| from transformers import PretrainedConfig | |
| from typing import List, Literal, Optional | |
| _SAMPLING_TYPE = Literal['fp32_gumbel', 'fp16_gumbel', 'multinomial'] | |
| class CharmenElectraConfig(PretrainedConfig): | |
| model_type = "SzegedAI/charmen-electra" | |
| _name_or_path = "SzegedAI/charmen-electra" | |
| architectures = [ | |
| "CharmenElectraModel" | |
| ] | |
| def __init__( | |
| self, | |
| downsampling_factor: int = 4, | |
| max_block_size: int = 4, | |
| score_consensus_attn: bool = True, | |
| upsample_output: bool = True, | |
| sampling: _SAMPLING_TYPE = 'fp32_gumbel', | |
| attention_probs_dropout_prob: float = 0.1, | |
| embedding_size: int = 768, | |
| hidden_act: str = "gelu", | |
| hidden_dropout_prob: float = 0.1, | |
| hidden_size: int = 512, | |
| initializer_range: float = 0.02, | |
| intermediate_size: int = 2048, | |
| layer_norm_eps: float = 1e-12, | |
| max_position_embeddings: int = 1024, | |
| model_type: str = "electra", | |
| num_attention_heads: int = 8, | |
| num_hidden_layers: int = 12, | |
| pad_token_id: int = 0, | |
| position_embedding_type: str = "absolute", | |
| summary_activation: str = "gelu", | |
| summary_last_dropout: float = 0.1, | |
| summary_type: str = "first", | |
| summary_use_proj: bool = True, | |
| type_vocab_size: int = 2, | |
| vocab_size: int = 261, | |
| classifier_dropout: Optional[float] = None, | |
| **kwargs, | |
| ): | |
| self.downsampling_factor = downsampling_factor | |
| self.max_block_size = max_block_size | |
| self.score_consensus_attn = score_consensus_attn | |
| self.upsample_output = upsample_output | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.embedding_size = embedding_size | |
| self.hidden_act = hidden_act | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.hidden_size = hidden_size | |
| self.initializer_range = initializer_range | |
| self.intermediate_size = intermediate_size | |
| self.layer_norm_eps = layer_norm_eps | |
| self.max_position_embeddings = max_position_embeddings | |
| self.model_type = model_type | |
| self.num_attention_heads = num_attention_heads | |
| self.num_hidden_layers = num_hidden_layers | |
| self.pad_token_id = pad_token_id | |
| self.position_embedding_type = position_embedding_type | |
| self.summary_activation = summary_activation | |
| self.summary_last_dropout = summary_last_dropout | |
| self.summary_type = summary_type | |
| self.summary_use_proj = summary_use_proj | |
| self.type_vocab_size = type_vocab_size | |
| self.vocab_size = vocab_size | |
| self.sampling = sampling | |
| self.classifier_dropout = classifier_dropout | |
| super().__init__(**kwargs) | |