def test_tc_test():
happy_tc = HappyTextClassification(
model_type="DISTILBERT",
model_name="distilbert-base-uncased-finetuned-sst-2-english")
result = happy_tc.test("../data/tc/test.csv")
answer = [
TextClassificationResult(label='POSITIVE', score=0.9998401999473572),
TextClassificationResult(label='NEGATIVE', score=0.9772131443023682),
TextClassificationResult(label='NEGATIVE', score=0.9966067671775818),
TextClassificationResult(label='POSITIVE', score=0.9792295098304749)
]
assert result == answer
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 example_2_4():
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.test("../../data/tc/test.csv")
print(type(result)) # <class 'list'>
print(
result
) # [TextClassificationResult(label='LABEL_1', score=0.9998401999473572), TextClassificationResult(label='LABEL_0', score=0.9772131443023682)...
print(
type(result[0])
) # <class 'happytransformer.happy_text_classification.TextClassificationResult'>
print(
result[0]
) # TextClassificationResult(label='LABEL_1', score=0.9998401999473572)
print(result[0].label) # LABEL_1