pijarcandra22/t5Sunda2Indo

This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 1.6406
• Validation Loss: 1.5932
• Epoch: 140

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	Validation Loss	Epoch
3.9668	3.4054	0
3.5451	3.1460	1
3.3386	2.9773	2
3.1917	2.8549	3
3.0808	2.7568	4
2.9921	2.6780	5
2.9187	2.6135	6
2.8536	2.5547	7
2.8010	2.5040	8
2.7482	2.4601	9
2.7056	2.4227	10
2.6709	2.3870	11
2.6325	2.3554	12
2.6016	2.3233	13
2.5685	2.2965	14
2.5407	2.2710	15
2.5158	2.2486	16
2.4905	2.2248	17
2.4680	2.2037	18
2.4459	2.1856	19
2.4257	2.1684	20
2.4045	2.1495	21
2.3861	2.1325	22
2.3676	2.1189	23
2.3479	2.1028	24
2.3333	2.0873	25
2.3192	2.0743	26
2.3035	2.0647	27
2.2889	2.0504	28
2.2742	2.0395	29
2.2608	2.0285	30
2.2471	2.0166	31
2.2344	2.0078	32
2.2211	1.9999	33
2.2086	1.9893	34
2.1965	1.9790	35
2.1879	1.9724	36
2.1774	1.9637	37
2.1663	1.9537	38
2.1573	1.9461	39
2.1470	1.9389	40
2.1344	1.9329	41
2.1259	1.9257	42
2.1192	1.9158	43
2.1098	1.9092	44
2.0993	1.9021	45
2.0930	1.8970	46
2.0831	1.8909	47
2.0729	1.8845	48
2.0669	1.8799	49
2.0587	1.8746	50
2.0519	1.8662	51
2.0446	1.8605	52
2.0338	1.8552	53
2.0297	1.8494	54
2.0207	1.8441	55
2.0151	1.8404	56
2.0116	1.8346	57
2.0029	1.8286	58
1.9942	1.8243	59
1.9894	1.8177	60
1.9820	1.8145	61
1.9753	1.8100	62
1.9698	1.8054	63
1.9635	1.8001	64
1.9588	1.7963	65
1.9533	1.7895	66
1.9443	1.7888	67
1.9381	1.7846	68
1.9320	1.7806	69
1.9281	1.7755	70
1.9232	1.7697	71
1.9141	1.7672	72
1.9128	1.7655	73
1.9041	1.7611	74
1.8987	1.7558	75
1.8955	1.7498	76
1.8874	1.7493	77
1.8845	1.7433	78
1.8793	1.7403	79
1.8723	1.7370	80
1.8669	1.7319	81
1.8626	1.7323	82
1.8593	1.7268	83
1.8540	1.7235	84
1.8504	1.7204	85
1.8468	1.7180	86
1.8398	1.7130	87
1.8358	1.7088	88
1.8321	1.7081	89
1.8271	1.7042	90
1.8203	1.7016	91
1.8189	1.6985	92
1.8117	1.6967	93
1.8089	1.6924	94
1.8035	1.6898	95
1.7984	1.6904	96
1.7957	1.6836	97
1.7925	1.6833	98
1.7880	1.6786	99
1.7816	1.6770	100
1.7772	1.6739	101
1.7754	1.6733	102
1.7712	1.6678	103
1.7677	1.6638	104
1.7641	1.6627	105
1.7601	1.6609	106
1.7573	1.6585	107
1.7516	1.6559	108
1.7478	1.6523	109
1.7443	1.6523	110
1.7406	1.6498	111
1.7361	1.6475	112
1.7326	1.6435	113
1.7285	1.6422	114
1.7244	1.6398	115
1.7205	1.6386	116
1.7187	1.6347	117
1.7157	1.6335	118
1.7113	1.6317	119
1.7094	1.6308	120
1.7074	1.6267	121
1.7007	1.6252	122
1.6965	1.6241	123
1.6931	1.6231	124
1.6913	1.6201	125
1.6862	1.6174	126
1.6833	1.6176	127
1.6827	1.6122	128
1.6774	1.6127	129
1.6726	1.6119	130
1.6698	1.6079	131
1.6663	1.6077	132
1.6631	1.6055	133
1.6617	1.6043	134
1.6573	1.6019	135
1.6549	1.5994	136
1.6514	1.5990	137
1.6484	1.5965	138
1.6490	1.5942	139
1.6406	1.5932	140

Framework versions

• Transformers 4.35.2
• TensorFlow 2.14.0
• Datasets 2.15.0
• Tokenizers 0.15.0