diff --git a/bert_no_ernie.py b/bert_no_ernie.py
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+++ b/bert_no_ernie.py
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+# PyTorch Imports
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+# scikit-learn Imports
+from sklearn.metrics import accuracy_score, confusion_matrix
+from sklearn.model_selection import train_test_split
+#Gensim Imports
+import gensim
+# Bert improts
+from transformers import BertForSequenceClassification, BertTokenizer, BertPreTrainedModel, AdamW
+#Default imports (pandas, numpy, matplotlib, etc.)
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+## Select Device
+if torch.cuda.is_available():
+   DEVICE = torch.device("cuda")
+else:
+    DEVICE = torch.device("cpu")
+
+
+class SimpleHumorDataset(Dataset):
+    def __init__(self,tokenizer,dataframe,max_length=256):
+        super().__init__()
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+        self.text = dataframe['text'].tolist()
+        self.labels = dataframe['is_humor'].tolist()
+    def __getitem__(self,idx):
+        text = self.text    
+        labels = self.labels
+        encoding = self.tokenizer.encode_plus(
+            text,
+            add_special_tokens=True,
+            padding="max_length",
+            trunction = True,
+            return_attention_mask = True,
+            return_token_type_ids = False,
+            max_length=self.max_length,
+            truncation = True,
+            return_tensors = 'pt'
+            )     
+        input_ids = encoding['input_ids'].flatten()
+        attention_mask = encoding['attention_mask'].flatten()
+
+        return {
+            'input_ids': torch.as_tensor(input_ids,dtype=torch.long),
+            'attention_mask':torch.as_tensor(attention_mask,dtype=torch.long),
+            'labels':torch.as_tensor(self.labels,dtype=torch.long),
+            'text':text
+        }
+    
+    def __len__(self):
+        return len(self.labels)
+    
+
+class BERT(nn.Module):
+    def __init__(self,dropout,num_layers=2):
+        super().__init__()
+        self.model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased"),
+        self.ln1 = nn.Linear(768,num_layers),
+        self.dropout = nn.Dropout(dropout),
+        self.sm1 = nn.Sigmoid()
+         
+    def forward(self,input_ids,attention_mask):
+        return self.sm1(self.dropout(self.ln1(self.model[0](input_ids,attention_mask))))
+        # return self.model(input_ids)
+    
+    def train(self,criterion,optimizer,train_loader):
+        self.model[0].train()
+        total_loss = 0
+        for train_batch in train_loader:
+            optimizer.zero_grad()
+            input_ids, att_mask, labels,_ = train_batch
+            outputs = self.forward(input_ids,att_mask)
+            loss = criterion(outputs,labels)
+            loss.backward()
+            optimizer.step()
+            total_loss+=loss.item()
+        print(f"Total Loss is {(total_loss/len(train_loader)):.4f}")            
+
+    def eval(model,criterion,validation_loader):
+        model.model[0].eval()
+        total_loss = 0.0
+        total_acc = 0.0
+        with torch.no_grad():
+            for val_batch in validation_loader:
+                input_ids, att_mask ,labels,_ = val_batch
+                input_ids, labels = input_ids.to(DEVICE),att_mask.to(DEVICE), labels.to(DEVICE)
+                outputs = model(input_ids,att_mask)
+                loss = criterion(outputs,labels)
+                total_loss += loss.item()
+                predictions = torch.argmax(outputs,dim=1)
+                total_acc += (predictions == labels).sum().item()
+        print(f"Total Loss: {total_loss/len(validation_loader)} ### Test Accuracy {total_acc/len(validation_loader)*100}%")
+
+def generate_tokens(tokenizer,raw_data):
+    return tokenizer.encode_plus(
+            raw_data,
+            add_special_tokens=True,
+            padding="max_length",
+            return_attention_mask = True,
+            return_token_type_ids = False,
+            max_length=128,
+            truncation = True,
+            return_tensors = 'pt'
+            )
+
+if __name__ == "__main__":
+    
+    # Initialize Bert Model with dropout probability and Num End Layers
+    bert = BERT(0.1)
+    
+    # Set Max Epoch Amount
+    EPOCH = 50
+   
+    # Read Raw Data from csv and save as DataFrame
+    df = pd.read_csv("./data/hack.csv",encoding="latin1")
+
+    # Initialize BertTokenizer from Pretrained 
+    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased",do_lower_case=True)
+
+    #Split DataFrame into Train and Test Sets
+    train,test = train_test_split(df,random_state=501,test_size=.2)
+
+    # Create Custom Datasets for Train and Test
+    train_data = SimpleHumorDataset(tokenizer,train)
+    test_data = SimpleHumorDataset(tokenizer,test)
+   
+   # Initialize Dataloader with Train and Test Sets
+    train_loader = DataLoader(dataset=train_data,batch_size=16,shuffle=True)
+    test_loader = DataLoader(dataset=test_data,batch_size=len(test_data))
+
+    # Set criterion to BCELoss (Binary Cross Entropy) and define Adam Optimizer with model parameters and learning rate
+    criterion_bce = nn.BCELoss()
+    optimizer_adam = optim.Adam(bert.model[0].parameters(), lr = 1e-5)
+    
+    for epoch in range(EPOCH):
+        print(f"For {epoch} the Scores are: ")
+        bert.train(optimizer=optimizer_adam,criterion=criterion_bce,train_loader=train_loader)
+        bert.eval(criterion=criterion_bce,validation_loader=test_loader)
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