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))
def fit(self, trn_data: Any, dev_data: Any, save_dir: str, word_embed: Union[str, int, dict] = 200, ngram_embed: Union[str, int, dict] = 50, embedding_trainable=True, window_size=4, kernel_size=3, filters=(200, 200, 200, 200, 200), dropout_embed=0.2, dropout_hidden=0.2, weight_norm=True, loss: Union[tf.keras.losses.Loss, str] = None, optimizer: Union[str, tf.keras.optimizers.Optimizer] = 'adam', metrics='f1', batch_size=100, epochs=100, logger=None, verbose=True, **kwargs): return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(self, trn_data: Any, dev_data: Any, save_dir: str, word_embed: Union[str, int, dict] = 200, ngram_embed: Union[str, int, dict] = 50, embedding_trainable=True, window_size=4, kernel_size=3, filters=(200, 200, 200, 200, 200), dropout_embed=0.2, dropout_hidden=0.2, weight_norm=True, loss: Union[tf.keras.losses.Loss, str] = None, optimizer: Union[str, tf.keras.optimizers.Optimizer] = 'adam', metrics='accuracy', batch_size=100, epochs=100, logger=None, verbose=True, **kwargs): assert kwargs.get('run_eagerly', True), 'NgramConvTaggingModel can only run eagerly' kwargs['run_eagerly'] = True return super().fit(**merge_locals_kwargs(locals(), kwargs))
def __init__(self, config: SerializableDict = None, map_x=True, map_y=True, lower=True, n_buckets=32, n_tokens_per_batch=5000, min_freq=2, **kwargs) -> None: super().__init__(**merge_locals_kwargs(locals(), kwargs)) self.form_vocab: Vocab = None self.cpos_vocab: Vocab = None self.rel_vocab: Vocab = None self.puncts: tf.Tensor = None
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[Vocab] = None self.tag_vocab: Optional[Vocab] = None self.char_vocab: Optional[Vocab] = None
def fit(self, trn_data: Any, dev_data: Any, save_dir: str, transformer: str, max_length: int = 128, optimizer='adamw', warmup_steps_ratio=0.1, use_amp=False, batch_size=32, epochs=3, logger=None, verbose=1, **kwargs): return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(self, trn_data, dev_data, save_dir, transformer, optimizer='adamw', learning_rate=5e-5, weight_decay_rate=0, epsilon=1e-8, clipnorm=1.0, warmup_steps_ratio=0, use_amp=False, max_seq_length=128, batch_size=32, epochs=3, metrics='accuracy', run_eagerly=False, logger=None, verbose=True, **kwargs): return super().fit(**merge_locals_kwargs(locals(), kwargs))
def fit(self, trn_data: str, dev_data: str = None, save_dir: str = None, embeddings=100, embedding_trainable=False, rnn_input_dropout=0.2, rnn_units=100, rnn_output_dropout=0.2, epochs=20, lower=False, logger=None, loss: Union[tf.keras.losses.Loss, str] = None, optimizer: Union[str, tf.keras.optimizers.Optimizer] = 'adam', metrics='f1', batch_size=32, dev_batch_size=32, lr_decay_per_epoch=None, verbose=True, **kwargs): return super().fit(**merge_locals_kwargs(locals(), kwargs))