sevvalkapcak/newModel2

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

• Train Loss: 0.0138
• Validation Loss: 0.6631
• Train Accuracy: 0.9225
• Epoch: 89

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

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': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Epoch
0.2465	0.2029	0.9085	0
0.1354	0.1302	0.939	1
0.1121	0.1588	0.934	2
0.0945	0.1551	0.937	3
0.0815	0.1696	0.939	4
0.0778	0.1647	0.932	5
0.0522	0.2356	0.931	6
0.0444	0.2861	0.9335	7
0.0329	0.2144	0.9355	8
0.0290	0.2548	0.935	9
0.0222	0.2866	0.93	10
0.0256	0.2787	0.9385	11
0.0267	0.2764	0.941	12
0.0201	0.2888	0.9315	13
0.0221	0.2737	0.934	14
0.0174	0.4403	0.93	15
0.0170	0.2836	0.932	16
0.0214	0.3033	0.9375	17
0.0125	0.3894	0.934	18
0.0271	0.3687	0.9305	19
0.0154	0.3817	0.9305	20
0.0149	0.4736	0.93	21
0.0196	0.4435	0.9325	22
0.0124	0.4873	0.929	23
0.0157	0.4008	0.932	24
0.0153	0.4074	0.931	25
0.0176	0.3996	0.9295	26
0.0160	0.3652	0.9355	27
0.0081	0.4446	0.934	28
0.0098	0.5249	0.934	29
0.0151	0.4112	0.937	30
0.0124	0.4888	0.929	31
0.0146	0.5022	0.9325	32
0.0130	0.5585	0.9305	33
0.0102	0.4304	0.935	34
0.0158	0.4239	0.933	35
0.0156	0.4849	0.93	36
0.0153	0.5097	0.9245	37
0.0135	0.4689	0.934	38
0.0178	0.4578	0.9285	39
0.0124	0.4083	0.9275	40
0.0106	0.4946	0.926	41
0.0098	0.4908	0.927	42
0.0131	0.5604	0.928	43
0.0143	0.4226	0.9315	44
0.0105	0.5664	0.9245	45
0.0189	0.5121	0.925	46
0.0148	0.5259	0.9245	47
0.0090	0.4567	0.9295	48
0.0156	0.4633	0.926	49
0.0128	0.5222	0.9295	50
0.0118	0.5461	0.921	51
0.0172	0.4626	0.927	52
0.0129	0.5266	0.922	53
0.0159	0.5203	0.925	54
0.0106	0.5360	0.9265	55
0.0158	0.4766	0.9305	56
0.0106	0.5630	0.926	57
0.0142	0.6162	0.922	58
0.0137	0.5518	0.916	59
0.0083	0.6281	0.9155	60
0.0071	0.6263	0.9245	61
0.0116	0.6166	0.9235	62
0.0162	0.5217	0.9195	63
0.0158	0.6366	0.9215	64
0.0120	0.5511	0.9245	65
0.0093	0.4895	0.9225	66
0.0094	0.5207	0.9255	67
0.0067	0.6252	0.9275	68
0.0058	0.6934	0.9235	69
0.0055	0.6577	0.928	70
0.0073	0.5865	0.9255	71
0.0336	0.4875	0.9175	72
0.0177	0.5256	0.923	73
0.0143	0.5042	0.917	74
0.0076	0.6803	0.9225	75
0.0114	0.5571	0.9205	76
0.0118	0.5649	0.9235	77
0.0147	0.5592	0.9245	78
0.0109	0.6044	0.9195	79
0.0095	0.6940	0.921	80
0.0139	0.6246	0.9245	81
0.0145	0.7057	0.917	82
0.0147	0.6455	0.9155	83
0.0100	0.6044	0.922	84
0.0074	0.6786	0.92	85
0.0093	0.7300	0.9125	86
0.0152	0.6264	0.9205	87
0.0115	0.6208	0.915	88
0.0138	0.6631	0.9225	89

Framework versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.16.1
• Tokenizers 0.15.1