class_docstr: A user facing class for text to text generation
class_docstr_tok: ["A","user","facing","class","for","text","to","text","generation"]
code: def __init__(
      self,
      model_type: str = "T5",
      model_name: str = "t5-small",
      load_path: str = "",
      use_auth_token: Union[bool, str] = None,
      trust_remote_code: bool = False,
  ):
      self.adaptor = get_adaptor(model_type)
      model_class = AutoModelForSeq2SeqLM
  
      super().__init__(
          model_type,
          model_name,
          model_class,
          use_auth_token=use_auth_token,
          load_path=load_path,
          trust_remote_code=trust_remote_code,
      )
  
      self._pipeline_class = Text2TextGenerationPipeline
  
      self._data_collator = DataCollatorForSeq2Seq(
          self.tokenizer, model=self.model
      )
      self._t_data_file_type = ["csv"]
      self._type = "tt"
  
file_path: happy-transformer/happytransformer/happy_text_to_text.py
imports: from dataclasses import dataclass
  from typing import Union
  from datasets import Dataset
  from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Text2TextGenerationPipeline
  from happytransformer.adaptors import get_adaptor
  from happytransformer.args import TTEvalArgs, TTTestArgs, TTTrainArgs
  from happytransformer.fine_tuning_util import EvalResult
  from happytransformer.happy_transformer import HappyTransformer
label_desc: Use image node_id 1 to create a new HappyTextToText object from inherited base classes: HappyTransformer with example: obj = HappyTextToText(model_type, model_name, load_path, use_auth_token, trust_remote_code)