class_docstr: Wrapper for building vocabulary from HuggingFace algorithms
  
  The wrapper class can be used to build huggingface vocabulary building algorithms
  with DeepChem.
  
  Parameters
  ----------
  model: tokenizers.models.Model
      The core tokenization algorithm to learn the vocabulary.
  trainers: tokenizers.trainers.Trainer
      The trainer capable of training the model
  
  Example
  -------
  >>> from tokenizers import models, trainers
  >>> from tokenizers.pre_tokenizers import Whitespace
  >>> model = models.BPE(unk_token="[UNK]")
  >>> special_tokens = ["[UNK]"]
  >>> trainer = trainers.BpeTrainer(vocab_size=20, special_tokens=special_tokens)
  >>> vb = HuggingFaceVocabularyBuilder(model=model, trainer=trainer)
docstr: Trains the algorithm on the vocabulary
  
  Parameters
  ----------
  paths: List[str]
      A list of file paths for training
docstr_tok: ["Trains","the","algorithm","on","the","vocabulary","Parameters","--","--","--","--","--","paths",":","List","[","str","]","A","list","of","file","paths","for","training"]
code: def build(self, paths: List[str]):  # type: ignore
      """Trains the algorithm on the vocabulary
  
      Parameters
      ----------
      paths: List[str]
          A list of file paths for training
      """
      self.tokenizer.train(paths, self.trainer)
  
file_path: deepchem/deepchem/feat/vocabulary_builders/hf_vocab.py
imports: from deepchem.feat.vocabulary_builders.vocabulary_builder import VocabularyBuilder
  from typing import List, TYPE_CHECKING
label_desc: Use image node_id 2 for calling the HuggingFaceVocabularyBuilder obj's underlying member method code with example usage: obj.build(paths) without return types