| from transformers import PretrainedConfig |
|
|
|
|
| class QiDeBERTaConfig(PretrainedConfig): |
| model_type = "QiDeBERTa" |
| attribute_map = { |
| "hidden_size": "d_model", |
| "num_attention_heads": "num_heads", |
| "num_hidden_layers": "num_layers", |
| "intermediate_size": "d_ff", |
| } |
|
|
| def __init__( |
| self, |
| vocab_size=25500, |
| d_model=1024, |
| num_layers=24, |
| num_heads=16, |
| d_ff=4096, |
| hidden_dropout_prob=0.1, |
| attention_probs_dropout_prob=0.1, |
| max_position_embeddings=512, |
| initializer_range=0.02, |
| layer_norm_eps=1e-7, |
| relative_attention=True, |
| max_relative_positions=-1, |
| classifier_num_labels=-1, |
| unk_token_id=0, |
| bos_token_id=1, |
| eos_token_id=2, |
| pad_token_id=3, |
| mask_token_id=4, |
| position_biased_input=False, |
| position_buckets=256, |
| pos_att_type="p2c|c2p", |
| share_att_key=True, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
|
|
| self.d_model = d_model |
| self.num_layers = num_layers |
| self.num_heads = num_heads |
| self.d_ff = d_ff |
| self.hidden_dropout_prob = hidden_dropout_prob |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob |
| self.max_position_embeddings = max_position_embeddings |
| self.initializer_range = initializer_range |
| self.relative_attention = relative_attention |
| self.max_relative_positions = max_relative_positions |
| self.classifier_num_labels = classifier_num_labels |
| self.unk_token_id = unk_token_id |
| self.bos_token_id = bos_token_id |
| self.eos_token_id = eos_token_id |
| self.pad_token_id = pad_token_id |
| self.mask_token_id = mask_token_id |
| self.position_biased_input = position_biased_input |
| self.share_att_key = share_att_key |
| self.position_buckets = position_buckets |
|
|
| |
| 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", d_model) |
|
|
