|
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() |
