docstr: :param model: any model object
  :param int batch_size: batch size to apply evaluation
  :param Object metrics: metrics takes the predicted labels
  and the ground-truth labels as input and calculates f1, recall
  and precision. It takes a NERSpanMetric metric
  as default, but can also be implemented by the user.
  :param str encoding_type: supporting 'bio', 'bmes', 'bmeso', 'bioes',
  'bio' as default.
code: def __init__(
      self, model, batch_size=100, metrics=None, encoding_type="bio"
  ):
      r"""
  
      :param model: any model object
      :param int batch_size: batch size to apply evaluation
      :param Object metrics: metrics takes the predicted labels
      and the ground-truth labels as input and calculates f1, recall
      and precision. It takes a NERSpanMetric metric
      as default, but can also be implemented by the user.
      :param str encoding_type: supporting 'bio', 'bmes', 'bmeso', 'bioes',
      'bio' as default.
      """
      super(FlintModelNER, self).__init__(
          model=model, tokenizer=None, task="NER", batch_size=batch_size
      )
      # load augments for the model
      self.batch_size = batch_size
      self.model = model
      self.encoding_type = encoding_type
      self.metrics2score = {
          "f": "f1_score",
          "pre": "precision",
          "rec": "recall",
      }
      if metrics is not None:
          self.metrics = metrics
      else:
          self.metrics = TASK_METRICS["NER"][0]["fun"](
              encoding_type=self.encoding_type
          )
  
file_path: textflint/textflint/input/model/flint_model/flint_model_ner.py
imports: import json
  from ..metrics.nerspanmetric import NERSpanMetric
  from .flint_model import TASK_METRICS, FlintModel
  from ..test_model.bilstm_crf_torch import TaggerFactory
label_desc: Use image node_id 1 to create a new FlintModelNER object from inherited base classes: FlintModel with example: obj = FlintModelNER(model, batch_size, metrics, encoding_type)