code: def __init__(self, batch_size: int):
      dataset_file = cached_path(
          "https://s3.amazonaws.com/datasets.huggingface.co/trec/"
          "trec-tokenized-bert.bin"
      )
      datasets = torch.load(dataset_file)
      tokenizer = BertTokenizer.from_pretrained(
          "bert-base-cased", do_lower_case=False
      )
  
      for split_name in ["train", "test"]:
          # Trim the samples to the transformer's input length minus 1 & add a classification token
          datasets[split_name] = [
              x[: 256 - 1] + [tokenizer.vocab["[CLS]"]]
              for x in datasets[split_name]
          ]
  
          # Pad the dataset to max length
          padding_length = max(len(x) for x in datasets[split_name])
          datasets[split_name] = [
              np.array(
                  x
                  + [tokenizer.vocab["[PAD]"]]
                  * (padding_length - len(x))
              )
              for x in datasets[split_name]
          ]
  
      valid_size = int(0.1 * len(datasets["train"]))
      c = list(zip(datasets["train"], datasets["train_labels"]))
      random.shuffle(c)
      datasets["train"], datasets["train_labels"] = zip(*c)
      datasets["train"], datasets["train_labels"] = list(
          datasets["train"]
      ), list(datasets["train_labels"])
  
      datasets["valid"], datasets["valid_labels"] = (
          datasets["train"][:valid_size],
          datasets["train_labels"][:valid_size],
      )
      datasets["train"], datasets["train_labels"] = (
          datasets["train"][valid_size:],
          datasets["train_labels"][valid_size:],
      )
  
      train_df = pd.DataFrame(
          data={
              "x": datasets["train"],
              "y_target": datasets["train_labels"],
          }
      )
      val_df = pd.DataFrame(
          data={
              "x": datasets["valid"],
              "y_target": datasets["valid_labels"],
          }
      )
      test_df = pd.DataFrame(
          data={
              "x": datasets["test"],
              "y_target": datasets["test_labels"],
          }
      )
  
      super().__init__(
          train_set=DataFrameDataset(train_df),
          train_batch_size=batch_size,
          val_set=DataFrameDataset(val_df),
          val_batch_size=batch_size,
          test_set=DataFrameDataset(test_df),
          test_batch_size=batch_size,
      )
  
file_path: transfer-nlp/experiments/transfer_learning/transformers/dataset.py
imports: import random
  import numpy
  import pandas
  import torch
  from pytorch_pretrained_bert import cached_path, BertTokenizer
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.plugins.config import register_plugin
label_desc: Use image node_id 1 to create a new BertCLFFinetuningDataset object from inherited base classes: DatasetSplits with example: obj = BertCLFFinetuningDataset(batch_size)