code: def __init__(
      self,
      model_path="roberta-base",
      temperature=1.0,
      top_k=None,
      top_p=None,
      batch_size=32,
      device="cuda",
      silence=True,
  ):
      super().__init__(
          device,
          temperature=temperature,
          top_k=top_k,
          top_p=top_p,
          batch_size=batch_size,
          silence=True,
      )
      try:
          from transformers import AutoModelForMaskedLM, AutoTokenizer
      except ModuleNotFoundError:
          raise ModuleNotFoundError(
              "Missed transformers library. Install transfomers by `pip install transformers`"
          )
  
      self.model_path = model_path
  
      self.tokenizer = AutoTokenizer.from_pretrained(model_path)
      self.mask_id = self.token2id(self.MASK_TOKEN)
      self.pad_id = self.token2id(self.PAD_TOKEN)
      if silence:
          # Transformers thrown an warning regrading to weight initialization. It is expected
          orig_log_level = logging.getLogger(
              "transformers." + "modeling_utils"
          ).getEffectiveLevel()
          logging.getLogger(
              "transformers." + "modeling_utils"
          ).setLevel(logging.ERROR)
          self.model = AutoModelForMaskedLM.from_pretrained(model_path)
          logging.getLogger(
              "transformers." + "modeling_utils"
          ).setLevel(orig_log_level)
      else:
          self.model = AutoModelForMaskedLM.from_pretrained(model_path)
  
      self.model.to(self.device)
      self.model.eval()
  
file_path: nlpaug/nlpaug/model/lang_models/roberta.py
imports: import logging
  from nlpaug.model.lang_models import LanguageModels
  from nlpaug.util.selection.filtering import *
label_desc: Use image node_id 1 to create a new Roberta object from inherited base classes: LanguageModels with example: obj = Roberta(model_path, temperature, top_k, top_p, batch_size, device, silence)