def add_cmdline_args(parser):
"""Add CLI args."""
TorchClassifierAgent.add_cmdline_args(parser)
parser = parser.add_argument_group('BERT Classifier Arguments')
parser.add_argument(
'--type-optimization',
type=str,
default='all_encoder_layers',
choices=[
'additional_layers',
'top_layer',
'top4_layers',
'all_encoder_layers',
'all',
],
help='which part of the encoders do we optimize '
'(defaults to all layers)',
)
parser.add_argument(
'--add-cls-token',
type='bool',
default=True,
help='add [CLS] token to text vec',
)
parser.add_argument(
'--sep-last-utt',
type='bool',
default=False,
help='separate the last utterance into a different'
'segment with [SEP] token in between',
)
parser.set_defaults(dict_maxexs=0) # skip building dictionary
def add_cmdline_args(parser):
TransformerRankerAgent.add_cmdline_args(parser) # add transformer args
TorchClassifierAgent.add_cmdline_args(parser)
parser.add_argument(
'--load-from-pretrained-ranker',
type='bool',
default=False,
help='load model from base transformer ranking model '
'(used for pretraining)',
)
parser.set_params(reduction_type='first')
def add_cmdline_args(parser):
TorchClassifierAgent.add_cmdline_args(parser)
parser = parser.add_argument_group('BERT Classifier Arguments')
parser.add_argument('--bert-id', type=str, default='bert-base-uncased')
parser.add_argument('--type-optimization',
type=str,
default='all_encoder_layers',
choices=[
'additional_layers', 'top_layer',
'top4_layers', 'all_encoder_layers', 'all'
],
help='which part of the encoders do we optimize '
'(defaults to all layers)')
def add_cmdline_args(parser):
TorchClassifierAgent.add_cmdline_args(parser)
parser = parser.add_argument_group('BERT Classifier Arguments')
parser.add_argument('--type-optimization',
type=str,
default='all_encoder_layers',
choices=[
'additional_layers', 'top_layer',
'top4_layers', 'all_encoder_layers', 'all'
],
help='which part of the encoders do we optimize '
'(defaults to all layers)')
parser.set_defaults(
dict_maxexs=0, # skip building dictionary
)