def fit(self,
trn_data,
dev_data,
save_dir,
embed,
context_layer,
batch_size=40,
batch_max_tokens=700,
lexical_dropout=0.5,
dropout=0.2,
span_width_feature_size=20,
ffnn_size=150,
ffnn_depth=2,
argument_ratio=0.8,
predicate_ratio=0.4,
max_arg_width=30,
mlp_label_size=100,
enforce_srl_constraint=False,
use_gold_predicates=False,
doc_level_offset=True,
use_biaffine=False,
lr=1e-3,
transformer_lr=1e-5,
adam_epsilon=1e-6,
weight_decay=0.01,
warmup_steps=0.1,
grad_norm=5.0,
gradient_accumulation=1,
loss_reduction='sum',
devices=None,
logger=None,
seed=None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(
self,
trn_data,
dev_data,
save_dir,
text_a_key=None,
text_b_key=None,
label_key=None,
transformer=None,
max_seq_length=512,
truncate_long_sequences=True,
# hidden_dropout_prob=0.0,
lr=5e-5,
transformer_lr=None,
adam_epsilon=1e-6,
weight_decay=0,
warmup_steps=0.1,
batch_size=32,
batch_max_tokens=None,
epochs=3,
logger=None,
# transform=None,
devices: Union[float, int, List[int]] = None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def __init__(self,
trn: str = None,
dev: str = None,
tst: str = None,
sampler_builder: SamplerBuilder = None,
dependencies: str = None,
scalar_mix: ScalarMixWithDropoutBuilder = None,
use_raw_hidden_states=False,
lr=2e-3, separate_optimizer=False,
punct=False,
tree=True,
pad_rel=None,
apply_constraint=False,
single_root=True,
no_zero_head=None,
n_mlp_arc=500,
n_mlp_rel=100,
mlp_dropout=.33,
mu=.9,
nu=.9,
epsilon=1e-12,
decay=.75,
decay_steps=5000,
cls_is_bos=True,
use_pos=False,
**kwargs) -> None:
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.vocabs = VocabDict()
def fit(self,
trn_data,
dev_data,
save_dir,
epochs=5,
append_after_sentence=None,
eos_chars=None,
eos_char_min_freq=200,
eos_char_is_punct=True,
char_min_freq=None,
window_size=5,
batch_size=32,
lr=0.001,
grad_norm=None,
loss_reduction='sum',
embedding_size=128,
rnn_type: str = 'LSTM',
rnn_size=256,
rnn_layers=1,
rnn_bidirectional=False,
dropout=0.2,
devices=None,
logger=None,
seed=None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def distill(self,
teacher: str,
trn_data,
dev_data,
save_dir,
batch_size=None,
epochs=None,
kd_criterion='kd_ce_loss',
temperature_scheduler='flsw',
devices=None,
logger=None,
seed=None,
**kwargs):
devices = devices or cuda_devices()
if isinstance(kd_criterion, str):
kd_criterion = KnowledgeDistillationLoss(kd_criterion)
if isinstance(temperature_scheduler, str):
temperature_scheduler = TemperatureScheduler.from_name(temperature_scheduler)
teacher = self.build_teacher(teacher, devices=devices)
self.vocabs = teacher.vocabs
config = copy(teacher.config)
batch_size = batch_size or config.get('batch_size', None)
epochs = epochs or config.get('epochs', None)
config.update(kwargs)
return super().fit(**merge_locals_kwargs(locals(),
config,
excludes=('self', 'kwargs', '__class__', 'config')))
def fit(self,
trn_data,
dev_data,
save_dir,
embed: Embedding,
context_layer,
sampler='sorting',
n_buckets=32,
batch_size=50,
lexical_dropout=0.5,
ffnn_size=150,
is_flat_ner=True,
doc_level_offset=True,
lr=1e-3,
transformer_lr=1e-5,
adam_epsilon=1e-6,
weight_decay=0.01,
warmup_steps=0.1,
grad_norm=5.0,
epochs=50,
loss_reduction='sum',
gradient_accumulation=1,
ret_tokens=True,
tagset=None,
sampler_builder=None,
devices=None,
logger=None,
seed=None,
**kwargs
):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(self,
trn_data,
dev_data,
save_dir,
