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:

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

预测小批量句子

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

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

得到的lines是一个元素为字典形式的列表，sentence是句子，labels是一个含有多标签的列表

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.

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 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`.

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

传入预测标签，目标标签，tags地址。

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.

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.

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.

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

:param task: 任务名，divorce，labor， loan :param tagDir: 任务标签文件，tags.txt :param originDir: 若是文件夹，则选择最后一个模型，若是文件名，则选择该模型文件。 :param convertD

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 `TrainingInstance`s for a single document.

Creates the predictions for the masked LM objective.

Creates the predictions for the masked LM objective.

:param bert_config: :param input_ids: :param input_mask: :param segment_ids: :param num_labels: 类别数 :param model_type: bert后接

Creates a classification model.

Creates a classification model.

Creates a classification model.