class_docstr: Generic processor for a single sentence classification data set.
class_docstr_tok: ["Generic","processor","for","a","single","sentence","classification","data","set","."]
code: def add_examples(
      self,
      texts_or_text_and_labels,
      labels=None,
      ids=None,
      overwrite_labels=False,
      overwrite_examples=False,
  ):
      if labels is not None and len(texts_or_text_and_labels) != len(
          labels
      ):
          raise ValueError(
              f"Text and labels have mismatched lengths {len(texts_or_text_and_labels)} and {len(labels)}"
          )
      if ids is not None and len(texts_or_text_and_labels) != len(ids):
          raise ValueError(
              f"Text and ids have mismatched lengths {len(texts_or_text_and_labels)} and {len(ids)}"
          )
      if ids is None:
          ids = [None] * len(texts_or_text_and_labels)
      if labels is None:
          labels = [None] * len(texts_or_text_and_labels)
      examples = []
      added_labels = set()
      for text_or_text_and_label, label, guid in zip(
          texts_or_text_and_labels, labels, ids
      ):
          if (
              isinstance(text_or_text_and_label, (tuple, list))
              and label is None
          ):
              text, label = text_or_text_and_label
          else:
              text = text_or_text_and_label
          added_labels.add(label)
          examples.append(
              InputExample(
                  guid=guid, text_a=text, text_b=None, label=label
              )
          )
  
      # Update examples
      if overwrite_examples:
          self.examples = examples
      else:
          self.examples.extend(examples)
  
      # Update labels
      if overwrite_labels:
          self.labels = list(added_labels)
      else:
          self.labels = list(set(self.labels).union(added_labels))
  
      return self.examples
  
imports: import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  from ...utils import is_tf_available, is_torch_available, logging
label_desc: Use image node_id 7 for calling the SingleSentenceClassificationProcessor obj's underlying member method code with example usage: obj.add_examples(texts_or_text_and_labels, labels, ids, overwrite_labels, overwrite_examples) and returns: self