cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100 – AI Model by hsohn3 | AlphaNeural AI

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hsohn3/
cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100

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hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100

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

Train Loss: 0.8009
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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Epoch
3.8699	0
3.1667	1
3.1286	2
3.1169	3
3.1077	4
3.0989	5
3.0911	6
3.0896	7
3.0820	8
3.0856	9
3.0827	10
3.0800	11
3.0647	12
3.0396	13
3.0052	14
2.9879	15
2.9633	16
2.9449	17
2.9217	18
2.8921	19
2.8625	20
2.8153	21
2.7495	22
2.6202	23
2.3762	24
2.1064	25
1.8489	26
1.6556	27
1.5005	28
1.4110	29
1.3472	30
1.2896	31
1.2391	32
1.2001	33
1.1663	34
1.1418	35
1.1159	36
1.0987	37
1.0753	38
1.0608	39
1.0456	40
1.0381	41
1.0248	42
1.0127	43
0.9970	44
0.9958	45
0.9847	46
0.9789	47
0.9617	48
0.9575	49
0.9517	50
0.9442	51
0.9379	52
0.9350	53
0.9325	54
0.9235	55
0.9182	56
0.9139	57
0.9074	58
0.8984	59
0.8988	60
0.8958	61
0.8937	62
0.8853	63
0.8812	64
0.8758	65
0.8729	66
0.8732	67
0.8647	68
0.8634	69
0.8604	70
0.8577	71
0.8597	72
0.8508	73
0.8510	74
0.8450	75
0.8451	76
0.8398	77
0.8392	78
0.8345	79
0.8350	80
0.8329	81
0.8299	82
0.8257	83
0.8217	84
0.8192	85
0.8211	86
0.8208	87
0.8171	88
0.8166	89
0.8134	90
0.8124	91
0.8102	92
0.8133	93
0.8066	94
0.8023	95
0.8049	96
0.8024	97
0.7980	98
0.8009	99

Framework versions

Transformers 4.20.1
TensorFlow 2.8.2
Datasets 2.3.2
Tokenizers 0.12.1