class_docstr: `SST-2 <https://nlp.stanford.edu/sentiment/index.html>`_ dataset is a dataset for sentiment analysis. It is a modified version containing only binary labels (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) on top of the original 5-labeled dataset released first in `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank <https://aclanthology.org/D13-1170.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/TextClassification"
  
      dataset_name = "SST-2"
      dataset_path = os.path.join(base_path, dataset_name)
      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() == 2
      assert processor.get_labels() == ['0','1']
      assert len(train_dataset) == 6920
      assert len(dev_dataset) == 872
      assert len(test_dataset) == 1821
      assert train_dataset[0].text_a == 'a stirring , funny and finally transporting re-imagining of beauty and the beast and 1930s horror films'
      assert train_dataset[0].label == 1
code: def __init__(self):
      super().__init__()
      self.labels = ["0", "1"]
  
imports: import os
  import json, csv
  from abc import ABC, abstractmethod
  from collections import defaultdict, Counter
  from typing import List, Dict, Callable
  from openprompt.utils.logging import logger
  from openprompt.data_utils.utils import InputExample
  from openprompt.data_utils.data_processor import DataProcessor
label_desc: Use image node_id 1 to create a new SST2Processor object from inherited base classes: DataProcessor with example: obj = SST2Processor()