Labira/LabiraPJOK_1_500

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

• Train Loss: 0.0001
• Validation Loss: 8.9416
• Epoch: 492

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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Epoch
0.0054	8.3302	0
0.0108	7.8442	1
0.0114	7.0958	2
0.0284	6.6490	3
0.0179	7.3034	4
0.0044	8.1785	5
0.0070	8.4039	6
0.0038	8.2728	7
0.0028	8.1154	8
0.0140	8.1207	9
0.0160	8.1384	10
0.0029	8.2978	11
0.0112	8.6940	12
0.0100	8.7433	13
0.0062	8.6486	14
0.0059	8.4821	15
0.0055	8.4559	16
0.0039	8.5136	17
0.0044	8.2783	18
0.0016	8.0974	19
0.0094	7.9739	20
0.0020	8.2513	21
0.0008	8.4637	22
0.0039	8.2813	23
0.0017	8.2027	24
0.0018	8.2722	25
0.0015	8.3875	26
0.0013	8.4975	27
0.0013	8.6171	28
0.0009	8.7272	29
0.0010	8.8335	30
0.0007	8.9168	31
0.0007	8.9992	32
0.0006	9.0661	33
0.0007	9.1103	34
0.0004	9.1424	35
0.0008	9.1573	36
0.0006	9.1666	37
0.0008	9.1732	38
0.0004	9.1781	39
0.0006	9.1867	40
0.0005	9.1986	41
0.0005	9.2203	42
0.0005	9.2512	43
0.0006	9.2889	44
0.0005	9.3360	45
0.0007	9.3759	46
0.0004	9.4144	47
0.0006	9.4461	48
0.0004	9.4718	49
0.0005	9.5113	50
0.0004	9.5425	51
0.0003	9.5667	52
0.0015	9.5468	53
0.0003	9.4515	54
0.0005	9.3881	55
0.0006	9.3797	56
0.0006	9.3887	57
0.0003	9.4038	58
0.0004	9.4206	59
0.0003	9.4417	60
0.0003	9.4627	61
0.0003	9.4775	62
0.0004	9.4930	63
0.0009	9.5593	64
0.0003	9.6068	65
0.0003	9.6416	66
0.0003	9.6715	67
0.0003	9.6956	68
0.0004	9.7146	69
0.0010	9.7344	70
0.0002	9.7946	71
0.0003	9.7965	72
0.0034	9.7113	73
0.0004	9.5730	74
0.0005	9.4858	75
0.0009	9.5826	76
0.0006	9.6923	77
0.0005	9.8243	78
0.0005	9.9368	79
0.0007	10.0514	80
0.0006	10.1386	81
0.0010	10.1427	82
0.0005	9.9261	83
0.0011	9.8122	84
0.0003	9.8724	85
0.0081	9.5494	86
0.0151	8.3043	87
0.0425	9.1449	88
0.0076	8.8560	89
0.0113	8.2403	90
0.0446	7.5457	91
0.0264	7.4204	92
0.1545	8.0820	93
0.3878	8.2238	94
0.4155	6.1718	95
0.0410	5.0625	96
0.0768	4.8214	97
0.0514	4.8477	98
0.0150	5.2002	99
0.0328	5.6224	100
0.0260	5.9887	101
0.0040	6.2793	102
0.0076	6.3696	103
0.0013	6.3642	104
0.0075	6.4379	105
0.0015	6.6379	106
0.0010	6.7736	107
0.0023	6.8582	108
0.0056	6.8884	109
0.0011	6.9125	110
0.0014	6.9437	111
0.0014	6.9807	112
0.0010	7.0239	113
0.0006	7.0602	114
0.0006	7.0919	115
0.0005	7.1213	116
0.0008	7.1457	117
0.0006	7.1679	118
0.0009	7.1871	119
0.0288	7.3166	120
0.0007	7.1397	121
0.0033	6.9025	122
0.0020	6.8509	123
0.0068	6.9533	124
0.0066	7.2446	125
0.0035	7.5351	126
0.0019	7.7354	127
0.0021	7.8376	128
0.0007	7.9071	129
0.0012	7.9566	130
0.0009	8.0014	131
0.0013	8.0186	132
0.0015	8.0123	133
0.0009	7.9870	134
