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

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

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`.

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 the predictions for the masked LM objective.

Creates a classification model.

Creates a classification model.

Creates a classification model.

Create `TrainingInstance`s from raw text.

calculate crf loss :param project_logits: [1, num_steps, num_tags] :return: scalar loss

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

Multi-class fbeta metric for Tensorflow Parameters ---------- labels : Tensor of tf.int32 or tf.int64 The true labels predicti

Flattens (potentially nested) a tuple/dictionary/list to a list.

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

Gathers the vectors at the specific positions over a minibatch.

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.

Gets output of the embedding lookup (i.e., input to the transformer). Returns: float Tensor of shape [batch_size, seq_length, hidden_size]

Project the tokenized prediction back to the original text.

Gets the list of labels for this data set.

Get loss and log probs for the masked LM.

Get loss and log probs for the next sentence prediction.

Gets a collection of `InputExample`s for prediction.

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

Finds all of the tensors in graph that are unreachable from outputs.

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

进行问答测试： 1、 实体检索:输入问题，ner得出实体集合，在数据库中检索与输入实体相关的所有三元组 2、 属性映射——bert分类/文本相似度 + 非语义匹配：如果所得三元组的关系(attribute)属性是 输入问题 字符串的子集，将所得三元组的答案(

Runs layer normalization followed by dropout.

Loads a vocabulary file into a dictionary.

构建模型 :param bert_config: :param num_labels: :param init_checkpoint: :param learning_rate: :param num_train_steps: :param

Returns `model_fn` closure for TPUEstimator.

Returns `model_fn` closure for TPUEstimator.

Returns `model_fn` closure for TPUEstimator.

Returns `model_fn` closure for TPUEstimator.

Returns `model_fn` closurimport_tfe for TPUEstimator.

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

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

Write a InputFeature to the TFRecordWriter as a tf.train.Example.

hidden layer between lstm layer and logits :param lstm_outputs: [batch_size, num_steps, emb_size] :return: [batch_size, num_

hidden layer between input layer and logits :param lstm_outputs: [batch_size, num_steps, emb_size] :return: [batch_size, num

Read a list of `InputExample`s from an input file.

将模型标注序列和输入序列结合 转化为结果 :param string: 输入序列 :param tags: 标注结果 :return:

Serializes this instance to a Python dictionary.

Serializes this instance to a JSON string.

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.

Validate the input FLAGS or throw an exception.

Create TF example files from `TrainingInstance`s.

Write final predictions to the json file and log-odds of null if needed.

将序列解析结果写入到文件中 只在mode=test的时候启用 :param tokens: :param mode: :return:

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

BLSTM-CRF 网络 :param embedded_chars: Fine-tuning embedding input :param hidden_unit: LSTM的隐含单元个数 :param cell_type: RNN

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

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

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

Constructs a AdamWeightDecayOptimizer.

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