batch_size=50,
epochs=100,
embed=100,
rnn_input=None,
rnn_hidden=256,
drop=0.5,
lr=0.001,
patience=10,
crf=True,
optimizer='adam',
token_key='token',
tagging_scheme=None,
anneal_factor: float = 0.5,
delimiter=None,
anneal_patience=2,
devices=None,
token_delimiter=None,
logger=None,
verbose=True,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def __init__(self,
trn: str = None,
dev: str = None,
tst: str = None,
sampler_builder: SamplerBuilder = None,
dependencies: str = None,
scalar_mix: ScalarMixWithDropoutBuilder = None,
use_raw_hidden_states=False,
lr=None,
separate_optimizer=False,
cls_is_bos=True,
sep_is_eos=True,
delete=('', ':', '``', "''", '.', '?', '!', '-NONE-', 'TOP',
',', 'S1'),
equal=(('ADVP', 'PRT'), ),
mbr=True,
n_mlp_span=500,
n_mlp_label=100,
mlp_dropout=.33,
no_subcategory=True,
**kwargs) -> None:
if isinstance(equal, tuple):
equal = dict(equal)
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.vocabs = VocabDict()
def fit(self,
trn_data,
dev_data,
save_dir,
transformer=None,
lr=5e-5,
transformer_lr=None,
adam_epsilon=1e-8,
weight_decay=0,
warmup_steps=0.1,
batch_size=32,
gradient_accumulation=1,
grad_norm=5.0,
transformer_grad_norm=None,
average_subwords=False,
scalar_mix: Union[ScalarMixWithDropoutBuilder, int] = None,
word_dropout=None,
hidden_dropout=None,
max_sequence_length=None,
ret_raw_hidden_states=False,
batch_max_tokens=None,
epochs=3,
logger=None,
devices: Union[float, int, List[int]] = None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def __init__(self,
trn: str = None,
dev: str = None,
tst: str = None,
sampler_builder: SamplerBuilder = None,
dependencies: str = None,
scalar_mix: ScalarMixWithDropoutBuilder = None,
use_raw_hidden_states=False,
lr=1e-3,
separate_optimizer=False,
lexical_dropout=0.5,
dropout=0.2,
span_width_feature_size=20,
ffnn_size=150,
ffnn_depth=2,
argument_ratio=0.8,
predicate_ratio=0.4,
max_arg_width=30,
mlp_label_size=100,
enforce_srl_constraint=False,
use_gold_predicates=False,
doc_level_offset=True,
use_biaffine=False,
loss_reduction='mean',
with_argument=' ',
**kwargs) -> None:
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.vocabs = VocabDict()
def fit(self,
trn_data,
dev_data,
save_dir,
encoder,
lr=5e-5,
transformer_lr=None,
adam_epsilon=1e-8,
weight_decay=0,
warmup_steps=0.1,
grad_norm=1.0,
n_mlp_span=500,
n_mlp_label=100,
mlp_dropout=.33,
batch_size=None,
batch_max_tokens=5000,
gradient_accumulation=1,
epochs=30,
patience=0.5,
mbr=True,
sampler_builder=None,
delete=('', ':', '``', "''", '.', '?', '!', '-NONE-', 'TOP', ',',
'S1'),
equal=(('ADVP', 'PRT'), ),
no_subcategory=True,
eval_trn=True,
transform=None,
devices=None,
logger=None,
seed=None,
**kwargs):
if isinstance(equal, tuple):
equal = dict(equal)
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(
self,
encoder: Embedding,
tasks: Dict[str, Task],
save_dir,
epochs,
patience=0.5,
lr=1e-3,
encoder_lr=5e-5,
adam_epsilon=1e-8,
weight_decay=0.0,
warmup_steps=0.1,
gradient_accumulation=1,
grad_norm=5.0,
encoder_grad_norm=None,
decoder_grad_norm=None,
tau: float = 0.8,
transform=None,
# prune: Callable = None,
eval_trn=True,
prefetch=None,
tasks_need_custom_eval=None,
_device_placeholder=False,
devices=None,
logger=None,
seed=None,
**kwargs):
trn_data, dev_data, batch_size = 'trn', 'dev', None
task_names = list(tasks.keys())
return super().fit(
**merge_locals_kwargs(locals(),
kwargs,
excludes=('self', 'kwargs', '__class__',
'tasks')), **tasks)
