File size: 1,804 Bytes
29b14b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | from transformers import PretrainedConfig
class GPTBertConfig(PretrainedConfig):
model_type = "gpt_bert_mntp"
def __init__(
self,
vocab_size=16000,
hidden_size=720,
intermediate_size=2048,
num_hidden_layers=12,
num_attention_heads=12,
max_position_embeddings=512,
position_bucket_size=32,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
layer_norm_eps=1e-5,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
unk_token_id=3,
mask_token_id=4,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.position_bucket_size = position_bucket_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.layer_norm_eps = layer_norm_eps
self.unk_token_id = unk_token_id
self.mask_token_id = mask_token_id
self.tie_word_embeddings = True
self.architectures = ["GPTBertForMaskedLM"]
self.auto_map = {
"AutoConfig": "configuration_gpt_bert.GPTBertConfig",
"AutoModel": "modeling_gpt_bert.GPTBertModel",
"AutoModelForMaskedLM": "modeling_gpt_bert.GPTBertForMaskedLM",
"AutoModelForCausalLM": "modeling_gpt_bert.GPTBertForCausalLM",
}
|