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A specification of BERT model for question answering.

uri TF-Hub path/url to Bert module.
model_dir The location of the model checkpoint files.
seq_len Length of the sequence to feed into the model.
query_len Length of the query to feed into the model.
doc_stride The stride when we do a sliding window approach to take chunks of the documents.
dropout_rate The rate for dropout.
initializer_range The stdev of the truncated_normal_initializer for initializing all weight matrices.
learning_rate The initial learning rate for Adam.
distribution_strategy A string specifying which distribution strategy to use. Accepted values are 'off', 'one_device', 'mirrored', 'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case insensitive. 'off' means not to use Distribution Strategy; 'tpu' means to use TPUStrategy using tpu_address.
num_gpus How many GPUs to use at each worker with the DistributionStrategies API. The default is -1, which means utilize all available GPUs.
tpu TPU address to connect to.
trainable boolean, whether pretrain layer is trainable.
predict_batch_size Batch size for prediction.
do_lower_case boolean, whether to lower case the input text. Should be True for uncased models and False for cased models.
is_tf2 boolean, whether the hub module is in TensorFlow 2.x format.
tflite_input_name Dict, input names for the TFLite model.
tflite_output_name Dict, output names for the TFLite model.
init_from_squad_model boolean, whether to initialize from the model that is already retrained on Squad 1.1.
default_batch_size Default batch size for training.
name Name of the object.



Builds the class. Used for lazy initialization.


Converts examples to features and write them into TFRecord file.


Creates the model for qa task.


Evaluate QA model.

model The keras model to be evaluated.
tflite_filepath File path to the TFLite model.
input_fn Function that returns a tf.data.Dataset used for evaluation.
num_steps Number of steps to evaluate the model.
eval_examples List of squad_lib.SquadExample for evaluation data.
eval_features List of squad_lib.InputFeatures for evaluation data.
predict_file The input predict file.
version_2_with_negative Whether the input predict file is SQuAD 2.0 format.
max_answer_length The maximum length of an answer that can be generated. This is needed because the start and end predictions are not conditioned on one another.
null_score_diff_threshold If null_score - best_non_null is greater than the threshold, predict null. This is only used for SQuAD v2.
verbose_logging If true, all of the warnings related to data processing will be printed. A number of warnings are expected for a normal SQuAD evaluation.
output_dir The output directory to save output to json files: predictions.json, nbest_predictions.json, null_odds.json. If None, skip saving to json files.

A dict contains two metrics: Exact match rate and F1 score.


Gets the configuration.


Gets the default quantization configuration.


Gets the dictionary describing the features.


Predicts the dataset from input_fn for model.


Predicts the input_fn dataset for TFLite model in tflite_filepath.


Reorders the tflite input details to map the order of keras model.


Reorders the tflite output details to map the order of keras model.


Prints the file path to the vocabulary.


Dispatches records to features and labels.


Run bert QA training.

compat_tf_versions [2]
convert_from_saved_model_tf2 True
need_gen_vocab False