class_docstr: Dataset for unlearning span corruption (https://arxiv.org/abs/2104.08691) in T5 models.
  Corresponds to the prefix-LM objective in the T5 paper (Table 3 in https://arxiv.org/abs/1910.10683).
code: def __init__(
      self,
      cfg,
      trainer,
      tokenizer,
      name,
      data_prefix,
      documents,
      indexed_dataset,
      num_samples,
      seed,
      max_seq_length_encoder,
      max_seq_length_decoder,
      **kwargs,
  ):
      self.max_seq_length_encoder = max_seq_length_encoder
      self.max_seq_length_decoder = max_seq_length_decoder
      self.seed = seed
      self.tokenizer = tokenizer
      super().__init__(
          cfg,
          trainer,
          tokenizer,
          name,
          data_prefix,
          documents,
          indexed_dataset,
          num_samples,
          self.max_seq_length_encoder
          + self.max_seq_length_decoder
          + 1,  # +1 because the decoder sequence gets truncated by one due to shifting to for teacher-forcing.
          seed,
      )
  
imports: import numpy
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_dataset import GPTDataset
  from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution
label_desc: Use image node_id 1 to create a new T5LMAdaptedDataset object from inherited base classes: GPTDataset with example: obj = T5LMAdaptedDataset(cfg, trainer, tokenizer, name, data_prefix, documents, indexed_dataset, num_samples, seed, max_seq_length_encoder, max_seq_length_decoder)