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# Libraries

import matplotlib.pyplot as plt
import pandas as pd
import torch

# Preliminaries

from torchtext.data import Field, TabularDataset, BucketIterator, Iterator

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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Model parameter
MAX_SEQ_LEN = 128
PAD_INDEX = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
UNK_INDEX = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)

# Fields

label_field = Field(sequential=False, use_vocab=False, batch_first=True, dtype=torch.float)

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class BERT(nn.Module):

    def __init__(self):
        super(BERT, self).__init__()

        options_name = "bert-base-uncased"
        self.encoder = BertForSequenceClassification.from_pretrained(options_name)

    def forward(self, text, label):
        loss, text_fea = self.encoder(text, labels=label)[:2]

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# Save and Load Functions

def save_checkpoint(save_path, model, valid_loss):

    if save_path == None:
        return
    
    state_dict = {'model_state_dict': model.state_dict(),
                  'valid_loss': valid_loss}
    

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# Training Function

def train(model,
          optimizer,
          criterion = nn.BCELoss(),
          train_loader = train_iter,
          valid_loader = valid_iter,
          num_epochs = 5,
          eval_every = len(train_iter) // 2,
          file_path = destination_folder,

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# Evaluation Function

def evaluate(model, test_loader):
    y_pred = []
    y_true = []

    model.eval()
    with torch.no_grad():
        for (labels, title, text, titletext), _ in test_loader:

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train_loss_list, valid_loss_list, global_steps_list = load_metrics(destination_folder + '/metrics.pt')
plt.plot(global_steps_list, train_loss_list, label='Train')
plt.plot(global_steps_list, valid_loss_list, label='Valid')
plt.xlabel('Global Steps')
plt.ylabel('Loss')
plt.legend()
plt.show() 

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Library"
   ]
  },
  {

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