text: Title: How and where to add add_unk = True
  Body: Hello, I am training a ner model, apparently I have a wrong tag in my training corpus because during the training process I get this error. 
  ![errorner](https://user-images.githubusercontent.com/50722626/168899243-e104d09c-a177-4a81-8a70-7f90938c9426.png)
  
  
  I already checked the corpus several times and I can't find the error, so I want to do what the problem tells me to add add_unk = True, but I don't know where to add it, in what part of the code.
  The code I am using is the following
  
  
  ```
  from flair.data import Corpus
  from flair.datasets import ColumnCorpus
  from flair.embeddings import TransformerWordEmbeddings
  from flair.models import SequenceTagger
  from flair.trainers import ModelTrainer
  with tf.device('/device:GPU:0'):
  
  
    # 1. get the corpus
    columns = {0:'text',1:'ner'}
    data_folder = '/content/drive/MyDrive/corpus de prueba/entrenamiento1'
  
    corpus: Corpus = ColumnCorpus(data_folder, columns,
                                train_file='train.txt',
                                test_file='test.txt',
                                dev_file='dev.txt')
  
    print(len(corpus.train))
  
    # 2. what label do we want to predict?
    label_type = 'ner'
  
    # 3. make the label dictionary from the corpus
    label_dict = corpus.make_label_dictionary(label_type=label_type)
    print("el diccionario de mi corpus contiene las etiquetas: ",label_dict)
  
    # 4. initialize fine-tuneable transformer embeddings WITH document context
    embeddings = TransformerWordEmbeddings(model='bert-base-multilingual-cased',
                                         layers="-1",
                                         subtoken_pooling="first",
                                         fine_tune=True,
                                         use_context=True,
                                         )
  
    # 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
    tagger = SequenceTagger(hidden_size=256,
                          embeddings=embeddings,
                          tag_dictionary=label_dict,
                          tag_type='ner',
                          use_crf=False,
                          use_rnn=False,
                          reproject_embeddings=False,
                          )
  
    # 6. initialize trainer
    trainer = ModelTrainer(tagger, corpus)
  
    # 7. run fine-tuning
    trainer.fine_tune('resources/taggers/pruebatest',
                    learning_rate=5.0e-3,
                    mini_batch_size=2,
                    max_epochs=5,
                    #mini_batch_chunk_size=1,  # remove this parameter to speed up computation if you have a big GPU
                    )
  ```
  
  Thanks