| from transformers import PretrainedConfig |
|
|
| class ProSSTConfig(PretrainedConfig): |
| model_type = "ProSST" |
|
|
| def __init__( |
| self, |
| token_dropout=True, |
| mlm_probability=0.15, |
| vocab_size=1024, |
| type_vocab_size=0, |
| ss_vocab_size=0, |
| hidden_size=768, |
| num_hidden_layers=12, |
| num_attention_heads=12, |
| intermediate_size=3072, |
| hidden_act="gelu", |
| hidden_dropout_prob=0.1, |
| attention_probs_dropout_prob=0.1, |
| mask_token_id=24, |
| initializer_range=0.02, |
| layer_norm_eps=1e-7, |
| pad_token_id=0, |
| position_biased_input=False, |
| pooler_dropout=0, |
| pooler_hidden_act="gelu", |
| pos_att_type=None, |
| position_embedding_type="relative", |
| max_position_embeddings=1024, |
| max_relative_positions=-1, |
| relative_attention=False, |
| pooling_head="mean", |
| scale_hidden=1, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
| self.token_dropout = token_dropout |
| self.mlm_probability = mlm_probability |
| self.hidden_size = hidden_size |
| 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.ss_vocab_size = ss_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 |
| self.mask_token_id = mask_token_id |
| self.position_embedding_type = position_embedding_type |
| self.pooling_head = pooling_head |
| self.scale_hidden = scale_hidden |
|
|
| |
| if type(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 |
| |
| ProSSTConfig.register_for_auto_class() |
