class_docstr: The 2014 Workshop on Statistical Machine Translation:
  https://aclanthology.org/W14-3302.pdf
  
  The scenario consists of 5 subsets, each of which is a parallel corpus between English and another language. The
  non-English languages include Czech, German, French, Hindi, and Russian.
  
  For each language pair, the validation and test set each includes around 3,000 examples, while the training set is
  usually much larger. We therefore randomly downsample the training set to speedup data processing.
  
  Task prompt structure:
  
      Translate {source_language} to {target_language}:
      {Hypothesis} = {Reference}
  
  Example from WMT14 Fr-En:
  
      Hypothesis: Assemblée générale
      Reference: General Assembly
docstr: Remove instances in the dataset with the same label.
code: def _deduplicate(self, dataset: List):
      """
      Remove instances in the dataset with the same label.
      """
  
      deduplicated_dataset = []
      seen_labels = set()
      for example in dataset:
          if example[self.target_language] not in seen_labels:
              seen_labels.add(example[self.target_language])
              deduplicated_dataset.append(example)
      return deduplicated_dataset
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/wmt_14_scenario.py
imports: from typing import List, Any
  from datasets import load_dataset
  from helm.common.hierarchical_logger import hlog
  from .scenario import Scenario, Instance, Reference, TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT, CORRECT_TAG, Input, Output
label_desc: Use image node_id 2 for calling the WMT14Scenario obj's underlying member method code with example usage: obj._deduplicate(dataset) and returns: deduplicated_dataset