class_docstr: `SemEval-2010 Task 8 <https://aclanthology.org/S10-1006.pdf>`_ is a  a traditional dataset in relation classification.
  
  It was released together with the paper `SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals (Hendrickx et al. 2010) <https://aclanthology.org/S10-1006.pdf>`_
  
  Examples:
  
  ..  code-block:: python
  
      from openprompt.data_utils.relation_classification_dataset import PROCESSORS
  
      base_path = "datasets/RelationClassification"
  
      dataset_name = "SemEval"
      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() == 19
      assert processor.get_labels() == ["Other", "Member-Collection(e1,e2)", "Entity-Destination(e1,e2)", "Content-Container(e1,e2)", "Message-Topic(e1,e2)", "Entity-Origin(e1,e2)", "Cause-Effect(e1,e2)", "Product-Producer(e1,e2)", "Instrument-Agency(e1,e2)", "Component-Whole(e1,e2)", "Member-Collection(e2,e1)", "Entity-Destination(e2,e1)", "Content-Container(e2,e1)", "Message-Topic(e2,e1)", "Entity-Origin(e2,e1)", "Cause-Effect(e2,e1)", "Product-Producer(e2,e1)", "Instrument-Agency(e2,e1)", "Component-Whole(e2,e1)"]
      assert len(train_dataset) == 6507
      assert len(dev_dataset) == 1493
      assert len(test_dataset) == 2717
      assert dev_dataset[0].text_a == 'the system as described above has its greatest application in an arrayed configuration of antenna elements .'
      assert dev_dataset[0].meta["head"] == "configuration"
      assert dev_dataset[0].meta["tail"] == "elements"
      assert dev_dataset[0].label == 18
code: def __init__(self):
      super().__init__()
      self.labels = [
          "Other",
          "Member-Collection(e1,e2)",
          "Entity-Destination(e1,e2)",
          "Content-Container(e1,e2)",
          "Message-Topic(e1,e2)",
          "Entity-Origin(e1,e2)",
          "Cause-Effect(e1,e2)",
          "Product-Producer(e1,e2)",
          "Instrument-Agency(e1,e2)",
          "Component-Whole(e1,e2)",
          "Member-Collection(e2,e1)",
          "Entity-Destination(e2,e1)",
          "Content-Container(e2,e1)",
          "Message-Topic(e2,e1)",
          "Entity-Origin(e2,e1)",
          "Cause-Effect(e2,e1)",
          "Product-Producer(e2,e1)",
          "Instrument-Agency(e2,e1)",
          "Component-Whole(e2,e1)",
      ]
  
imports: import os
  import json, csv
  from abc import ABC, abstractmethod
  from collections import defaultdict, Counter
  from typing import *
  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 SemEvalProcessor object from inherited base classes: DataProcessor with example: obj = SemEvalProcessor()