class_docstr: A model wrapper queries a model with a list of text inputs.
  
  Classification-based models return a list of lists, where each sublist
  represents the model's scores for a given input.
  
  Text-to-text models return a list of strings, where each string is the
  output – like a translation or summarization – for a given input.
class_docstr_tok: ["A","model","wrapper","queries","a","model","with","a","list","of","text","inputs",".","Classification-based","models","return","a","list","of","lists",",","where","each","sublist","represents","the","model","'s","scores","for","a","given","input",".","Text-to-text","models","return","a","list","of","strings",",","where","each","string","is","the","output","\u2013","like","a","translation","or","summarization","\u2013","for","a","given","input","."]
docstr: :param model: any model object
  :param tokenizer: support tokenize sentence and convert tokens to
      model input ids
  :param str task: task name
  :param int batch_size: batch size to apply evaluation
docstr_tok: [":","param","model",":","any","model","object",":","param","tokenizer",":","support","tokenize","sentence","and","convert","tokens","to","model","input","ids",":","param","str","task",":","task","name",":","param","int","batch_size",":","batch","size","to","apply","evaluation"]
code: def __init__(self, model, tokenizer, task="SA", batch_size=1):
      r"""
  
      :param model: any model object
      :param tokenizer: support tokenize sentence and convert tokens to
          model input ids
      :param str task: task name
      :param int batch_size: batch size to apply evaluation
  
      """
      if task not in TASK_METRICS:
          raise ValueError(f"Not support task {task} !")
      self.task = task
      self.model = model
      self.tokenizer = tokenizer
      self.batch_size = batch_size
  
file_path: textflint/textflint/input/model/flint_model/flint_model.py
imports: from abc import ABC
  import numpy
  from ..metrics.metrics import accuracy_score
  from ..metrics.metrics import POSMetric
  from ..metrics.nerspanmetric import NERSpanMetric
label_desc: Use image node_id 1 to create a new FlintModel object from inherited base classes: ABC with example: obj = FlintModel(model, tokenizer, task, batch_size)