class_docstr: Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018),
  the subsequent more difficult SuperGLUE (Wang et al., 2019),
  other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018),
  and similar initiatives in other domains (Peng et al., 2019),
  we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark,
  a benchmark dataset to evaluate the performance of NLP methods in legal tasks.
  LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE.
  Find more information on the dataset here: https://huggingface.co/datasets/lex_glue
  
  We prompt models using the following format (example for unfair_tos)
  
      <sentence>
      Unfair Contractual Term Type:
  
      Target completion:
          <sentence> (<sentence>:"Limitation of liability", "Unilateral termination", "Unilateral change",
                      "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration")
  
  Using an example from the training dataset, we have
  
  ```
  "tinder may terminate your account at any time without notice if it believes that you have violated this agreement."
  
  Unfair Contractual Term Type:
  Target completion:
      "Unilateral change"
  ```
code: def __init__(self, subset: str):
      super().__init__()
      assert subset in list(TASK_CODE_MAPPING.keys()) + [
          "all"
      ], f"Unknown subset: {subset}"
      self.subsets = (
          [subset]
          if subset != "all"
          else list(TASK_CODE_MAPPING.keys())
      )
      self.random: random.Random = random.Random(42)
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/lex_glue_scenario.py
imports: import random
  from pathlib import Path
  from typing import List, Any
  import datasets
  from datasets import load_dataset
  from .lextreme_scenario import TaskType
  from .scenario import Scenario, Instance, Reference, CORRECT_TAG, TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT, Input, Output
label_desc: Use image node_id 1 to create a new LexGLUEScenario object from inherited base classes: Scenario with example: obj = LexGLUEScenario(subset)