from transformers import PretrainedConfig class DebertaV2Config(PretrainedConfig): def __init__( self, vocab_size=128100, hidden_size=1536, sep_token_id=2, mask_token_id=128000, num_hidden_layers=24, num_attention_heads=24, intermediate_size=6144, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=0, initializer_range=0.02, layer_norm_eps=1e-7, relative_attention=False, max_relative_positions=-1, pad_token_id=0, position_biased_input=True, pos_att_type=None, pooler_dropout=0, pooler_hidden_act="gelu", **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.mask_token_id = mask_token_id self.sep_token_id = sep_token_id self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.relative_attention = relative_attention self.max_relative_positions = max_relative_positions self.pad_token_id = pad_token_id self.position_biased_input = position_biased_input # Backwards compatibility if isinstance(pos_att_type, str): pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")] self.pos_att_type = pos_att_type self.vocab_size = vocab_size self.layer_norm_eps = layer_norm_eps self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size) self.pooler_dropout = pooler_dropout self.pooler_hidden_act = pooler_hidden_act