0.0008	7.9685	135
0.0005	7.9599	136
0.0005	7.9553	137
0.0005	7.9574	138
0.0005	7.9631	139
0.0010	7.9780	140
0.0006	7.9910	141
0.0006	8.0078	142
0.0004	8.0283	143
0.0006	8.0500	144
0.0005	8.0704	145
0.0008	8.0899	146
0.0003	8.1078	147
0.0003	8.1243	148
0.0005	8.1384	149
0.0005	8.1534	150
0.0003	8.1678	151
0.0003	8.1827	152
0.0002	8.1955	153
0.0004	8.2093	154
0.0003	8.2218	155
0.0003	8.2338	156
0.0003	8.2454	157
0.0003	8.2566	158
0.0004	8.2696	159
0.0006	8.2696	160
0.0003	8.2700	161
0.0003	8.2745	162
0.0004	8.2834	163
0.0004	8.2918	164
0.0003	8.3035	165
0.0004	8.3182	166
0.0005	8.3357	167
0.0003	8.3499	168
0.0002	8.3616	169
0.0005	8.3759	170
0.0003	8.3901	171
0.0002	8.4020	172
0.0004	8.4105	173
0.0004	8.4120	174
0.0005	8.4166	175
0.0003	8.4209	176
0.0003	8.4287	177
0.0011	8.4219	178
0.0005	8.3854	179
0.0003	8.3589	180
0.0003	8.3630	181
0.0002	8.3680	182
0.0003	8.3735	183
0.0003	8.3812	184
0.0003	8.3882	185
0.0003	8.3937	186
0.0002	8.3989	187
0.0003	8.4022	188
0.0003	8.4048	189
0.0003	8.4102	190
0.0004	8.4197	191
0.0003	8.4328	192
0.0004	8.4468	193
0.0002	8.4609	194
0.0011	8.4712	195
0.0003	8.4735	196
0.0002	8.4789	197
0.0007	8.4928	198
0.0002	8.5066	199
0.0003	8.5205	200
0.0003	8.5368	201
0.0003	8.5531	202
0.0002	8.5676	203
0.0002	8.5815	204
0.0003	8.5989	205
0.0003	8.6161	206
0.0001	8.6305	207
0.0003	8.6473	208
0.0003	8.6626	209
0.0003	8.6764	210
0.0002	8.6899	211
0.0002	8.7019	212
0.0002	8.7119	213
0.0002	8.7212	214
0.0002	8.7302	215
0.0004	8.7417	216
0.0002	8.7514	217
0.0002	8.7593	218
0.0003	8.7690	219
0.0002	8.7771	220
0.0002	8.7845	221
0.0001	8.7917	222
0.0002	8.7980	223
0.0002	8.8040	224
0.0003	8.8093	225
0.0003	8.8150	226
0.0002	8.8209	227
0.0003	8.8271	228
0.0002	8.8329	229
0.0002	8.8378	230
0.0002	8.8429	231
0.0003	8.8493	232
0.0003	8.8575	233
0.0004	8.8548	234
0.0003	8.8510	235
0.0001	8.8501	236
0.0003	8.8473	237
0.0003	8.8561	238
0.0003	8.8667	239
0.0002	8.8767	240
0.0001	8.8847	241
0.0002	8.8915	242
0.0002	8.8980	243
0.0002	8.9038	244
0.0002	8.9119	245
0.0002	8.9202	246
0.0002	8.9279	247
0.0002	8.9345	248
0.0001	8.9400	249
0.0001	8.9441	250
0.0002	8.9473	251
0.0001	8.9504	252
0.0002	8.9537	253
0.0001	8.9574	254
0.0002	8.9610	255
0.0002	8.9650	256
0.0002	8.9691	257
0.0001	8.9724	258
0.0002	8.9753	259
0.0002	8.9769	260
0.0001	8.9793	261
0.0002	8.9827	262
0.0003	8.9841	263
0.0001	8.9854	264
0.0001	8.9873	265
0.0010	8.9858	266
0.0006	8.9368	267
0.0002	8.9002	268
0.0003	8.8744	269
0.0002	8.8568	270
0.0001	8.8456	271
0.0002	8.8382	272
0.0001	8.8340	273
0.0001	8.8317	274
0.0002	8.8310	275
0.0001	8.8316	276
0.0002	8.8335	277
0.0002	8.8372	278
