def main(_):
with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
input_meta_data = json.loads(reader.read().decode('utf-8'))
if not FLAGS.model_dir:
FLAGS.model_dir = '/tmp/bert20/'
strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=FLAGS.distribution_strategy,
num_gpus=FLAGS.num_gpus,
tpu_address=FLAGS.tpu)
max_seq_length = input_meta_data['max_seq_length']
train_input_fn = run_classifier_bert.get_dataset_fn(FLAGS.train_data_path,
max_seq_length,
FLAGS.train_batch_size,
is_training=True)
eval_input_fn = run_classifier_bert.get_dataset_fn(FLAGS.eval_data_path,
max_seq_length,
FLAGS.eval_batch_size,
is_training=False)
albert_config = albert_configs.AlbertConfig.from_json_file(
FLAGS.bert_config_file)
if FLAGS.mode == 'train_and_eval':
run_classifier_bert.run_bert(strategy, input_meta_data, albert_config,
train_input_fn, eval_input_fn)
elif FLAGS.mode == 'predict':
predict(strategy, albert_config, input_meta_data, eval_input_fn)
else:
raise ValueError('Unsupported mode is specified: %s' % FLAGS.mode)
return
def main(_):
# Users should always run this script under TF 2.x
assert tf.version.VERSION.startswith('2.')
with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
input_meta_data = json.loads(reader.read().decode('utf-8'))
if not FLAGS.model_dir:
FLAGS.model_dir = '/tmp/bert20/'
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=FLAGS.distribution_strategy,
num_gpus=FLAGS.num_gpus,
tpu_address=FLAGS.tpu)
max_seq_length = input_meta_data['max_seq_length']
train_input_fn = run_classifier_bert.get_dataset_fn(FLAGS.train_data_path,
max_seq_length,
FLAGS.train_batch_size,
is_training=True)
eval_input_fn = run_classifier_bert.get_dataset_fn(FLAGS.eval_data_path,
max_seq_length,
FLAGS.eval_batch_size,
is_training=False)
albert_config = albert_configs.AlbertConfig.from_json_file(
FLAGS.bert_config_file)
run_classifier_bert.run_bert(strategy, input_meta_data, albert_config,
train_input_fn, eval_input_fn)