code: def test_qa_train_effectiveness():
      """
      Ensures that HappyQuestionAnswering.train() results in
      lowering the loss as determined by HappyQuestionAnswering.eval()
      """
      # use a non-fine-tuned model so we DEFINITELY get an improvement
      happy = HappyQuestionAnswering("BERT", "bert-base-uncased")
      args = QATrainArgs(num_train_epochs=3)
      before_loss = happy.eval("../data/qa/train-eval.csv").loss
      happy.train("../data/qa/train-eval.csv", args=args)
      after_loss = happy.eval("../data/qa/train-eval.csv").loss
  
      assert after_loss < before_loss
  
file_path: happy-transformer/tests/test_qa.py
imports: from happytransformer.happy_question_answering import HappyQuestionAnswering, QATrainArgs, QAEvalArgs, QATestArgs
  import pytest
  from pytest import approx
  from tests import happy_qa
label_desc: Use image node_id 5 for calling a global function with example usage: test_qa_train_effectiveness() without return types