def fit(self,
trn_data,
dev_data,
save_dir,
encoder,
batch_size=None,
batch_max_tokens=17776,
epochs=1000,
gradient_accumulation=4,
char2concept_dim=128,
char2word_dim=128,
cnn_filters=((3, 256), ),
concept_char_dim=32,
concept_dim=300,
dropout=0.2,
embed_dim=512,
eval_every=20,
ff_embed_dim=1024,
graph_layers=2,
inference_layers=4,
lr_scale=1.0,
ner_dim=16,
num_heads=8,
pos_dim=32,
pretrained_file=None,
rel_dim=100,
snt_layers=4,
start_rank=0,
unk_rate=0.33,
warmup_steps=2000,
with_bert=True,
word_char_dim=32,
word_dim=300,
lr=1.,
transformer_lr=None,
adam_epsilon=1e-6,
weight_decay=1e-4,
grad_norm=1.0,
joint_arc_concept=False,
joint_rel=False,
external_biaffine=False,
optimize_every_layer=False,
squeeze=False,
levi_graph=False,
separate_rel=False,
extra_arc=False,
bart=False,
shuffle_sibling_steps=50000,
vocab_min_freq=5,
amr_version='2.0',
devices=None,
logger=None,
seed=None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(self,
trn_data,
dev_data,
save_dir,
feat=None,
n_embed=100,
pretrained_embed=None,
transformer=None,
average_subwords=False,
word_dropout: float = 0.2,
transformer_hidden_dropout=None,
layer_dropout=0,
mix_embedding: int = None,
embed_dropout=.33,
n_lstm_hidden=400,
n_lstm_layers=3,
hidden_dropout=.33,
n_mlp_arc=500,
n_mlp_rel=100,
mlp_dropout=.33,
arc_dropout=None,
rel_dropout=None,
arc_loss_interpolation=0.4,
lr=2e-3,
transformer_lr=5e-5,
mu=.9,
nu=.9,
epsilon=1e-12,
clip=5.0,
decay=.75,
decay_steps=5000,
weight_decay=0,
warmup_steps=0.1,
separate_optimizer=True,
patience=100,
batch_size=None,
sampler_builder=None,
lowercase=False,
epochs=50000,
apply_constraint=False,
single_root=None,
no_zero_head=None,
punct=False,
min_freq=2,
logger=None,
verbose=True,
unk=UNK,
pad_rel=None,
max_sequence_length=512,
gradient_accumulation=1,
devices: Union[float, int, List[int]] = None,
transform=None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def distill(self,
teacher: str,
trn_data,
dev_data,
save_dir,
transformer: str,
batch_size=None,
temperature_scheduler='flsw',
epochs=None,
devices=None,
logger=None,
seed=None,
**kwargs):
return super().distill(**merge_locals_kwargs(locals(), kwargs))
def fit(self,
trn_data,
dev_data,
save_dir,
feat=None,
n_embed=100,
pretrained_embed=None,
transformer=None,
average_subwords=False,
word_dropout: float = 0.2,
transformer_hidden_dropout=None,
layer_dropout=0,
scalar_mix: int = None,
embed_dropout=.33,
n_lstm_hidden=400,
n_lstm_layers=3,
hidden_dropout=.33,
n_mlp_arc=500,
n_mlp_rel=100,
mlp_dropout=.33,
lr=2e-3,
transformer_lr=5e-5,
mu=.9,
nu=.9,
epsilon=1e-12,
clip=5.0,
decay=.75,
decay_steps=5000,
patience=100,
batch_size=None,
sampler_builder=None,
lowercase=False,
epochs=50000,
tree=False,
punct=False,
min_freq=2,
apply_constraint=True,
joint=False,
no_cycle=False,
root=None,
logger=None,
verbose=True,
unk=UNK,
pad_rel=None,
max_sequence_length=512,
devices: Union[float, int, List[int]] = None,
transform=None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(self,
trn_data,
dev_data,
save_dir,
embed,
n_mlp_arc=500,
n_mlp_rel=100,
n_mlp_sib=100,
mlp_dropout=.33,
lr=2e-3,
transformer_lr=5e-5,
mu=.9,
nu=.9,
epsilon=1e-12,
grad_norm=5.0,
decay=.75,
decay_steps=5000,
weight_decay=0,
warmup_steps=0.1,
separate_optimizer=True,
patience=100,
lowercase=False,
epochs=50000,
tree=False,
proj=True,
mbr=True,
partial=False,
punct=False,
min_freq=2,
logger=None,
verbose=True,
unk=UNK,
max_sequence_length=512,
