code: def lm_finetuning():
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      next_sent_pred_style = "bert-style"
      next_sent_pred = True
      set_all_seeds(seed=42)
      ml_logger = MLFlowLogger(
          tracking_uri="https://public-mlflow.deepset.ai/"
      )
      ml_logger.init_experiment(
          experiment_name="LM_refactoring",
          run_name=f"new, nsp: {next_sent_pred}, {next_sent_pred_style}",
      )
      ##########################
      ########## Settings
      ##########################
      device, n_gpu = initialize_device_settings(use_cuda=True)
      n_epochs = 1
      batch_size = 32
      evaluate_every = 1000
      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
      processor = BertStyleLMProcessor(
          data_dir=Path("../data/lm_finetune_nips"),
          tokenizer=tokenizer,
          max_seq_len=128,
          max_docs=None,  # You can have set max_docs here to limit the number of docs in the dataset and speed up this example
          next_sent_pred_style=next_sent_pred_style,
      )
  
      # 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,
          max_multiprocessing_chunksize=20,
      )
  
      # 4. Create an AdaptiveModel
      # a) which consists of a pretrained language model as a basis
      language_model = LanguageModel.load(lang_model)
      # b) and *two* prediction heads on top that are suited for our task => Language Model finetuning
      lm_prediction_head = BertLMHead.load(lang_model)
      next_sentence_head = NextSentenceHead.load(lang_model)
  
      model = AdaptiveModel(
          language_model=language_model,
          prediction_heads=[lm_prediction_head, next_sentence_head],
          embeds_dropout_prob=0.1,
          lm_output_types=["per_token", "per_sequence"],
          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,
          eval_report=False,
      )
  
      # 7. Let it grow! Watch the tracked metrics live on the public mlflow server: https://public-mlflow.deepset.ai
      trainer.train()
  
      # 8. Hooray! You have a model. Store it:
      save_dir = Path("saved_models/bert-english-lm-tutorial")
      model.save(save_dir)
      processor.save(save_dir)
  
file_path: FARM/examples/lm_finetuning.py
imports: import logging
  from pathlib import Path
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import BertStyleLMProcessor
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import BertLMHead, NextSentenceHead
  from farm.modeling.tokenization import Tokenizer
  from farm.train import Trainer
  from farm.modeling.optimization import initialize_optimizer
  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: lm_finetuning() without return types