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 example_2_3():
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!
result = happy_tc.eval("../../data/tc/train-eval.csv")
print(type(result)) # <class 'happytransformer.happy_trainer.EvalResult'>
print(result) # EvalResult(eval_loss=0.007262040860950947)
print(result.loss) # 0.007262040860950947
def example_2_3():
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!
args = TCEvalArgs(
save_preprocessed_data=False) # for demonstration -- not needed
result = happy_tc.eval("../../data/tc/train-eval.csv", args=args)
print(type(result)) # <class 'happytransformer.happy_trainer.EvalResult'>
print(result) # EvalResult(eval_loss=0.007262040860950947)
print(result.loss) # 0.007262040860950947
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_eval():
happy_tc = HappyTextClassification(
model_type="DISTILBERT",
model_name="distilbert-base-uncased-finetuned-sst-2-english")
results = happy_tc.eval("../data/tc/train-eval.csv")
assert results.loss == approx(0.007262040860950947, 0.01)