class_docstr: A sequence tagging model for POS and Chunks written in Tensorflow (and Keras) based on the
  paper 'Deep multi-task learning with low level tasks supervised at lower layers'.
  The model has 3 Bi-LSTM layers and outputs POS and Chunk tags.
  
  Args:
      use_cudnn (bool, optional): use GPU based model (CUDNNA cells)
class_docstr_tok: ["A","sequence","tagging","model","for","POS","and","Chunks","written","in","Tensorflow","(","and","Keras",")","based","on","the","paper","'Deep","multi-task","learning","with","low","level","tasks","supervised","at","lower","layers","'",".","The","model","has","3","Bi-LSTM","layers","and","outputs","POS","and","Chunk","tags",".","Args",":","use_cudnn","(","bool",",","optional",")",":","use","GPU","based","model","(","CUDNNA","cells",")"]
code: def __init__(self, use_cudnn=False):
      self.vocabulary_size = None
      self.num_pos_labels = None
      self.num_chunk_labels = None
      self.char_vocab_size = None
      self.feature_size = None
      self.dropout = None
      self.max_word_len = None
      self.classifier = None
      self.optimizer = None
      self.model = None
      self.use_cudnn = use_cudnn
  
file_path: nlp-architect/nlp_architect/models/chunker.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import tensorflow
  from nlp_architect.nn.tensorflow.python.keras.layers.crf import CRF
  from nlp_architect.nn.tensorflow.python.keras.utils import load_model, save_model
label_desc: Use image node_id 1 to create a new SequenceTagger object from inherited base classes: object with example: obj = SequenceTagger(use_cudnn)