batch_size=None,
sampler_builder=None,
gradient_accumulation=1,
devices: Union[float, int, List[int]] = None,
transform=None,
eval_trn=False,
bos='\0',
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def __init__(self,
trn: str = None,
dev: str = None,
tst: str = None,
sampler_builder: SamplerBuilder = None,
dependencies: str = None,
scalar_mix: ScalarMixWithDropoutBuilder = None,
use_raw_hidden_states=False,
lr=1e-3,
separate_optimizer=False,
cls_is_bos=False,
sep_is_eos=False,
delimiter=None,
max_seq_len=None,
sent_delimiter=None,
char_level=False,
hard_constraint=False,
token_key='token',
**kwargs) -> None:
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.vocabs = VocabDict()
def fit(self,
trn_data,
dev_data,
save_dir,
transformer=None,
mask_prob=0.15,
projection=None,
average_subwords=False,
transformer_hidden_dropout=None,
layer_dropout=0,
mix_embedding: int = None,
embed_dropout=.33,
n_mlp_arc=500,
n_mlp_rel=100,
mlp_dropout=.33,
lr=2e-3,
transformer_lr=5e-5,
mu=.9,
nu=.9,
epsilon=1e-12,
clip=5.0,
decay=.75,
decay_steps=5000,
patience=100,
sampler='kmeans',
n_buckets=32,
batch_max_tokens=5000,
batch_size=None,
epochs=50000,
tree=False,
punct=False,
logger=None,
verbose=True,
max_sequence_length=512,
devices: Union[float, int, List[int]] = None,
transform=None,
**kwargs):
return TorchComponent.fit(self,
**merge_locals_kwargs(locals(), kwargs))
def fit(self,
trn_data,
dev_data,
save_dir,
transformer,
average_subwords=False,
word_dropout: float = 0.2,
hidden_dropout=None,
layer_dropout=0,
scalar_mix=None,
mix_embedding: int = 0,
grad_norm=5.0,
transformer_grad_norm=None,
lr=5e-5,
transformer_lr=None,
transformer_layers=None,
gradient_accumulation=1,
adam_epsilon=1e-6,
weight_decay=0,
warmup_steps=0.1,
secondary_encoder=None,
crf=False,
reduction='sum',
batch_size=32,
sampler_builder: SamplerBuilder = None,
epochs=3,
patience=5,
token_key=None,
delimiter=None,
max_seq_len=None,
sent_delimiter=None,
char_level=False,
hard_constraint=False,
transform=None,
logger=None,
devices: Union[float, int, List[int]] = None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(self,
trn_data,
dev_data,
save_dir,
encoder,
batch_size=None,
batch_max_tokens=17776,
epochs=1000,
gradient_accumulation=4,
char2concept_dim=128,
cnn_filters=((3, 256), ),
concept_char_dim=32,
concept_dim=300,
dropout=0.2,
embed_dim=512,
eval_every=20,
ff_embed_dim=1024,
graph_layers=2,
inference_layers=4,
num_heads=8,
rel_dim=100,
snt_layers=4,
unk_rate=0.33,
warmup_steps=0.1,
lr=1e-3,
transformer_lr=1e-4,
adam_epsilon=1e-6,
weight_decay=0,
grad_norm=1.0,
shuffle_sibling_steps=0.9,
vocab_min_freq=5,
amr_version='2.0',
devices=None,
logger=None,
seed=None,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def __init__(self,
trn: str = None,
dev: str = None,
tst: str = None,
sampler_builder: SamplerBuilder = None,
dependencies: str = None,
scalar_mix: ScalarMixWithDropoutBuilder = None,
use_raw_hidden_states=False,
lr=1e-3,
separate_optimizer=False,
cls_is_bos=True,
sep_is_eos=False,
char2concept_dim=128,
cnn_filters=((3, 256), ),
concept_char_dim=32,
concept_dim=300,
dropout=0.2,
embed_dim=512,
eval_every=20,
ff_embed_dim=1024,
graph_layers=2,
inference_layers=4,
num_heads=8,
rel_dim=100,
snt_layers=4,
unk_rate=0.33,
vocab_min_freq=5,
beam_size=8,
alpha=0.6,
max_time_step=100,
amr_version='2.0',
**kwargs) -> None:
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.vocabs = VocabDict()
