def test_tc_train_effectiveness(): """assert that training decreases the loss""" happy_tc = HappyTextClassification(model_type="DISTILBERT", model_name="distilbert-base-uncased") before_loss = happy_tc.eval("../data/tc/train-eval.csv").loss happy_tc.train("../data/tc/train-eval.csv") after_loss = happy_tc.eval("../data/tc/train-eval.csv").loss assert after_loss < before_loss
def test_tc_train_effectiveness_multi(): happy_tc = HappyTextClassification(model_type="DISTILBERT", model_name="distilbert-base-uncased", num_labels=3) before_loss = happy_tc.eval("../data/tc/train-eval-multi.csv").loss happy_tc.train("../data/tc/train-eval-multi.csv") after_loss = happy_tc.eval("../data/tc/train-eval-multi.csv").loss assert after_loss < before_loss
def example_2_5(): happy_tc = HappyTextClassification( model_type="DISTILBERT", model_name="distilbert-base-uncased-finetuned-sst-2-english", num_labels=2) # Don't forget to set num_labels! before_loss = happy_tc.eval("../../data/tc/train-eval.csv").loss happy_tc.train("../../data/tc/train-eval.csv") after_loss = happy_tc.eval("../../data/tc/train-eval.csv").loss print("Before loss: ", before_loss) # 0.007262040860950947 print("After loss: ", after_loss) # 0.000162081079906784
def test_tc_with_dataclass(): happy_tc = HappyTextClassification() train_args = TCTrainArgs(learning_rate=0.01, num_train_epochs=1) happy_tc.train("../data/tc/train-eval.csv", args=train_args) eval_args = TCEvalArgs() result_eval = happy_tc.eval("../data/tc/train-eval.csv", args=eval_args) test_args = TCTestArgs() result_test = happy_tc.test("../data/tc/test.csv", args=test_args)
def test_tc_with_dic(): happy_tc = HappyTextClassification() train_args = {'learning_rate': 0.01, "num_train_epochs": 1} happy_tc.train("../data/tc/train-eval.csv", args=train_args) eval_args = {} result_eval = happy_tc.eval("../data/tc/train-eval.csv", args=eval_args) test_args = {} result_test = happy_tc.test("../data/tc/test.csv", args=test_args)
def test_tc_train(): happy_tc = HappyTextClassification( model_type="DISTILBERT", model_name="distilbert-base-uncased-finetuned-sst-2-english") results = happy_tc.train("../data/tc/train-eval.csv")
def example_2_2(): happy_tc = HappyTextClassification( model_type="DISTILBERT", model_name="distilbert-base-uncased-finetuned-sst-2-english", num_labels=2) # Don't forget to set num_labels! happy_tc.train("../../data/tc/train-eval.csv")