class_docstr: A spaCy Language wrapper for NLP NER monitoring in argilla
class_docstr_tok: ["A","spaCy","Language","wrapper","for","NLP","NER","monitoring","in","argilla"]
code: def _prepare_log_data(
      self, docs_info: Tuple[Doc, Optional[Dict[str, Any]]]
  ) -> Dict[str, Any]:
      return dict(
          records=[
              self.doc2token_classification(
                  doc,
                  agent=self.__wrapped__.path.name,
                  metadata=metadata,
              )
              for doc, metadata in docs_info
          ],
          name=self.dataset,
          tags={
              k: v
              for k, v in self.__model__.meta.items()
              if isinstance(v, str)
          },
          metadata=self.__model__.meta,
      )
  
file_path: argilla/src/argilla/monitoring/_spacy.py
imports: from datetime import datetime
  from typing import Any, Dict, Optional, Tuple
  from argilla import TokenClassificationRecord
  from argilla.client.api import Api
  from argilla.monitoring.base import BaseMonitor
  from argilla.monitoring.types import MissingType
label_desc: Use image node_id 2 for calling the SpacyNERMonitor obj's underlying member method code with example usage: obj._prepare_log_data(docs_info) and returns: dict