code: def doc_classification_multilabel():
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
  
      ml_logger = MLFlowLogger(
          tracking_uri="https://public-mlflow.deepset.ai/"
      )
      ml_logger.init_experiment(
          experiment_name="Public_FARM",
          run_name="Run_doc_classification",
      )
  
      ##########################
      ########## Settings
      ##########################
      set_all_seeds(seed=42)
      device, n_gpu = initialize_device_settings(use_cuda=True)
      n_epochs = 1
      batch_size = 32
  
      evaluate_every = 500
      lang_model = "bert-base-uncased"
      do_lower_case = True
  
      # 1.Create a tokenizer
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path=lang_model,
          do_lower_case=do_lower_case,
      )
  
      # 2. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset
      # Here we load Toxic Comments Data automaticaly if it is not available.
  
      label_list = [
          "toxic",
          "severe_toxic",
          "obscene",
          "threat",
          "insult",
          "identity_hate",
      ]
      metric = "acc"
  
      processor = TextClassificationProcessor(
          tokenizer=tokenizer,
          max_seq_len=128,
          data_dir=Path("../data/toxic-comments"),
          label_list=label_list,
          label_column_name="label",
          metric=metric,
          quote_char='"',
          multilabel=True,
          train_filename="train.tsv",
          dev_filename="val.tsv",
          test_filename=None,
          dev_split=0,
      )
  
      # 3. Create a DataSilo that loads several datasets (train/dev/test), provides DataLoaders for them and calculates a few descriptive statistics of our datasets
      data_silo = DataSilo(processor=processor, batch_size=batch_size)
  
      # 4. Create an AdaptiveModel
      # a) which consists of a pretrained language model as a basis
      language_model = LanguageModel.load(lang_model)
      # b) and a prediction head on top that is suited for our task => Text classification
      prediction_head = MultiLabelTextClassificationHead(
          num_labels=len(label_list)
      )
  
      model = AdaptiveModel(
          language_model=language_model,
          prediction_heads=[prediction_head],
          embeds_dropout_prob=0.1,
          lm_output_types=["per_sequence"],
          device=device,
      )
  
      # 5. Create an optimizer
      model, optimizer, lr_schedule = initialize_optimizer(
          model=model,
          learning_rate=3e-5,
          device=device,
          n_batches=len(data_silo.loaders["train"]),
          n_epochs=n_epochs,
      )
  
      # 6. Feed everything to the Trainer, which keeps care of growing our model into powerful plant and evaluates it from time to time
      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,
      )
  
      # 7. Let it grow
      trainer.train()
  
      # 8. Hooray! You have a model. Store it:
      save_dir = Path("../saved_models/bert-german-multi-doc-tutorial")
      model.save(save_dir)
      processor.save(save_dir)
  
      # 9. Load it & harvest your fruits (Inference)
      basic_texts = [
          {"text": "You fucking bastards"},
          {"text": "What a lovely world"},
      ]
      model = Inferencer.load(save_dir)
      result = model.inference_from_dicts(dicts=basic_texts)
      print(result)
      model.close_multiprocessing_pool()
  
file_path: FARM/examples/doc_classification_multilabel.py
imports: import logging
  from pathlib import Path
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import TextClassificationProcessor
  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 MultiLabelTextClassificationHead
  from farm.modeling.tokenization import Tokenizer
  from farm.train import Trainer
  from farm.utils import set_all_seeds, MLFlowLogger, initialize_device_settings
label_desc: Use image node_id 1 for calling a global function with example usage: doc_classification_multilabel() without return types