GyanShashwat/distilbert-base-uncased-finetuned-test-data

This model is a fine-tuned version of GyanShashwat/distilbert-base-uncased-finetuned-test-data on an unknown dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.01, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Train Loss	Train End Logits Accuracy	Train Start Logits Accuracy	Epoch
6.5953	0.0	0.0	0
6.0959	0.0	0.0	1
6.0750	0.0	0.1429	2
6.2449	0.0	0.0	3
6.6021	0.0	0.0	4
6.4264	0.0	0.0	5
6.6183	0.0	0.0	6
6.4572	0.0	0.0	7
6.2062	0.0	0.0	8
6.3750	0.0	0.0	9
6.4880	0.0	0.0	10
6.6889	0.0	0.0	11
6.0914	0.0	0.0	12
6.0446	0.0	0.0	13
6.8131	0.0	0.0	14
6.9439	0.0	0.0	15
6.0789	0.0	0.0	16
6.3060	0.0	0.0	17
6.1862	0.0	0.0	18
6.4202	0.0	0.0	19
6.0899	0.0	0.0	20
6.4460	0.0	0.0	21
6.0554	0.0	0.0	22
6.1655	0.0	0.0	23
6.3298	0.0	0.0	24
6.1062	0.0	0.0	25
6.2737	0.0	0.0	26
6.1412	0.0	0.0	27
6.2286	0.0	0.0	28
6.2041	0.0	0.0	29
6.7055	0.0	0.0	30
6.2596	0.0	0.0	31
6.7166	0.0	0.0	32
6.1891	0.0	0.0	33
6.1920	0.0	0.0	34
6.2608	0.0	0.0	35
6.0968	0.0	0.0	36
6.6072	0.0	0.0	37
6.2966	0.0	0.0	38
6.4528	0.0	0.0	39
6.5660	0.0	0.0	40
6.3345	0.0	0.0	41
6.1812	0.0	0.0	42
6.1986	0.0	0.0	43
6.2477	0.0	0.0	44
6.2783	0.0	0.0	45
6.7758	0.0	0.0	46
6.0984	0.0	0.0	47
6.1547	0.0	0.0	48
6.1153	0.0	0.0	49
6.2574	0.0	0.0	50
5.9857	0.0	0.0	51
6.1978	0.0	0.0	52
6.4674	0.0	0.0	53
6.0991	0.0	0.0	54
6.2534	0.0	0.0	55
6.1088	0.0	0.0	56
5.8161	0.0	0.0	57
5.9146	0.0	0.0	58
6.2400	0.0	0.0	59
6.2602	0.1429	0.0	60
6.0889	0.0	0.0	61
6.2283	0.0	0.0	62
6.4321	0.0	0.0	63
6.6588	0.0	0.0	64
6.2557	0.0	0.0	65
6.2958	0.0	0.0	66
6.1113	0.0	0.0	67
6.3594	0.0	0.0	68
5.9983	0.0	0.0	69
6.0230	0.0	0.1429	70
6.1085	0.0	0.0	71
6.3313	0.0	0.0	72
6.4739	0.0	0.0	73
6.1131	0.0	0.0	74
6.0539	0.0	0.0	75