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",")"]
docstr: Fit provided X and Y on built model
  
  Args:
      x: x samples
      y: y samples
      batch_size (int, optional): batch size per sample
      epochs (int, optional): number of epochs to run before ending training process
      validation_data (optional): x and y samples to validate at the end of the epoch
      callbacks (optional): additional callbacks to run with fitting
docstr_tok: ["Fit","provided","X","and","Y","on","built","model","Args",":","x",":","x","samples","y",":","y","samples","batch_size","(","int",",","optional",")",":","batch","size","per","sample","epochs","(","int",",","optional",")",":","number","of","epochs","to","run","before","ending","training","process","validation_data","(","optional",")",":","x","and","y","samples","to","validate","at","the","end","of","the","epoch","callbacks","(","optional",")",":","additional","callbacks","to","run","with","fitting"]
code: def fit(
      self,
      x,
      y,
      batch_size=1,
      epochs=1,
      validation_data=None,
      callbacks=None,
  ):
      """
      Fit provided X and Y on built model
  
      Args:
          x: x samples
          y: y samples
          batch_size (int, optional): batch size per sample
          epochs (int, optional): number of epochs to run before ending training process
          validation_data (optional): x and y samples to validate at the end of the epoch
          callbacks (optional): additional callbacks to run with fitting
      """
      self.model.fit(
          x=x,
          y=y,
          batch_size=batch_size,
          epochs=epochs,
          validation_data=validation_data,
          callbacks=callbacks,
      )
  
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 5 for calling the SequenceTagger obj's underlying member method code with example usage: obj.fit(x, y, batch_size, epochs, validation_data, callbacks) without return types