tflite_model_maker.question_answer.BertQaSpec

A specification of BERT model for question answering.

tflite_model_maker.question_answer.BertQaSpec(
    uri='https://hub.tensorflow.google.cn/tensorflow/bert_en_uncased_L-12_H-768_A-12/1',
    model_dir=None,
    seq_len=384,
    query_len=64,
    doc_stride=128,
    dropout_rate=0.1,
    initializer_range=0.02,
    learning_rate=8e-05,
    distribution_strategy='mirrored',
    num_gpus=-1,
    tpu='',
    trainable=True,
    predict_batch_size=8,
    do_lower_case=True,
    is_tf2=True,
    tflite_input_name=None,
    tflite_output_name=None,
    init_from_squad_model=False,
    default_batch_size=16,
    name='Bert'
)

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.

Methods

build

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build()

Builds the class. Used for lazy initialization.

convert_examples_to_features

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convert_examples_to_features(
    examples, is_training, output_fn, batch_size
)

Converts examples to features and write them into TFRecord file.

create_model

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create_model()

Creates the model for qa task.

evaluate

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evaluate(
    model,
    tflite_filepath,
    dataset,
    num_steps,
    eval_examples,
    eval_features,
    predict_file,
    version_2_with_negative,
    max_answer_length,
    null_score_diff_threshold,
    verbose_logging,
    output_dir
)

Evaluate QA model.

Args
model The keras model to be evaluated.
tflite_filepath File path to the TFLite model.
dataset 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.

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

get_config

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get_config()

Gets the configuration.

get_default_quantization_config

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get_default_quantization_config()

Gets the default quantization configuration.

get_name_to_features

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get_name_to_features(
    is_training
)

Gets the dictionary describing the features.

predict

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predict(
    model, dataset, num_steps
)

Predicts the dataset for model.

predict_tflite

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predict_tflite(
    tflite_filepath, dataset
)

Predicts the dataset for TFLite model in tflite_filepath.

reorder_input_details

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reorder_input_details(
    tflite_input_details
)

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

reorder_output_details

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reorder_output_details(
    tflite_output_details
)

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

save_vocab

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save_vocab(
    vocab_filename
)

Prints the file path to the vocabulary.

select_data_from_record

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select_data_from_record(
    record
)

Dispatches records to features and labels.

train

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train(
    train_ds, epochs, steps_per_epoch, **kwargs
)

Run bert QA training.

Args
train_ds tf.data.Dataset, training data to be fed in tf.keras.Model.fit().
epochs Integer, training epochs.
steps_per_epoch Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. If steps_per_epoch is None, the epoch will run until the input dataset is exhausted.
**kwargs Other parameters used in the tf.keras.Model.fit().

Returns
tf.keras.Model, the keras model that's already trained.

compat_tf_versions [2]
convert_from_saved_model_tf2 True
need_gen_vocab False