t5Indo2Sunda

pijarcandra22/t5Indo2Sunda

t5Indo2Sunda is a machine learning model from pijarcandra22. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Dec 3, 2023
Updated: Dec 3, 2023
SHA: 330a2bde

Base models

Model card

From the Hugging Face model README

pijarcandra22/t5Indo2Sunda

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: 2.1941
Validation Loss: 2.1230
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	Validation Loss	Epoch
4.3724	3.9124	0
3.9887	3.6348	1
3.7534	3.4215	2
3.5819	3.2847	3
3.4632	3.1902	4
3.3751	3.1139	5
3.3039	3.0493	6
3.2447	2.9955	7
3.1911	2.9481	8
3.1455	2.9082	9
3.1068	2.8718	10
3.0697	2.8387	11
3.0381	2.8105	12
3.0050	2.7825	13
2.9796	2.7568	14
2.9510	2.7350	15
2.9259	2.7096	16
2.9053	2.6881	17
2.8833	2.6696	18
2.8599	2.6510	19
2.8403	2.6328	20
2.8207	2.6171	21
2.8046	2.5999	22
2.7861	2.5857	23
2.7715	2.5699	24
2.7557	2.5542	25
2.7387	2.5420	26
2.7225	2.5299	27
2.7085	2.5182	28
2.6950	2.5081	29
2.6818	2.4951	30
2.6687	2.4864	31
2.6578	2.4760	32
2.6461	2.4651	33
2.6334	2.4559	34
2.6213	2.4477	35
2.6096	2.4373	36
2.5993	2.4297	37
2.5906	2.4208	38
2.5778	2.4100	39
2.5703	2.4025	40
2.5594	2.3962	41
2.5521	2.3901	42
2.5414	2.3808	43
2.5318	2.3726	44
2.5235	2.3684	45
2.5165	2.3592	46
2.5060	2.3507	47
2.4972	2.3466	48
2.4892	2.3388	49
2.4807	2.3325	50
2.4732	2.3281	51
2.4654	2.3210	52
2.4592	2.3138	53
2.4525	2.3100	54
2.4439	2.3046	55
2.4349	2.2980	56
2.4283	2.2926	57
2.4222	2.2884	58
2.4139	2.2824	59
2.4071	2.2759	60
2.4008	2.2705	61
2.3941	2.2664	62
2.3882	2.2588	63
2.3813	2.2566	64
2.3759	2.2498	65
2.3674	2.2461	66
2.3618	2.2425	67
2.3534	2.2377	68
2.3522	2.2314	69
2.3398	2.2269	70
2.3391	2.2241	71
2.3303	2.2184	72
2.3275	2.2137	73
2.3190	2.2100	74
2.3159	2.2048	75
2.3078	2.2011	76
2.3048	2.1971	77
2.3005	2.1936	78
2.2938	2.1899	79
2.2892	2.1859	80
2.2824	2.1819	81
2.2758	2.1787	82
2.2739	2.1757	83
2.2689	2.1716	84
2.2623	2.1664	85
2.2574	2.1657	86
2.2537	2.1618	87
2.2483	2.1563	88
2.2407	2.1554	89
2.2387	2.1510	90
2.2351	2.1469	91
2.2286	2.1436	92
2.2226	2.1413	93
2.2171	2.1395	94
2.2159	2.1342	95
2.2109	2.1314	96
2.2041	2.1284	97
2.1999	2.1260	98
2.1941	2.1230	99

Framework versions

Transformers 4.35.2
TensorFlow 2.14.0
Datasets 2.15.0
Tokenizers 0.15.0

Library: transformers
Type: t5
License: apache-2.0
Created: Dec 3, 2023
Updated: Dec 3, 2023