code: def test_ner(caplog, use_fast):
      if caplog:
          caplog.set_level(logging.CRITICAL)
  
      set_all_seeds(seed=42)
      device, n_gpu = initialize_device_settings(use_cuda=False)
      n_epochs = 3
      batch_size = 2
      evaluate_every = 1
      lang_model = "distilbert-base-german-cased"
  
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path=lang_model,
          do_lower_case=False,
          use_fast=use_fast,
      )
  
      ner_labels = [
          "[PAD]",
          "X",
          "O",
          "B-MISC",
          "I-MISC",
          "B-PER",
          "I-PER",
          "B-ORG",
          "I-ORG",
          "B-LOC",
          "I-LOC",
          "B-OTH",
          "I-OTH",
      ]
  
      processor = NERProcessor(
          tokenizer=tokenizer,
          max_seq_len=8,
          data_dir=Path("samples/ner"),
          train_filename="train-sample.txt",
          dev_filename="dev-sample.txt",
          test_filename=None,
          delimiter=" ",
          label_list=ner_labels,
          metric="seq_f1",
          multithreading_rust=False,
      )
  
      data_silo = DataSilo(
          processor=processor, batch_size=batch_size, max_processes=1
      )
      language_model = LanguageModel.load(lang_model)
      prediction_head = TokenClassificationHead(num_labels=13)
  
      model = AdaptiveModel(
          language_model=language_model,
          prediction_heads=[prediction_head],
          embeds_dropout_prob=0.1,
          lm_output_types=["per_token"],
          device=device,
      )
  
      model, optimizer, lr_schedule = initialize_optimizer(
          model=model,
          learning_rate=2e-5,
          # optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
          n_batches=len(data_silo.loaders["train"]),
          n_epochs=1,
          device=device,
          schedule_opts={
              "name": "LinearWarmup",
              "warmup_proportion": 0.1,
          },
      )
      trainer = Trainer(
          model=model,
          optimizer=optimizer,
          data_silo=data_silo,
          epochs=n_epochs,
          n_gpu=n_gpu,
          lr_schedule=lr_schedule,
          evaluate_every=evaluate_every,
          device=device,
      )
  
      save_dir = Path("testsave/ner")
      model = trainer.train()
      model.save(save_dir)
      processor.save(save_dir)
  
      del model
      del processor
      del optimizer
      del data_silo
      del trainer
  
      basic_texts = [
          {"text": "Paris is a town in France."},
      ]
      model = Inferencer.load(
          model_name_or_path="dbmdz/bert-base-cased-finetuned-conll03-english",
          num_processes=0,
          task_type="ner",
          use_fast=use_fast,
      )
      # labels arent correctly inserted from transformers
      # They are converted to LABEL_1 ... LABEL_N
      # For the inference result to contain predictions we need them in IOB NER format
      model.processor.tasks["ner"]["label_list"][-1] = "B-LOC"
      result = model.inference_from_dicts(dicts=basic_texts)
  
      assert result[0]["predictions"][0][0]["context"] == "Paris"
      assert isinstance(
          result[0]["predictions"][0][0]["probability"], np.float32
      )
      assert result[0]["predictions"][0][0]["probability"] > 0.99
      assert result[0]["predictions"][0][0]["label"] == "LOC"
  
file_path: FARM/test/test_ner.py
imports: from pathlib import Path
  import pytest
  import numpy
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import NERProcessor
  from farm.modeling.optimization import initialize_optimizer
  from farm.infer import Inferencer
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import TokenClassificationHead
  from farm.modeling.tokenization import Tokenizer
  from farm.train import Trainer
  from farm.utils import set_all_seeds, initialize_device_settings
  import logging
label_desc: Use image node_id 1 for calling a global function with example usage: test_ner(caplog, use_fast) without return types