class_docstr: Roberta Featurizer.
  
  The Roberta Featurizer is a wrapper class of the Roberta Tokenizer,
  which is used by Huggingface's transformers library for tokenizing large corpuses for Roberta Models.
  Please confirm the details in [1]_.
  
  Please see https://github.com/huggingface/transformers
  and https://github.com/seyonechithrananda/bert-loves-chemistry for more details.
  
  Examples
  --------
  >>> from deepchem.feat import RobertaFeaturizer
  >>> smiles = ["Cn1c(=O)c2c(ncn2C)n(C)c1=O", "CC(=O)N1CN(C(C)=O)C(O)C1O"]
  >>> featurizer = RobertaFeaturizer.from_pretrained("seyonec/SMILES_tokenized_PubChem_shard00_160k")
  >>> out = featurizer(smiles, add_special_tokens=True, truncation=True)
  
  References
  ----------
  .. [1] Chithrananda, Seyone, Grand, Gabriel, and Ramsundar, Bharath (2020): "Chemberta: Large-scale self-supervised
      pretraining for molecular property prediction." arXiv. preprint. arXiv:2010.09885.
  
  
  Note
  -----
  This class requires transformers to be installed.
  RobertaFeaturizer uses dual inheritance with RobertaTokenizerFast in Huggingface for rapid tokenization,
  as well as DeepChem's MolecularFeaturizer class.
code: def __init__(self, **kwargs):
      super().__init__(**kwargs)
      return
  
file_path: deepchem/deepchem/feat/roberta_tokenizer.py
imports: from deepchem.feat import Featurizer
  from typing import Dict, List
label_desc: Use image node_id 1 to create a new RobertaFeaturizer object from inherited base classes: RobertaTokenizerFast, Featurizer with example: obj = RobertaFeaturizer()