readme: Toy Wordlevel Tokenizer created for testing.
  
  Code used for its creation:
  
  ```
  from tokenizers import Tokenizer, normalizers, pre_tokenizers
  from tokenizers.models import WordLevel
  from tokenizers.normalizers import NFD, Lowercase, StripAccents
  from tokenizers.pre_tokenizers import Digits, Whitespace
  from tokenizers.processors import TemplateProcessing
  from tokenizers.trainers import WordLevelTrainer
  
  
  SMALL_TRAINING_CORPUS = [
      ["This is the first sentence.", "This is the second one."],
      ["This sentence (contains #) over symbols and numbers 12 3.", "But not this one."],
  ]
  
  tokenizer = Tokenizer(WordLevel(unk_token="[UNK]"))
  tokenizer.normalizer = normalizers.Sequence([NFD(), Lowercase(), StripAccents()])
  
  tokenizer.pre_tokenizer = pre_tokenizers.Sequence([Whitespace(), Digits(individual_digits=True)])
  
  tokenizer.post_processor = TemplateProcessing(
      single="[CLS] $A [SEP]",
      pair="[CLS] $A [SEP] $B:1 [SEP]:1",
      special_tokens=[
          ("[CLS]", 1),
          ("[SEP]", 2),
      ],
  )
  
  trainer = WordLevelTrainer(vocab_size=100, special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
  
  tokenizer.train_from_iterator(SMALL_TRAINING_CORPUS, trainer=trainer)
  
  tokenizer.save("tokenizer.json")
  ```
  
  ```
  from transformers import PreTrainedTokenizerFast
  
  tokenizer = PreTrainedTokenizerFast(
      tokenizer_file="tokenizer.json",
      bos_token="[CLS]",
      eos_token="[SEP]",
      unk_token="[UNK]",
      sep_token="[SEP]",
      pad_token="[PAD]",
      cls_token= "[CLS]",
      mask_token="[MASK]",
      model_max_length=10,
      padding_side="right"
  
  )
  
  tokenizer.push_to_hub('dummy-tokenizer-wordlevel', commit_message="add tokenizer")
  ```