second model version

models.py

@@ -1,50 +0,0 @@
-import torch
-from transformers import BertTokenizer
-from torch import nn
-from transformers import BertModel
-
-
-#CustomMBERTModel= torch.load("/data2/Akash_for_interface/model_mbert_1416.pt")
-tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
-sentences= "you are good  person."
-max_len=len(sentences)
-encoding = tokenizer.encode_plus(sentences,add_special_tokens=True,max_length=max_len,padding='max_length',truncation=True,return_tensors='pt')
-input_ids=encoding['input_ids'].flatten(),
-attention_mask= encoding['attention_mask'].flatten()
-#print(input_ids[0])
-
-labels=["Non Hateful","Hateful"]
-
-
-
-device = 'cpu'
-class CustomMBERTModel(nn.Module):
-    def __init__(self, num_labels):
-        super(CustomMBERTModel, self).__init__()
-        self.bert = BertModel.from_pretrained('bert-base-multilingual-cased')
-        
-        # Freeze all layers except the top 2
-        for param in self.bert.parameters():
-            param.requires_grad = False
-
-        # Unfreeze the parameters of the top 2 layers
-        for param in self.bert.encoder.layer[-2:].parameters():
-            param.requires_grad = True
-
-        # Adding Linear layer
-        self.linear = nn.Linear(self.bert.config.hidden_size, num_labels)
-
-    def forward(self, input_ids, attention_mask):
-        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
-        hidden_states = outputs.last_hidden_state
-
-        pooled_output= hidden_states[:,0,:]
-        pooled_output=torch.squeeze(pooled_output,dim=1)
-
-        #print('p-shape:', pooled_output.shape)
-
-        logits = self.linear(pooled_output)
-
-        #print('l-shape:', logits.shape)
-        return logits
-