Delete AbLang_bert_model.py

AbLang_bert_model.py

@@ -1,34 +0,0 @@
-from transformers.models.bert.modeling_bert import BertEncoder, BertPooler, BertEmbeddings, BertForMaskedLM, MaskedLMOutput
-from transformers import BertModel
-from typing import List, Optional, Tuple, Union
-import torch
-
-class BertEmbeddingsV2(BertEmbeddings):
-    def __init__(self, config):
-        super().__init__(config)
-        self.pad_token_id = config.pad_token_id
-        self.position_embeddings = torch.nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=0) # here padding_idx is always 0
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        past_key_values_length: int = 0,
-    ) -> torch.Tensor:
-        inputs_embeds = self.word_embeddings(input_ids)
-        position_ids = self.create_position_ids_from_input_ids(input_ids)  
-        position_embeddings = self.position_embeddings(position_ids)
-        embeddings = inputs_embeds + position_embeddings
-        return self.dropout(self.LayerNorm(embeddings))
-    
-    def create_position_ids_from_input_ids(self, input_ids: torch.LongTensor) -> torch.Tensor:
-        mask = input_ids.ne(self.pad_token_id).int()
-        return torch.cumsum(mask, dim=1).long() * mask
-
-
-class BertModelV2(BertModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.embeddings = BertEmbeddingsV2(config)