def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
config = tf_example_utils.PretrainConversionConfig(
vocab_file=FLAGS.vocab_file,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
random_seed=FLAGS.random_seed,
masked_lm_prob=FLAGS.masked_lm_prob,
max_column_id=FLAGS.max_column_id,
max_row_id=FLAGS.max_row_id,
min_question_length=FLAGS.min_question_length,
max_question_length=FLAGS.max_question_length,
always_continue_cells=FLAGS.always_continue_cells,
strip_column_names=False,
)
pipeline = pretrain_utils.build_pretrain_data_pipeline(
input_file=FLAGS.input_file,
output_dir=FLAGS.output_dir,
config=config,
dupe_factor=FLAGS.dupe_factor,
min_num_rows=FLAGS.min_num_rows,
min_num_columns=FLAGS.min_num_columns,
)
beam_runner.run(pipeline)
def run(inputs, outputs, input_format):
beam_runner.run(
create_data.build_retrieval_pipeline(
input_files=inputs,
input_format=input_format,
output_files=outputs,
config=tf_example_utils.RetrievalConversionConfig(
vocab_file=FLAGS.vocab_file,
max_seq_length=FLAGS.max_seq_length,
max_column_id=FLAGS.max_column_id,
max_row_id=FLAGS.max_row_id,
strip_column_names=False,
cell_trim_length=FLAGS.cell_trim_length,
use_document_title=FLAGS.use_document_title,
),
converter_impl=FLAGS.converter_impl,
)).wait_until_finish()
def main(unused_argv):
r"""Reads nearest neigbors adds them to the interactions."""
del unused_argv
inputs_outputs = _get_inputs_outputs(
FLAGS.interaction_dir,
FLAGS.json_dir,
FLAGS.output_dir,
)
pipeline = add_negative_tables_to_interactions(
max_num_negatives=FLAGS.max_num_negatives,
input_interactions_files=inputs_outputs.input_interaction_files,
input_tables_file=FLAGS.input_tables_file,
input_json_files=inputs_outputs.input_json_files,
output_files=inputs_outputs.output_interaction_files,
)
beam_runner.run(pipeline)
def main(unused_argv):
del unused_argv
config = synthesize_entablement.SynthesizationConfig(
prob_count_aggregation=FLAGS.prob_count_aggregation, )
conversion_config = None
if FLAGS.convert_to_examples:
conversion_config = tf_example_utils.ClassifierConversionConfig(
vocab_file=FLAGS.vocab_file,
max_seq_length=FLAGS.max_seq_length,
max_column_id=FLAGS.max_seq_length,
max_row_id=FLAGS.max_seq_length,
strip_column_names=False,
)
pipeline = intermediate_pretrain_utils.build_pipeline(
mode=FLAGS.mode,
config=config,
use_fake_table=FLAGS.use_fake_table,
add_opposite_table=FLAGS.add_opposite_table,
drop_without_support_rate=FLAGS.drop_without_support_rate,
input_file=FLAGS.input_file,
output_dir=FLAGS.output_dir,
output_suffix=FLAGS.output_suffix,
conversion_config=conversion_config)
beam_runner.run(pipeline).wait_until_finish()
def main(_):
beam_runner.run(
build_pipeline(inputs=FLAGS.inputs,
output_dir=FLAGS.output_dir)).wait_until_finish()
