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

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

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

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

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

An example input function to pass to predict. It must take a generator as input

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

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, hidde

Gets the list of labels for this data set.

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

Returns text encoded in a way suitable for print or `tf.logging`.

Serializes this instance to a JSON string.

Tokenizes a piece of text.

Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the gi

Checks whether the casing config is consistent with the checkpoint name.