utils_dir = get_resource(get_amr_utils(amr_version))
self.sense_restore = NodeRestore(NodeUtilities.from_json(utils_dir))
def fit(self,
trn_data,
dev_data,
save_dir,
batch_size=32,
epochs=30,
transformer='facebook/bart-base',
lr=5e-05,
grad_norm=2.5,
weight_decay=0.004,
warmup_steps=1,
dropout=0.25,
attention_dropout=0.0,
pred_min=5,
eval_after=0.5,
collapse_name_ops=False,
use_pointer_tokens=True,
raw_graph=False,
gradient_accumulation=1,
recategorization_tokens=('PERSON', 'COUNTRY', 'QUANTITY',
'ORGANIZATION', 'DATE_ATTRS',
'NATIONALITY', 'LOCATION', 'ENTITY',
'CITY', 'MISC', 'ORDINAL_ENTITY',
'IDEOLOGY', 'RELIGION',
'STATE_OR_PROVINCE', 'URL',
'CAUSE_OF_DEATH', 'O', 'TITLE', 'DATE',
'NUMBER', 'HANDLE', 'SCORE_ENTITY',
'DURATION', 'ORDINAL', 'MONEY', 'SET',
'CRIMINAL_CHARGE', '_1', '_2', '_3', '_4',
'_2', '_5', '_6', '_7', '_8', '_9', '_10',
'_11', '_12', '_13', '_14', '_15'),
additional_tokens=(
'date-entity', 'government-organization', 'temporal-quantity',
'amr-unknown', 'multi-sentence', 'political-party',
'monetary-quantity', 'ordinal-entity', 'religious-group',
'percentage-entity', 'world-region', 'url-entity',
'political-movement', 'et-cetera', 'at-least', 'mass-quantity',
'have-org-role-91', 'have-rel-role-91', 'include-91',
'have-concession-91', 'have-condition-91', 'be-located-at-91',
'rate-entity-91', 'instead-of-91', 'hyperlink-91',
'request-confirmation-91', 'have-purpose-91',
'be-temporally-at-91', 'regardless-91', 'have-polarity-91',
'byline-91', 'have-manner-91', 'have-part-91', 'have-quant-91',
'publication-91', 'be-from-91', 'have-mod-91',
'have-frequency-91', 'score-on-scale-91', 'have-li-91',
'be-compared-to-91', 'be-destined-for-91', 'course-91',
'have-subevent-91', 'street-address-91', 'have-extent-91',
'statistical-test-91', 'have-instrument-91', 'have-name-91',
'be-polite-91', '-00', '-01', '-02', '-03', '-04', '-05',
'-06', '-07', '-08', '-09', '-10', '-11', '-12', '-13', '-14',
'-15', '-16', '-17', '-18', '-19', '-20', '-21', '-22', '-23',
'-24', '-25', '-26', '-27', '-28', '-29', '-20', '-31', '-32',
'-33', '-34', '-35', '-36', '-37', '-38', '-39', '-40', '-41',
'-42', '-43', '-44', '-45', '-46', '-47', '-48', '-49', '-50',
'-51', '-52', '-53', '-54', '-55', '-56', '-57', '-58', '-59',
'-60', '-61', '-62', '-63', '-64', '-65', '-66', '-67', '-68',
'-69', '-70', '-71', '-72', '-73', '-74', '-75', '-76', '-77',
'-78', '-79', '-80', '-81', '-82', '-83', '-84', '-85', '-86',
'-87', '-88', '-89', '-90', '-91', '-92', '-93', '-94', '-95',
'-96', '-97', '-98', '-of'),
devices=None,
logger=None,
seed=None,
finetune: Union[bool, str] = False,
eval_trn=True,
_device_placeholder=False,
**kwargs):
"""
Args:
trn_data:
dev_data:
save_dir:
batch_size:
epochs:
transformer:
lr:
grad_norm:
weight_decay:
warmup_steps:
dropout:
attention_dropout:
pred_min:
eval_after:
collapse_name_ops: ``True`` to merge name ops.
use_pointer_tokens: ``True`` to use pointer tokens to represent variables.
raw_graph: ``True`` to use the raw graph as input and skip all pre/post-processing steps.
gradient_accumulation:
recategorization_tokens: Tokens used in re-categorization. They will be added to tokenizer too but do not
put them into ``additional_tokens``.
additional_tokens: Tokens to be added to the tokenizer vocab.
devices:
logger:
seed:
finetune:
eval_trn:
_device_placeholder:
**kwargs:
Returns:
"""
return super().fit(**merge_locals_kwargs(locals(), kwargs))
