code: def doc_regression():
      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_regression"
      )
  
      ##########################
      ########## Settings
      ##########################
      set_all_seeds(seed=42)
      device, n_gpu = initialize_device_settings(use_cuda=True)
      n_epochs = 5
      batch_size = 32
      evaluate_every = 30
      lang_model = "bert-base-cased"
      do_lower_case = False
  
      # 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
      #    We do not have a sample dataset for regression yet, add your own dataset to run the example
      processor = RegressionProcessor(
          tokenizer=tokenizer,
          max_seq_len=128,
          data_dir=Path("../data/<YOUR-DATASET>"),
          label_column_name="label",
      )
  
      # 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 regression
      prediction_head = RegressionHead()
  
      model = AdaptiveModel(
          language_model=language_model,
          prediction_heads=[prediction_head],
          embeds_dropout_prob=0.1,
          lm_output_types=["per_sequence_continuous"],
          device=device,
      )
  
      # 5. Create an optimizer
      model, optimizer, lr_schedule = initialize_optimizer(
          model=model,
          learning_rate=2e-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-doc-regression-tutorial")
      model.save(save_dir)
      processor.save(save_dir)
  
      # 9. Load it & harvest your fruits (Inference)
      #    Add your own text adapted to the dataset you provide
      basic_texts = [
          {"text": ""},
          {"text": ""},
      ]
      model = Inferencer.load(save_dir)
      result = model.inference_from_dicts(dicts=basic_texts)
  
      print(result)
      model.close_multiprocessing_pool()
  
file_path: FARM/examples/doc_regression.py
imports: import logging
  from pathlib import Path
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import RegressionProcessor
  from farm.experiment 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 RegressionHead
  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_regression() without return types