class_docstr: Base class for NER backends.
class_docstr_tok: ["Base","class","for","NER","backends","."]
docstr: Predict annotations for a collection of documents.
  
  :param texts:
      Raw texts to be annotated.
  :param batch_size:
      Size of text batches that are
      passed to the model.
  :returns:
      NER annotations.
docstr_tok: ["Predict","annotations","for","a","collection","of","documents",".",":","param","texts",":","Raw","texts","to","be","annotated",".",":","param","batch_size",":","Size","of","text","batches","that","are","passed","to","the","model",".",":","returns",":","NER","annotations","."]
code: def annotate(
      self, texts: List[str], *, batch_size: int = 1
  ) -> List[List[NamedEntityAnnotation]]:
      """
      Predict annotations for a collection of documents.
  
      :param texts:
          Raw texts to be annotated.
      :param batch_size:
          Size of text batches that are
          passed to the model.
      :returns:
          NER annotations.
      """
  
imports: from abc import ABC, abstractmethod
  from contextlib import contextmanager
  from dataclasses import dataclass
  from enum import Enum, EnumMeta
  from typing import Any, Dict, List, Optional, Union
  from ...None import ComponentError, DeserializationError, Document, component, default_from_dict, default_to_dict
  from ...lazy_imports import LazyImport
label_desc: Use image node_id 4 for calling the _NerBackend obj's underlying member method code with example usage: obj.annotate(texts) without return types