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: Unzip sample to input texts and labels.
  
  :param list data_samples: sample list
  :return: (inputs_text), labels.
docstr_tok: ["Unzip","sample","to","input","texts","and","labels",".",":","param","list","data_samples",":","sample","list",":","return",":","(","inputs_text",")",",","labels","."]
code: def unzip_samples(self, data_samples):
      r"""
      Unzip sample to input texts and labels.
  
      :param list data_samples: sample list
      :return: (inputs_text), labels.
  
      """
      raise NotImplementedError()
  
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 6 for calling the FlintModel obj's underlying member method code with example usage: obj.unzip_samples(data_samples) without return types