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:

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

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.

Reads a tab separated value file.

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.

Run layer normalization on the last dimension of the tensor.

do online prediction. each time make prediction for one instance. you can change to a batch if you want. :param line: a list. element is

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.

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

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

加载要素-描述-代表性描述的文件 :param source_file: :return: 返回一个dict: dict[factor_zh]={desc,sentence_repres1,sentence_repres2,sentence_repres3}

读取原始数据 :param data_path: :param category: :return:

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

写单个文件 :param data_list: :param target_file: :return:

Performs invalid character removal and whitespace cleanup on text.

Decodes a record to a TensorFlow example.

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.

Truncates a sequence pair in place to the maximum length.

See base class.

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

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

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 a classification model.

Creates a classification model.

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

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

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

输入3个数据文件和

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

获取允许的候选项 :param k_candi_tag_dict: '借款纠纷_拒绝履行偿还': {'label_zh': '拒绝履行偿还', 'desc': '未按时偿还借款|拒不偿还借款', 'sentence_repres1': '如果XX未按指定的期间履行给付金钱义务，应当

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

:param labels_prob_1: {'LB1': 0.0016595844645053148, 'LB2': 0.11449998617172241, 'LB3': 0.003680239664390683, :param labels_prob_2: {'LB1':

Gets the list of labels for this data set.

Gets the list of labels for this data set.

获得负样本 :param divorce_selectedtags_dict: :param labels_word_list: :param category: :param sentence: :param count: :param d

得到正样本的训练数据 :return:

Gets a collection of `InputExample`s for prediction.

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

Runs layer normalization followed by dropout.

从文件中读取标签对应的代表性的正样本 :param source_path: 文件所在位置 :return:

Loads a vocabulary file into a dictionary.

Returns `model_fn` closure for TPUEstimator.

通过预测得到标签的列表: 会综合使用多个标签或句子来做预测 :param sentence: 句子 :param category_en: 类别信息，如 'labor' : return: label_result_list: 预测除的标签. e.g. label_

Serializes this instance to a Python dictionary.

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

写多个文件 :param total_list: :param target_path: :return:

Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For sing

Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input.

Constructs a AdamWeightDecayOptimizer.

Constructs BertConfig. Args: vocab_size: Vocabulary size of `inputs_ids` in `BertModel`. hidden_size: Size of the encoder layers and

Constructor for BertModel. Args: config: `BertConfig` instance. is_training: bool. true for training model, false for eval model. Con

Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first seque

检测是否有有效候选项 :param labels_with_prob_dict: :return: True if has candidate; False is not has candidate

整合两个概率，概率取加权平均 :param labels_prob_1: :param labels_prob_2: :return: 加权平均后的概率

Convert a set of `InputExample`s to a list of `InputFeatures`.

Creates an optimizer training op.

Gaussian Error Linear Unit. This is a smoother version of the RELU. Original paper: https://arxiv.org/abs/1606.08415 Args: x: float Tensor

Compute the union of the current variables and checkpoint variables.

See base class.