为word_id

Runs layer normalization followed by dropout.

Runs layer normalization followed by dropout.

@description: 无标注数据评测 @param {type} @return: