def fit(self,
trn_data,
dev_data,
save_dir,
transformer,
max_seq_length=256,
transformer_dropout=.33,
d_positional=None,
n_mlp_arc=500,
n_mlp_rel=100,
mlp_dropout=.33,
optimizer='adamw',
learning_rate=5e-5,
learning_rate_transformer=None,
weight_decay_rate=0,
epsilon=1e-8,
clipnorm=None,
fp16=False,
warmup_steps_ratio=0,
arc_loss='binary_crossentropy',
rel_loss='sparse_categorical_crossentropy',
metrics=('UF', 'LF'),
batch_size=3000,
samples_per_batch=150,
max_samples_per_batch=None,
epochs=100,
tree=False,
punct=False,
token_mapping=None,
enhanced_only=False,
run_eagerly=False,
logger=None,
verbose=True,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))
def __init__(self,
config: SerializableDict = None,
map_x=True,
map_y=True,
use_char=False,
**kwargs) -> None:
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.word_vocab: Optional[VocabTF] = None
self.tag_vocab: Optional[VocabTF] = None
self.char_vocab: Optional[VocabTF] = None
def __init__(self,
config: SerializableDict = None,
map_x=True,
map_y=True,
lower=False,
**kwargs) -> None:
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.token_vocab = VocabTF()
self.pos_vocab = VocabTF(pad_token=None, unk_token=None)
self.ner_vocab = VocabTF(pad_token=None)
self.deprel_vocab = VocabTF(pad_token=None, unk_token=None)
self.rel_vocab = VocabTF(pad_token=None, unk_token=None)
def fit(self,
trn_data,
dev_data,
save_dir,
n_embed=100,
pretrained_embed=None,
embed_dropout=.33,
n_lstm_hidden=400,
n_lstm_layers=3,
lstm_dropout=.33,
n_mlp_arc=500,
n_mlp_rel=100,
mlp_dropout=.33,
optimizer='adam',
lr=2e-3,
mu=.9,
nu=.9,
epsilon=1e-12,
clip=5.0,
decay=.75,
decay_steps=5000,
patience=100,
arc_loss='sparse_categorical_crossentropy',
rel_loss='sparse_categorical_crossentropy',
metrics=('UAS', 'LAS'),
n_buckets=32,
batch_size=5000,
epochs=50000,
early_stopping_patience=100,
tree=False,
punct=False,
min_freq=2,
run_eagerly=False,
logger=None,
verbose=True,
**kwargs):
return super().fit(**merge_locals_kwargs(locals(), kwargs))