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import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
from transformers import AutoTokenizer, BertModel
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np
import pandas as pd
from news_dataset import NewsDataset
class CustomBert(nn.Module):
def __init__(self, model_name_or_path="bert-base-uncased", n_classes=2):
super(CustomBert, self).__init__()
self.bert_pretrained = BertModel.from_pretrained(model_name_or_path)
self.classifier = nn.Linear(self.bert_pretrained.config.hidden_size, n_classes)
def forward(self, input_ids, attention_mask):
x = self.bert_pretrained(input_ids=input_ids, attention_mask=attention_mask)
x = self.classifier(x.pooler_output)
return x
#Training function
def training_step(model, data_loader, loss_fn, optimizer):
model.train()
total_loss = 0
for data in tqdm(data_loader, total=len(data_loader)):
input_ids = data['input_ids']
attention_mask = data['attention_mask']
labels = data['labels']
output = model(input_ids=input_ids, attention_mask=attention_mask)
loss = loss_fn(output, labels)
loss.backward()
optimizer.step()
optimizer.zero_grad()
total_loss += loss.item()
return total_loss / len(data_loader.dataset)
#Evaluation
def evaluation(model, test_dataloader, loss_fn):
model.eval()
correct_predictions = 0
losses = []
for data in tqdm(test_dataloader, total=len(test_dataloader)):
input_ids = data['input_ids']
attention_mask = data['attention_mask']
labels = data['labels']
output = model(input_ids=input_ids, attention_mask=attention_mask)
_, pred = output.max(1)
correct_predictions += torch.sum(pred == labels)
loss = loss_fn(output, labels)
losses.append(loss.item())
return correct_predictions.double() / len(test_dataloader.dataset), np.mean(losses)
#main
if __name__ == "__main__":
dataset = NewsDataset(csv_file="./inshort_news_data.csv", max_length=100)
num_classes = len(dataset.labels_dict)
train_data, test_data = train_test_split(dataset, test_size=0.2)
train_dataloader = DataLoader(train_data, batch_size=8, shuffle=True)
test_dataloader = DataLoader(test_data, batch_size=8, shuffle=False)
model = CustomBert(n_classes=num_classes)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=2e-5)
num_epochs = 3
for epoch in range(num_epochs):
print(f"Epoch {epoch + 1}/{num_epochs}")
train_loss = training_step(model, train_dataloader, loss_fn, optimizer)
print(f"Train Loss: {train_loss:.4f}")
val_acc, val_loss = evaluation(model, test_dataloader, loss_fn)
print(f"Validation Accuracy: {val_acc:.4f}, Validation Loss: {val_loss:.4f}")
# Save the model
import os
os.makedirs('./models', exist_ok=True)
torch.save(model.state_dict(), './models/trained_model1.pth')
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