pijarcandra22/t5Jawa2Indo

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: 0.9572
• Validation Loss: 1.1659
• Epoch: 299

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
3.8958	3.3598	0
3.4684	3.0863	1
3.2505	2.9092	2
3.0952	2.7813	3
2.9749	2.6834	4
2.8813	2.6016	5
2.8008	2.5321	6
2.7323	2.4726	7
2.6741	2.4187	8
2.6219	2.3724	9
2.5735	2.3279	10
2.5324	2.2918	11
2.4934	2.2575	12
2.4570	2.2271	13
2.4214	2.1950	14
2.3906	2.1661	15
2.3628	2.1396	16
2.3341	2.1168	17
2.3097	2.0924	18
2.2824	2.0717	19
2.2592	2.0504	20
2.2377	2.0338	21
2.2139	2.0142	22
2.1953	1.9946	23
2.1751	1.9793	24
2.1572	1.9625	25
2.1375	1.9471	26
2.1208	1.9300	27
2.1063	1.9190	28
2.0866	1.9050	29
2.0748	1.8916	30
2.0568	1.8809	31
2.0418	1.8682	32
2.0274	1.8551	33
2.0139	1.8468	34
2.0026	1.8347	35
1.9880	1.8248	36
1.9746	1.8128	37
1.9608	1.8056	38
1.9524	1.7968	39
1.9414	1.7840	40
1.9269	1.7764	41
1.9160	1.7662	42
1.9041	1.7602	43
1.8962	1.7503	44
1.8826	1.7414	45
1.8737	1.7359	46
1.8635	1.7273	47
1.8544	1.7207	48
1.8476	1.7135	49
1.8355	1.7051	50
1.8272	1.6969	51
1.8178	1.6906	52
1.8079	1.6862	53
1.7998	1.6786	54
1.7939	1.6712	55
1.7826	1.6628	56
1.7752	1.6567	57
1.7675	1.6518	58
1.7606	1.6464	59
1.7510	1.6408	60
1.7456	1.6329	61
1.7390	1.6284	62
1.7289	1.6233	63
1.7183	1.6176	64
1.7127	1.6125	65
1.7087	1.6098	66
1.6990	1.5985	67
1.6945	1.5934	68
1.6872	1.5876	69
1.6795	1.5816	70
1.6758	1.5778	71
1.6659	1.5742	72
1.6603	1.5702	73
1.6516	1.5618	74
1.6463	1.5592	75
1.6400	1.5541	76
1.6354	1.5484	77
1.6305	1.5424	78
1.6217	1.5378	79
1.6169	1.5338	80
1.6102	1.5301	81
1.6070	1.5229	82
1.5979	1.5195	83
1.5926	1.5163	84
1.5875	1.5106	85
1.5814	1.5075	86
1.5748	1.5021	87
1.5672	1.4984	88
1.5657	1.4945	89
1.5597	1.4913	90
1.5530	1.4863	91
1.5506	1.4821	92
1.5437	1.4785	93
1.5405	1.4730	94
1.5325	1.4678	95
1.5285	1.4666	96
1.5233	1.4634	97
1.5189	1.4580	98
1.5122	1.4558	99
1.5078	1.4517	100
1.5059	1.4471	101
1.4956	1.4446	102
1.4944	1.4396	103
1.4881	1.4371	104
1.4851	1.4334	105
1.4763	1.4295	106
1.4725	1.4273	107
1.4686	1.4243	108
1.4663	1.4196	109
1.4588	1.4180	110
1.4558	1.4152	111
1.4525	1.4127	112
1.4465	1.4085	113
1.4431	1.4052	114
1.4386	1.4025	115
1.4343	1.4000	116
1.4306	1.3969	117
1.4259	1.3925	118
1.4192	1.3919	119
1.4165	1.3886	120
1.4109	1.3857	121
1.4093	1.3844	122
1.4058	1.3797	123
1.4003	1.3779	124
1.3992	1.3733	125
1.3898	1.3721	126
1.3877	1.3692	127
1.3845	1.3681	128
1.3821	1.3665	129
1.3767	1.3652	130
1.3720	1.3600	131
1.3707	1.3572	132
1.3674	1.3546	133
1.3628	1.3550	134
1.3582	1.3510	135
1.3548	1.3484	136
1.3518	1.3481	137
1.3490	1.3467	138
1.3463	1.3423	139
1.3411	1.3401	140
1.3367	1.3387	141
1.3332	1.3371	142
1.3313	1.3341	143
1.3285	1.3304	144
1.3235	1.3302	145
1.3203	1.3292	146
1.3186	1.3259	147
1.3132	1.3230	148
1.3106	1.3233	149
