class_docstr: use AutoModelForMaskedLM, and select the output by another layer in the lit model
code: def __init__(self, model, device, args, tokenizer):
      super().__init__(model, device, args)
      self.tokenizer = tokenizer
  
      with open(f"{args.data_dir}/rel2id.json", "r") as file:
          rel2id = json.load(file)
  
      Na_num = 0
      for k, v in rel2id.items():
          if k == "NA" or k == "no_relation" or k == "Other":
              Na_num = v
              break
      num_relation = len(rel2id)
      # init loss function
      self.loss_fn = (
          multilabel_categorical_crossentropy
          if "dialogue" in args.data_dir
          else nn.CrossEntropyLoss()
      )
      # ignore the no_relation class to compute the f1 score
      self.eval_fn = (
          f1_eval
          if "dialogue" in args.data_dir
          else partial(f1_score, rel_num=num_relation, na_num=Na_num)
      )
      self.best_f1 = 0
      self.t_lambda = args.t_lambda
  
      self.label_st_id = tokenizer(
          "[class1]", add_special_tokens=False
      )["input_ids"][0]
  
      self._init_label_word()
  
imports: import json
  import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from .base import BaseLitModel
  from .util import dialog_f1_eval, f1_eval, compute_f1, acc, f1_score
  from transformers.optimization import get_linear_schedule_with_warmup
  from functools import partial
label_desc: Use image node_id 1 to create a new BertLitModel object from inherited base classes: BaseLitModel with example: obj = BertLitModel(model, device, args, tokenizer)