class_docstr: Load data from disk
      
code: def _extract_sentence(self, sentence):
      rows = sentence.split("\n")
      rows = [_.split(" ") for _ in rows]
      tokens = [_[0] for _ in rows]
      labels = [_[1] for _ in rows]
      labels_encoded = self.label_encoder.encode(labels)
      return {"tokens": tokens, "labels": labels_encoded}
  
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 2 for calling the TokenClassificationCorpus obj's underlying member method code with example usage: obj._extract_sentence(sentence) and returns: dict