AlmogMor345/my_awesome_model

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: 1.3321
Validation Loss: 1.3458
Train Precision: 0.0
Train Recall: 0.0
Train F1: 0.0
Train Accuracy: 0.9140
Epoch: 99

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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Epoch
2.6690	2.1130	0.9140	0
1.8194	1.5602	0.9140	1
1.3472	1.3458	0.9140	2
1.2568	1.3458	0.9140	3
1.3789	1.3458	0.9140	4
1.2069	1.3458	0.9140	5
1.2804	1.3458	0.9140	6
1.2624	1.3458	0.9140	7
1.3139	1.3458	0.9140	8
1.2605	1.3458	0.9140	9
1.2763	1.3458	0.9140	10
1.2969	1.3458	0.9140	11
1.2780	1.3458	0.9140	12
1.1983	1.3458	0.9140	13
1.2138	1.3458	0.9140	14
1.2663	1.3458	0.9140	15
1.2843	1.3458	0.9140	16
1.2251	1.3458	0.9140	17
1.3197	1.3458	0.9140	18
1.2989	1.3458	0.9140	19
1.2213	1.3458	0.9140	20
1.2360	1.3458	0.9140	21
1.2389	1.3458	0.9140	22
1.2087	1.3458	0.9140	23
1.2446	1.3458	0.9140	24
1.2931	1.3458	0.9140	25
1.2623	1.3458	0.9140	26
1.2253	1.3458	0.9140	27
1.2853	1.3458	0.9140	28
1.3132	1.3458	0.9140	29
1.2183	1.3458	0.9140	30
1.2482	1.3458	0.9140	31
1.2190	1.3458	0.9140	32
1.3112	1.3458	0.9140	33
1.2852	1.3458	0.9140	34
1.2445	1.3458	0.9140	35
1.2528	1.3458	0.9140	36
1.2339	1.3458	0.9140	37
1.2053	1.3458	0.9140	38
1.2210	1.3458	0.9140	39
1.2258	1.3458	0.9140	40
1.2772	1.3458	0.9140	41
1.1695	1.3458	0.9140	42
1.2562	1.3458	0.9140	43
1.2413	1.3458	0.9140	44
1.2390	1.3458	0.9140	45
1.3280	1.3458	0.9140	46
1.2614	1.3458	0.9140	47
1.2350	1.3458	0.9140	48
1.3510	1.3458	0.9140	49
1.3331	1.3458	0.9140	50
1.1755	1.3458	0.9140	51
1.2463	1.3458	0.9140	52
1.3322	1.3458	0.9140	53
1.1857	1.3458	0.9140	54
1.3005	1.3458	0.9140	55
1.2379	1.3458	0.9140	56
1.2763	1.3458	0.9140	57
1.2821	1.3458	0.9140	58
1.2670	1.3458	0.9140	59
1.3589	1.3458	0.9140	60
1.3354	1.3458	0.9140	61
1.2851	1.3458	0.9140	62
1.2417	1.3458	0.9140	63
1.2591	1.3458	0.9140	64
1.2056	1.3458	0.9140	65
1.2569	1.3458	0.9140	66
1.3113	1.3458	0.9140	67
1.2131	1.3458	0.9140	68
1.2395	1.3458	0.9140	69
1.2507	1.3458	0.9140	70
1.3242	1.3458	0.9140	71
1.2997	1.3458	0.9140	72
1.2895	1.3458	0.9140	73
1.3044	1.3458	0.9140	74
1.2696	1.3458	0.9140	75
1.2138	1.3458	0.9140	76
1.2914	1.3458	0.9140	77
1.1968	1.3458	0.9140	78
1.3639	1.3458	0.9140	79
1.2451	1.3458	0.9140	80
1.2949	1.3458	0.9140	81
1.2724	1.3458	0.9140	82
1.3940	1.3458	0.9140	83
1.3156	1.3458	0.9140	84
1.3080	1.3458	0.9140	85
1.2519	1.3458	0.9140	86
1.2222	1.3458	0.9140	87
1.2304	1.3458	0.9140	88
1.2843	1.3458	0.9140	89
1.2523	1.3458	0.9140	90
1.2531	1.3458	0.9140	91
1.2974	1.3458	0.9140	92
1.3301	1.3458	0.9140	93
1.2726	1.3458	0.9140	94
1.3171	1.3458	0.9140	95
1.3577	1.3458	0.9140	96
1.2373	1.3458	0.9140	97
1.2556	1.3458	0.9140	98
1.3321	1.3458	0.9140	99

Framework versions

Transformers 4.28.1
TensorFlow 2.12.0
Datasets 2.11.0
Tokenizers 0.13.3

AlmogMor345/my_awesome_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js