class_docstr: A user facing class for text classification
class_docstr_tok: ["A","user","facing","class","for","text","classification"]
code: def __init__(
      self,
      model_type: str = "DISTILBERT",
      model_name: str = "distilbert-base-uncased",
      load_path: str = "",
      use_auth_token: Union[bool, str] = None,
      trust_remote_code: bool = False,
  ):
      self.adaptor = get_adaptor(model_type)
      model_class = AutoModelForMaskedLM
  
      super().__init__(
          model_type,
          model_name,
          model_class,
          load_path=load_path,
          use_auth_token=use_auth_token,
          trust_remote_code=trust_remote_code,
      )
  
      self._pipeline_class = FillMaskPipeline
  
      # mlm_probability is modified to the train args value within HappyTransformer._run_eval() and HappyTransformer._run_eval()
      self._data_collator = DataCollatorForLanguageModeling(
          tokenizer=self.tokenizer, mlm_probability=0.1
      )
      self._t_data_file_type = ["text", "csv"]
      self._type = "wp"
  
file_path: happy-transformer/happytransformer/happy_word_prediction.py
imports: from dataclasses import dataclass
  from datasets import Dataset
  from typing import List, Optional, Union
  from transformers import AutoModelForMaskedLM, DataCollatorForLanguageModeling, FillMaskPipeline
  from happytransformer.adaptors import get_adaptor
  from happytransformer.args import WPEvalArgs, WPTrainArgs
  from happytransformer.fine_tuning_util import EvalResult, tok_text_gen_mlm, csv_tok_text_gen_mlm
  from happytransformer.happy_transformer import HappyTransformer
label_desc: Use image node_id 1 to create a new HappyWordPrediction object from inherited base classes: HappyTransformer with example: obj = HappyWordPrediction(model_type, model_name, load_path, use_auth_token, trust_remote_code)