class_docstr: Summarize given model performance on a dataset based on selected scorers.
  
  Parameters
  ----------
  scorers : Union[List[str], Dict[str, Union[str, Callable]]], default: None
      List of scorers to use. If None, use default scorers.
      Scorers can be supplied as a list of scorer names or as a dictionary of names and functions.
  max_rows_to_display : int, default: 15
      Maximum number of rows to display in the check result.
  n_samples : int , default: 10_000
      Maximum number of samples to use for this check.
class_docstr_tok: ["Summarize","given","model","performance","on","a","dataset","based","on","selected","scorers",".","Parameters","--","--","--","--","--","scorers",":","Union","[","List","[","str","]",",","Dict","[","str",",","Union","[","str",",","Callable","]","]","]",",","default",":","None","List","of","scorers","to","use",".","If","None",",","use","default","scorers",".","Scorers","can","be","supplied","as","a","list","of","scorer","names","or","as","a","dictionary","of","names","and","functions",".","max_rows_to_display",":","int",",","default",":","15","Maximum","number","of","rows","to","display","in","the","check","result",".","n_samples",":","int",",","default",":","10_000","Maximum","number","of","samples","to","use","for","this","check","."]
code: def __init__(
      self,
      scorers: Union[List[str], Dict[str, Union[str, Callable]]] = None,
      max_rows_to_display: int = 15,
      n_samples: int = 10_000,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.scorers = scorers
      self.max_rows_to_display = max_rows_to_display
      self.n_samples = n_samples
  
file_path: deepchecks/deepchecks/nlp/checks/model_evaluation/single_dataset_performance.py
imports: from numbers import Number
  from typing import Callable, Dict, List, Union
  import pandas
  from deepchecks.core import CheckResult
  from deepchecks.core.check_utils.single_dataset_performance_base import BaseSingleDatasetPerformance
  from deepchecks.nlp.base_checks import SingleDatasetCheck
  from deepchecks.nlp.context import Context
  from deepchecks.nlp.metric_utils.scorers import infer_on_text_data
label_desc: Use image node_id 1 to create a new SingleDatasetPerformance object from inherited base classes: SingleDatasetCheck, BaseSingleDatasetPerformance with example: obj = SingleDatasetPerformance(scorers, max_rows_to_display, n_samples)