class_docstr: Named Entity Recognition pipeline for transformers.
  
  Example:
      >>> nlp = Ner("dslim/bert-base-NER")
      >>> doc = nlp("Hugging Face Inc. is a company based in New York City.")
      >>> [(ent.text, ent.label) for ent in doc.ents]
      [("Hugging Face Inc", "ORG"), ("New York City", "LOC")]
  
  Args:
      pretrained_model_name_or_path: passed to transformers' `from_pretrained` methods.
      tokenizer_kwargs: passed to transformers' tokenizer constructor.
      model_kwargs: passed to transformers' model constructor.
code: def from_disk(cls, path: Path) -> "Ner":
      meta = cls._SerdeMeta.from_disk(path)
      return cls(str(path), **asdict(meta))
  
imports: from dataclasses import asdict, dataclass
  from pathlib import Path
  from typing import Any, ClassVar, Dict, List, Optional, Tuple
  import textspan
  import torch
  from camphr.doc import Doc, Ent
  from camphr.nlp import Nlp
  from camphr.serde import SerDe, SerDeDataclassMixin
  from transformers import AutoModelForTokenClassification, AutoTokenizer
  from typing_extensions import TypedDict
label_desc: Use image node_id 5 for calling the Ner obj's underlying member method code with example usage: obj.from_disk(cls, path) and returns: cls