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 pipe(self, *args, **kwargs):
      as_tuples = kwargs.get("as_tuples")
      results = self.__model__.pipe(*args, **kwargs)
  
      log_info = []
      for r in results:
          metadata = {}
          if as_tuples:
              doc, metadata = r  # context
          else:
              doc = r
          if self.is_record_accepted():
              log_info.append((doc, metadata))
          yield r
  
      self.send_records(log_info)
  
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 3 for calling the SpacyNERMonitor obj's underlying member method code with example usage: obj.pipe() without return types