class_docstr: `The Stanford Natural Language Inference (SNLI) corpus <https://nlp.stanford.edu/projects/snli/>`_ is a dataset for natural language inference. It is first released in `A large annotated corpus for learning natural language inference (Bowman et al. 2015) <https://nlp.stanford.edu/pubs/snli_paper.pdf>`_
  
  We use the data released in `Making Pre-trained Language Models Better Few-shot Learners (Gao et al. 2020) <https://arxiv.org/pdf/2012.15723.pdf>`_
  
  Examples:
  
  ..  code-block:: python
  
      from openprompt.data_utils.lmbff_dataset import PROCESSORS
  
      base_path = "datasets"
  
      dataset_name = "SNLI"
      dataset_path = os.path.join(base_path, dataset_name, '16-13')
      processor = PROCESSORS[dataset_name.lower()]()
      train_dataset = processor.get_train_examples(dataset_path)
      dev_dataset = processor.get_dev_examples(dataset_path)
      test_dataset = processor.get_test_examples(dataset_path)
  
      assert processor.get_num_labels() == 3
      assert processor.get_labels() == ['entailment', 'neutral', 'contradiction']
      assert len(train_dataset) == 549367
      assert len(dev_dataset) == 9842
      assert len(test_dataset) == 9824
      assert train_dataset[0].text_a == 'A person on a horse jumps over a broken down airplane.'
      assert train_dataset[0].text_b == 'A person is training his horse for a competition.'
      assert train_dataset[0].label == 1
code: def __init__(self):
      super().__init__()
      self.labels = ["entailment", "neutral", "contradiction"]
  
file_path: OpenPrompt/openprompt/data_utils/nli_dataset.py
imports: import os
  from typing import List
  from openprompt.data_utils import InputExample
  from openprompt.data_utils.data_processor import DataProcessor
label_desc: Use image node_id 1 to create a new SNLIProcessor object from inherited base classes: DataProcessor with example: obj = SNLIProcessor()