code: def text_pair_classification():
      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_text_pair_classification",
      )
  
      ##########################
      ########## Settings ######
      ##########################
      set_all_seeds(seed=42)
      device, n_gpu = initialize_device_settings(use_cuda=True)
      n_epochs = 2
      batch_size = 64
      evaluate_every = 500
      lang_model = "bert-base-cased"
      label_list = ["0", "1"]
  
      # 1.Create a tokenizer
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path=lang_model, do_lower_case=False
      )
  
      # 2. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset.
      # The TextPairClassificationProcessor expects a csv with columns called "text', "text_b" and "label"
      processor = TextPairClassificationProcessor(
          tokenizer=tokenizer,
          label_list=label_list,
          metric="f1_macro",
          max_seq_len=128,
          train_filename="train.tsv",
          dev_filename="dev.tsv",
          test_filename=None,
          data_dir=Path("../data/asnq_binary"),
          delimiter="\t",
      )
  
      # 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
      prediction_head = TextClassificationHead(
          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_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/text_pair_classification_model")
      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
      # For correct Text Pair Classification on raw dictionaries (inference mode), we need to put both
      # texts (text, text_b) into a tuple.
      # See corresponding conversion in the file_to_dicts() method of TextPairClassificationProcessor: https://github.com/deepset-ai/FARM/blob/5ab5b1620cb51ceb874d4b30c887e377ad1a6e9a/farm/data_handler/processor.py#L744
      basic_texts = [
          {
              "text": (
                  "how many times have real madrid won the champions league in a row",
                  "They have also won the competition the most times in a row, winning it five times from 1956 to 1960",
              )
          },
          {
              "text": (
                  "how many seasons of the blacklist are there on netflix",
                  "Retrieved March 27 , 2018 .",
              )
          },
      ]
  
      model = Inferencer.load(save_dir)
      result = model.inference_from_dicts(dicts=basic_texts)
  
      print(result)
      model.close_multiprocessing_pool()
  
file_path: FARM/examples/text_pair_classification.py
imports: import logging
  from pathlib import Path
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import TextPairClassificationProcessor
  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 TextClassificationHead
  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: text_pair_classification() without return types