class_docstr: Load data from disk
      
code: def __init__(self, corpus_folder):
      train_file = join(corpus_folder, "train.txt")
      val_file = join(corpus_folder, "dev.txt")
      test_file = join(corpus_folder, "test.txt")
      self.label_encoder = LabelEncoder()
      self.train = self._extract_sentences(train_file)
      self.val = self._extract_sentences(val_file)
      self.test = self._extract_sentences(test_file)
      self.num_labels = self.label_encoder.vocab_size
  
imports: import logging
  from os.path import join
  import pytorch_lightning
  from pytorch_lightning.loggers import WandbLogger
  from torch.optim import AdamW
  import torch.nn
  import torch
  from torchmetrics import F1
  from torch.utils.data import DataLoader, Dataset
  from transformers import AutoTokenizer, AutoModel
label_desc: Use image node_id 1 to create a new TokenClassificationCorpus object  with example: obj = TokenClassificationCorpus(corpus_folder)