| 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", | |
| } | |