| | 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 |
| |
|
| | |
| | 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 |
| |
|
