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from typing import Optional
from transformers import PreTrainedModel, PretrainedConfig, DistilBertModel, BertModel
import torch
from torch import nn
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class TransformerBasedModelDistilBert(nn.Module):
def __init__(self):
super(TransformerBasedModelDistilBert, self).__init__()
self.bert = DistilBertModel.from_pretrained('distilbert-base-uncased')
self.dropout = nn.Dropout(0.55)
self.fc = nn.Linear(768, 2)
def forward(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None):
input_shape = input_ids.size()
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
pooled_output = outputs.last_hidden_state[:, 0, :]
pooled_output = self.dropout(pooled_output)
logits = self.fc(pooled_output)
return logits
class TransformerBasedModelBert(nn.Module):
def __init__(self):
super(TransformerBasedModelBert, self).__init__()
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.dropout = nn.Dropout(0.55)
self.fc = nn.Linear(768, 2)
def forward(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None):
input_shape = input_ids.size()
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.fc(pooled_output)
return logits
class MyConfigDistil(PretrainedConfig):
model_type = "distilbert"
def __init__(self, final_dropout=0.55, **kwargs):
super().__init__(**kwargs)
self.final_dropout = final_dropout
class MyConfig(PretrainedConfig):
model_type = "bert"
def __init__(self, final_dropout=0.55, **kwargs):
super().__init__(**kwargs)
self.final_dropout = final_dropout
class MyHFModel_DistilBertBased(PreTrainedModel):
config_class = MyConfigDistil
def __init__(self, config):
super().__init__(config)
self.config = config
self.model = TransformerBasedModelDistilBert()
def forward(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None):
input_shape = input_ids.size()
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
return self.model(input_ids=input_ids, attention_mask=attention_mask)
class MyHFModel_BertBased(PreTrainedModel):
config_class = MyConfig
def __init__(self, config):
super().__init__(config)
self.config = config
self.model = TransformerBasedModelBert()
def forward(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None):
input_shape = input_ids.size()
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
return self.model(input_ids=input_ids, attention_mask=attention_mask)
config = MyConfigDistil(0.55)
HF_DistilBertBasedModelAppDocs = MyHFModel_DistilBertBased(config)
config_db = MyConfig(0.55)
HF_BertBasedModelAppDocs = MyHFModel_BertBased(config_db)
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