text: import os
  
  import pandas as pd
  import pytest
  
  from simpletransformers.language_modeling import LanguageModelingModel
  
  
  @pytest.mark.parametrize(
      "model_type, model_name",
      [
          ("bert", "bert-base-uncased"),
          ("longformer", "allenai/longformer-base-4096"),
          ("bert", None),
          ("electra", None),
          ("longformer", None),
          # ("xlnet", "xlnet-base-cased"),
          # ("xlm", "xlm-mlm-17-1280"),
          ("roberta", "roberta-base"),
          # ("distilbert", "distilbert-base-uncased"),
          # ("albert", "albert-base-v1"),
          # ("camembert", "camembert-base"),
          # ("xlmroberta", "xlm-roberta-base"),
          # ("flaubert", "flaubert-base-cased"),
      ],
  )
  def test_language_modeling(model_type, model_name):
      with open("train.txt", "w") as f:
          for i in range(100):
              f.writelines("Hello world with Simple Transformers! \n")
  
      if model_type == "electra":
          model_args = {
              "reprocess_input_data": True,
              "overwrite_output_dir": True,
              "num_train_epochs": 1,
              "no_save": True,
              "vocab_size": 100,
              "generator_config": {
                  "embedding_size": 512,
                  "hidden_size": 256,
                  "num_hidden_layers": 1,
              },
              "discriminator_config": {
                  "embedding_size": 512,
                  "hidden_size": 256,
                  "num_hidden_layers": 2,
              },
          }
      else:
          model_args = {
              "reprocess_input_data": True,
              "overwrite_output_dir": True,
              "num_train_epochs": 1,
              "no_save": True,
          }
          if model_name is None:
              model_args["vocab_size"] = 100
  
      model = LanguageModelingModel(
          model_type,
          model_name,
          args=model_args,
          train_files="train.txt",
          use_cuda=False,
      )
  
      # Train the model
      model.train_model("train.txt")
  