1.3083	1.3169	150
1.3011	1.3179	151
1.2986	1.3151	152
1.2975	1.3150	153
1.2905	1.3124	154
1.2887	1.3096	155
1.2862	1.3105	156
1.2831	1.3064	157
1.2796	1.3051	158
1.2777	1.3024	159
1.2758	1.2993	160
1.2694	1.2997	161
1.2681	1.2974	162
1.2626	1.2935	163
1.2617	1.2946	164
1.2592	1.2928	165
1.2562	1.2899	166
1.2520	1.2890	167
1.2488	1.2876	168
1.2468	1.2848	169
1.2450	1.2840	170
1.2388	1.2861	171
1.2384	1.2815	172
1.2331	1.2808	173
1.2328	1.2774	174
1.2299	1.2770	175
1.2253	1.2752	176
1.2251	1.2740	177
1.2188	1.2722	178
1.2167	1.2706	179
1.2141	1.2679	180
1.2125	1.2671	181
1.2080	1.2674	182
1.2049	1.2665	183
1.2021	1.2635	184
1.2013	1.2629	185
1.1975	1.2599	186
1.1946	1.2593	187
1.1939	1.2599	188
1.1897	1.2560	189
1.1879	1.2569	190
1.1841	1.2539	191
1.1829	1.2540	192
1.1804	1.2538	193
1.1759	1.2513	194
1.1745	1.2480	195
1.1690	1.2483	196
1.1686	1.2458	197
1.1647	1.2450	198
1.1628	1.2457	199
1.1624	1.2461	200
1.1584	1.2429	201
1.1563	1.2417	202
1.1543	1.2407	203
1.1489	1.2391	204
1.1464	1.2422	205
1.1482	1.2384	206
1.1446	1.2355	207
1.1425	1.2351	208
1.1373	1.2343	209
1.1378	1.2327	210
1.1362	1.2311	211
1.1331	1.2304	212
1.1315	1.2279	213
1.1265	1.2290	214
1.1254	1.2284	215
1.1220	1.2276	216
1.1208	1.2230	217
1.1218	1.2220	218
1.1140	1.2222	219
1.1115	1.2205	220
1.1120	1.2223	221
1.1081	1.2213	222
1.1059	1.2190	223
1.1025	1.2186	224
1.1031	1.2182	225
1.0996	1.2155	226
1.0972	1.2144	227
1.0953	1.2136	228
1.0929	1.2126	229
1.0893	1.2153	230
1.0868	1.2147	231
1.0877	1.2114	232
1.0834	1.2118	233
1.0815	1.2103	234
1.0802	1.2096	235
1.0771	1.2110	236
1.0740	1.2087	237
1.0735	1.2058	238
1.0731	1.2077	239
1.0693	1.2051	240
1.0667	1.2055	241
1.0662	1.2034	242
1.0659	1.2028	243
1.0619	1.2009	244
1.0601	1.2020	245
1.0578	1.1984	246
1.0541	1.2002	247
1.0524	1.1992	248
1.0474	1.1996	249
1.0493	1.1975	250
1.0466	1.1986	251
1.0454	1.1955	252
1.0448	1.1940	253
1.0388	1.1944	254
1.0373	1.1930	255
1.0345	1.1956	256
1.0330	1.1915	257
1.0329	1.1902	258
1.0310	1.1923	259
1.0277	1.1905	260
1.0282	1.1890	261
1.0229	1.1895	262
1.0225	1.1888	263
1.0227	1.1877	264
1.0207	1.1845	265
1.0165	1.1870	266
1.0143	1.1850	267
1.0133	1.1838	268
1.0107	1.1851	269
1.0097	1.1852	270
1.0082	1.1829	271
1.0050	1.1824	272
1.0032	1.1834	273
1.0017	1.1806	274
1.0017	1.1805	275
0.9989	1.1814	276
0.9985	1.1779	277
0.9947	1.1782	278
0.9940	1.1776	279
0.9921	1.1779	280
0.9909	1.1788	281
0.9876	1.1764	282
0.9867	1.1763	283
0.9832	1.1762	284
0.9795	1.1743	285
0.9791	1.1762	286
0.9772	1.1724	287
0.9770	1.1729	288
0.9754	1.1757	289
0.9730	1.1711	290
0.9707	1.1734	291
0.9700	1.1732	292
0.9683	1.1699	293
0.9653	1.1705	294
0.9660	1.1706	295
0.9626	1.1679	296
0.9625	1.1666	297
0.9592	1.1693	298
0.9572	1.1659	299

Framework versions

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