class_docstr: Token Classification Model with BERT, applicable for tasks such as Named Entity Recognition
class_docstr_tok: ["Token","Classification","Model","with","BERT",",","applicable","for","tasks","such","as","Named","Entity","Recognition"]
docstr: Add predicted token labels to the queries. Use this method for debugging and prototyping.
  Args:
      queries: text
      batch_size: batch size to use during inference.
      output_file: file to save models predictions
  Returns:
      result: text with added entities
docstr_tok: ["Add","predicted","token","labels","to","the","queries",".","Use","this","method","for","debugging","and","prototyping",".","Args",":","queries",":","text","batch_size",":","batch","size","to","use","during","inference",".","output_file",":","file","to","save","models","predictions","Returns",":","result",":","text","with","added","entities"]
code: def add_predictions(
      self,
      queries: Union[List[str], str],
      batch_size: int = 32,
      output_file: Optional[str] = None,
  ) -> List[str]:
      """
      Add predicted token labels to the queries. Use this method for debugging and prototyping.
      Args:
          queries: text
          batch_size: batch size to use during inference.
          output_file: file to save models predictions
      Returns:
          result: text with added entities
      """
      if queries is None or len(queries) == 0:
          return []
  
      if isinstance(queries, str):
          logging.info(f"Reading from {queries} file")
          with open(queries, "r") as f:
              queries = f.readlines()
  
      result = []
      all_preds = self._infer(queries, batch_size)
  
      queries = [q.strip().split() for q in queries]
      num_words = [len(q) for q in queries]
      if sum(num_words) != len(all_preds):
          raise ValueError("Pred and words must have the same length")
  
      ids_to_labels = {v: k for k, v in self._cfg.label_ids.items()}
      start_idx = 0
      end_idx = 0
      for query in queries:
          end_idx += len(query)
  
          # extract predictions for the current query from the list of all predictions
          preds = all_preds[start_idx:end_idx]
          start_idx = end_idx
  
          query_with_entities = ""
          for j, word in enumerate(query):
              # strip out the punctuation to attach the entity tag to the word not to a punctuation mark
              # that follows the word
              if word[-1].isalpha():
                  punct = ""
              else:
                  punct = word[-1]
                  word = word[:-1]
  
              query_with_entities += word
              label = ids_to_labels[preds[j]]
  
              if label != self._cfg.dataset.pad_label:
                  query_with_entities += "[" + label + "]"
              query_with_entities += punct + " "
          result.append(query_with_entities.strip())
  
      if output_file is not None:
          with open(output_file, "w") as f:
              for r in result:
                  f.write(r + "\n")
          logging.info(f"Predictions saved to {output_file}")
      return result
  
imports: import os
  from typing import List, Optional, Union
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import CrossEntropyLoss
  from nemo.collections.nlp.data.data_utils.data_preprocessing import get_labels_to_labels_id_mapping
  from nemo.collections.nlp.data.token_classification.token_classification_dataset import BertTokenClassificationDataset, BertTokenClassificationInferDataset
  from nemo.collections.nlp.data.token_classification.token_classification_utils import get_label_ids
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common import TokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import get_classification_report, plot_confusion_matrix, tensor2list
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 16 for calling the TokenClassificationModel obj's underlying member method code with example usage: obj.add_predictions(queries, batch_size, output_file) and returns: result, list