hezar.models.sequence_labeling.roberta.roberta_sequence_labeling_config module

class hezar.models.sequence_labeling.roberta.roberta_sequence_labeling_config.RobertaSequenceLabelingConfig(task: str = <TaskType.SEQUENCE_LABELING: 'sequence_labeling'>, num_labels: int = None, id2label: dict = None, attention_probs_dropout_prob: float = 0.1, bos_token_id: int = 0, eos_token_id: int = 2, gradient_checkpointing: bool = False, hidden_act: str = 'gelu', hidden_dropout_prob: float = 0.1, hidden_size: int = 768, classifier_dropout: float = None, initializer_range: int = 0.02, intermediate_size: int = 3072, layer_norm_eps: float = 1e-12, max_position_embeddings: int = 514, num_attention_heads: int = 12, num_hidden_layers: int = 12, pad_token_id: int = 1, position_embedding_type: str = 'absolute', type_vocab_size: int = 1, use_cache: bool = True, vocab_size: int = 42000, prediction_skip_tokens: Tuple[str] = ('<s>', '</s>, <pad>'))

Bases: ModelConfig

attention_probs_dropout_prob: float = 0.1

bos_token_id: int = 0

classifier_dropout: float = None

eos_token_id: int = 2

gradient_checkpointing: bool = False

hidden_act: str = 'gelu'

hidden_dropout_prob: float = 0.1

hidden_size: int = 768

id2label: dict = None

initializer_range: int = 0.02

intermediate_size: int = 3072

layer_norm_eps: float = 1e-12

max_position_embeddings: int = 514

name: str = 'roberta_sequence_labeling'

num_attention_heads: int = 12

num_hidden_layers: int = 12

num_labels: int = None

pad_token_id: int = 1

position_embedding_type: str = 'absolute'

prediction_skip_tokens: Tuple[str] = ('<s>', '</s>, <pad>')

task: str = 'sequence_labeling'

type_vocab_size: int = 1

use_cache: bool = True

vocab_size: int = 42000