tarsssss/eng-jagoy-t5-001

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: 4.7399
• Validation Loss: 5.1356
• Epoch: 138

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
7.8603	7.4105	0
7.3775	7.1273	1
7.1632	6.9598	2
7.0228	6.8372	3
6.9085	6.7335	4
6.8226	6.6458	5
6.7451	6.5671	6
6.6785	6.5022	7
6.6254	6.4409	8
6.5606	6.3842	9
6.5163	6.3361	10
6.4682	6.2908	11
6.4250	6.2436	12
6.3749	6.1907	13
6.3293	6.1494	14
6.2822	6.1098	15
6.2560	6.0750	16
6.2078	6.0508	17
6.1839	6.0229	18
6.1561	5.9944	19
6.1146	5.9732	20
6.0885	5.9490	21
6.0587	5.9243	22
6.0366	5.9064	23
6.0135	5.8857	24
5.9904	5.8675	25
5.9681	5.8482	26
5.9473	5.8262	27
5.9263	5.8127	28
5.9031	5.7896	29
5.8827	5.7721	30
5.8566	5.7482	31
5.8406	5.7355	32
5.8285	5.7231	33
5.7944	5.7049	34
5.7822	5.6968	35
5.7567	5.6813	36
5.7526	5.6650	37
5.7363	5.6614	38
5.7132	5.6398	39
5.6945	5.6383	40
5.6786	5.6243	41
5.6636	5.6071	42
5.6527	5.5955	43
5.6390	5.5876	44
5.6198	5.5754	45
5.6082	5.5663	46
5.6070	5.5572	47
5.5782	5.5493	48
5.5679	5.5487	49
5.5520	5.5301	50
5.5307	5.5261	51
5.5284	5.5089	52
5.5160	5.5003	53
5.4976	5.4981	54
5.4864	5.4860	55
5.4795	5.4816	56
5.4653	5.4652	57
5.4484	5.4639	58
5.4335	5.4580	59
5.4231	5.4454	60
5.4132	5.4358	61
5.4064	5.4349	62
5.3886	5.4261	63
5.3913	5.4193	64
5.3692	5.4138	65
5.3556	5.4028	66
5.3469	5.4001	67
5.3421	5.3942	68
5.3194	5.3826	69
5.3243	5.3799	70
5.3081	5.3713	71
5.2921	5.3737	72
5.2845	5.3681	73
5.2754	5.3601	74
5.2594	5.3524	75
5.2527	5.3420	76
5.2496	5.3367	77
5.2360	5.3320	78
5.2193	5.3253	79
5.2141	5.3178	80
5.1993	5.3150	81
5.1923	5.3157	82
5.1875	5.3097	83
5.1776	5.3051	84
5.1693	5.3050	85
5.1533	5.3115	86
5.1567	5.2943	87
5.1348	5.2757	88
5.1317	5.2849	89
5.1191	5.2846	90
5.1102	5.2742	91
5.1054	5.2725	92
5.0944	5.2624	93
5.0906	5.2560	94
5.0712	5.2502	95
5.0719	5.2495	96
5.0628	5.2498	97
5.0597	5.2454	98
5.0402	5.2420	99
5.0308	5.2441	100
5.0193	5.2379	101
5.0198	5.2298	102
5.0110	5.2315	103
5.0087	5.2304	104
4.9906	5.2261	105
4.9883	5.2288	106
4.9818	5.2069	107
4.9612	5.2003	108
4.9560	5.2009	109
4.9453	5.2123	110
4.9385	5.2136	111
4.9238	5.2178	112
4.9291	5.1994	113
4.9097	5.1940	114
4.9093	5.1840	115
4.9057	5.1824	116
4.8907	5.1894	117
4.8919	5.1841	118
4.8699	5.1806	119
4.8671	5.1795	120
4.8629	5.1696	121
4.8552	5.1646	122
4.8414	5.1709	123
4.8444	5.1534	124
4.8330	5.1698	125
4.8231	5.1501	126
4.8198	5.1565	127
4.8004	5.1522	128
4.7996	5.1478	129
4.7915	5.1409	130
4.7845	5.1484	131
4.7837	5.1476	132
4.7727	5.1446	133
4.7729	5.1379	134
4.7628	5.1379	135
4.7568	5.1359	136
4.7400	5.1292	137
4.7399	5.1356	138

Framework versions

• Transformers 4.33.2
• TensorFlow 2.10.0
• Datasets 2.15.0
• Tokenizers 0.13.3