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.

Precision, Recall and F1 from the confusion matrix Parameters ---------- cm : tf.Tensor of type tf.int32, of shape (num_classes, num_class

Safe division, return 0 if denominator is 0

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

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

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Reads a BIO data.

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.

将数据转化为TF_Record 结构，作为模型数据输入 :param examples: 样本 :param label_list:标签list :param max_seq_length: 预先设定的最大序列长度 :param tokenizer: to

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 peice of text.

Get the n-best logits from a list.

Truncates a sequence pair in place to the maximum length.

RNN 类型 :return:

See base class.

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

将id形式的结果转化为真实序列结果 :param pred_ids_result: :param idx2label: :return:

将一个样本进行分析，然后将字转化为id, 标签转化为id,然后结构化到InputFeatures对象中 :param ex_index: index :param example: 一个样本 :param label_list: 标签列表 :param ma

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

创建X模型 :param bert_config: bert 配置 :param is_training: :param input_ids: 数据的idx 表示 :param input_mask: :param segment_ids:

:param candidate: :param answer: :return:

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.

Uses a confusion matrix to compute precision, recall and fbeta

Read a SQuAD json file into a list of SquadExample.

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

组合策略 :param pred_label_result: :param types: :return:

双向RNN :return:

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.

blstm-crf网络 :return:

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

:return: