Delete toxicity_model.py

toxicity_model.py

@@ -1,47 +0,0 @@
-from transformers import BertPreTrainedModel, BertModel
-from .toxicity_model_config import ToxicityModelConfig
-import torch
-
-class ToxicityModel(BertPreTrainedModel):
-    """
-    ToxicityModel class for PyTorch
-
-    Args:
-        config (transformers.configuration): model configuration
-
-    Returns:    
-        output (torch.tensor): tensor containing the output logits [-1,1]
-    """
-    config_class = ToxicityModelConfig
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.bert = BertModel(config)
-        
-        self.cls_layer1 = torch.nn.Linear(config.hidden_size,config.linear_layer)
-        self.relu1 = torch.nn.ReLU()
-        self.ff1 = torch.nn.Linear(config.linear_layer,config.linear_layer)
-        self.tanh1 = torch.nn.Tanh()
-        self.ff2 = torch.nn.Linear(config.linear_layer,config.linear_layer_output)
-
-    def forward(self, input_ids, attention_mask, alpha=1, beta=1e-5):
-        
-        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
-        
-        logits = outputs.last_hidden_state[:,0,:]
-        output = self.cls_layer1(logits)
-        output = self.relu1(output)
-        output = self.ff1(output)
-        output = self.tanh1(output)
-        output = self.ff2(output)
-
-        # Apply alpha and beta to output (if not training)  
-        if not self.training:
-            
-            # alpha multiplies the output by a scalar
-            output = torch.mul(output, alpha)
-
-            # beta clamps the output to a minimum value
-            output = torch.clamp(output, min=beta)
-
-        return output