def test_bertscore_order_invariant(self):
metric = BertScore()
self.assert_order_invariant(metric)
def test_bertscore_idf(self):
# This is a regression test, not necessarily a test for correctness
metric = BertScore(idf=True)
expected_output = [{
'bertscore': {
'precision': 0.8256407976150513,
'recall': 0.8295320272445679,
'f1': 0.8275818228721619
}
}, {
'bertscore': {
'precision': 0.828471302986145,
'recall': 0.8554045557975769,
'f1': 0.8417225480079651
}
}, {
'bertscore': {
'precision': 0.838839054107666,
'recall': 0.8432000279426575,
'f1': 0.8410139083862305
}
}, {
'bertscore': {
'precision': 0.831351637840271,
'recall': 0.8347938060760498,
'f1': 0.8330692052841187
}
}, {
'bertscore': {
'precision': 0.808471143245697,
'recall': 0.8274324536323547,
'f1': 0.8178418874740601
}
}, {
'bertscore': {
'precision': 0.8056685924530029,
'recall': 0.8246532678604126,
'f1': 0.8150504231452942
}
}, {
'bertscore': {
'precision': 0.8379611968994141,
'recall': 0.8408616781234741,
'f1': 0.8390330076217651
}
}, {
'bertscore': {
'precision': 0.8419498205184937,
'recall': 0.8478491902351379,
'f1': 0.8448891639709473
}
}, {
'bertscore': {
'precision': 0.8836315870285034,
'recall': 0.8468402624130249,
'f1': 0.8648448586463928
}
}, {
'bertscore': {
'precision': 0.8534250855445862,
'recall': 0.8621121644973755,
'f1': 0.8577466011047363
}
}, {
'bertscore': {
'precision': 0.8462548851966858,
'recall': 0.8589195013046265,
'f1': 0.852540135383606
}
}, {
'bertscore': {
'precision': 0.8441416621208191,
'recall': 0.8447036743164062,
'f1': 0.8444225788116455
}
}]
super().assert_expected_output(metric, expected_output)
def test_bertscore(self):
# This is a regression test, not necessarily a test for correctness
metric = BertScore()
expected_output = [{
'bertscore': {
'precision': 0.8534530401229858,
'recall': 0.8503388166427612,
'f1': 0.8518930673599243
}
}, {
'bertscore': {
'precision': 0.8642909526824951,
'recall': 0.8720692992210388,
'f1': 0.8681626319885254
}
}, {
'bertscore': {
'precision': 0.8623127341270447,
'recall': 0.8670819997787476,
'f1': 0.8646908402442932
}
}, {
'bertscore': {
'precision': 0.8549671173095703,
'recall': 0.856684148311615,
'f1': 0.8558247685432434
}
}, {
'bertscore': {
'precision': 0.8397505283355713,
'recall': 0.8507593870162964,
'f1': 0.8451763987541199
}
}, {
'bertscore': {
'precision': 0.8410190343856812,
'recall': 0.8510528206825256,
'f1': 0.8460061550140381
}
}, {
'bertscore': {
'precision': 0.8653707504272461,
'recall': 0.8665218949317932,
'f1': 0.8659459948539734
}
}, {
'bertscore': {
'precision': 0.868201494216919,
'recall': 0.8704793453216553,
'f1': 0.8693389296531677
}
}, {
'bertscore': {
'precision': 0.9007625579833984,
'recall': 0.8653964400291443,
'f1': 0.8827253580093384
}
}, {
'bertscore': {
'precision': 0.8833473920822144,
'recall': 0.8772095441818237,
'f1': 0.8802677989006042
}
}, {
'bertscore': {
'precision': 0.8683465719223022,
'recall': 0.8797310590744019,
'f1': 0.8740017414093018
}
}, {
'bertscore': {
'precision': 0.8667279481887817,
'recall': 0.8681085109710693,
'f1': 0.8665944337844849
}
}]
super().assert_expected_output(metric, expected_output)