0.0003	8.8456	279
0.0008	8.9045	280
0.0002	8.9467	281
0.0002	8.9773	282
0.0002	9.0012	283
0.0001	9.0195	284
0.0002	9.0337	285
0.0002	9.0464	286
0.0001	9.0564	287
0.0003	9.0657	288
0.0001	9.0737	289
0.0002	9.0798	290
0.0001	9.0844	291
0.0002	9.0884	292
0.0002	9.0919	293
0.0006	9.0998	294
0.0001	9.1140	295
0.0002	9.1256	296
0.0001	9.1349	297
0.0001	9.1424	298
0.0001	9.1486	299
0.0001	9.1534	300
0.0001	9.1576	301
0.0001	9.1614	302
0.0001	9.1649	303
0.0001	9.1676	304
0.0001	9.1701	305
0.0001	9.1720	306
0.0002	9.1744	307
0.0002	9.1769	308
0.0002	9.1787	309
0.0002	9.1789	310
0.0001	9.1801	311
0.0001	9.1813	312
0.0001	9.1832	313
0.0001	9.1858	314
0.0001	9.1880	315
0.0001	9.1901	316
0.0001	9.1921	317
0.0001	9.1948	318
0.0001	9.1989	319
0.0001	9.2026	320
0.0002	9.2092	321
0.0001	9.2147	322
0.0001	9.2194	323
0.0002	9.2234	324
0.0001	9.2268	325
0.0001	9.2295	326
0.0002	9.2321	327
0.0001	9.2347	328
0.0002	9.2369	329
0.0002	9.2350	330
0.0001	9.2327	331
0.0001	9.2315	332
0.0002	9.2312	333
0.0001	9.2322	334
0.0002	9.2349	335
0.0001	9.2378	336
0.0003	9.2406	337
0.0001	9.2434	338
0.0001	9.2461	339
0.0001	9.2483	340
0.0001	9.2502	341
0.0001	9.2519	342
0.0002	9.2568	343
0.0001	9.2606	344
0.0001	9.2637	345
0.0001	9.2661	346
0.0001	9.2682	347
0.0001	9.2703	348
0.0001	9.2723	349
0.0001	9.2742	350
0.0001	9.2758	351
0.0001	9.2770	352
0.0001	9.2782	353
0.0002	9.2796	354
0.0002	9.2826	355
0.0001	9.2851	356
0.0001	9.2871	357
0.0002	9.2901	358
0.0002	9.2941	359
0.0001	9.2968	360
0.0002	9.2944	361
0.0001	9.2931	362
0.0002	9.2926	363
0.0001	9.2924	364
0.0001	9.2928	365
0.0001	9.2933	366
0.0001	9.2945	367
0.0001	9.2985	368
0.0001	9.3021	369
0.0002	9.3048	370
0.0001	9.3067	371
0.0001	9.3083	372
0.0001	9.3100	373
0.0001	9.3117	374
0.0001	9.3135	375
0.0001	9.3154	376
0.0001	9.3169	377
0.0001	9.3184	378
0.0001	9.3200	379
0.0001	9.3215	380
0.0001	9.3230	381
0.0001	9.3238	382
0.0001	9.3252	383
0.0001	9.3261	384
0.0002	9.3277	385
0.0002	9.3294	386
0.0001	9.3312	387
0.0001	9.3329	388
0.0001	9.3345	389
0.0001	9.3359	390
0.0001	9.3372	391
0.0001	9.3384	392
0.0001	9.3400	393
0.0001	9.3413	394
0.0001	9.3425	395
0.0002	9.3422	396
0.0002	9.3412	397
0.0001	9.3394	398
0.0001	9.3381	399
0.0001	9.3372	400
0.0001	9.3368	401
0.0002	9.3364	402
0.0001	9.3356	403
0.0001	9.3350	404
0.0001	9.3347	405
0.0001	9.3348	406
0.0001	9.3349	407
0.0001	9.3353	408
0.0002	9.3362	409
0.0001	9.3371	410
0.0001	9.3379	411
0.0001	9.3389	412
0.0001	9.3399	413
0.0001	9.3408	414
0.0001	9.3416	415
0.0001	9.3423	416
0.0001	9.3430	417
0.0001	9.3440	418
0.0001	9.3450	419
0.0001	9.3459	420
0.0001	9.3470	421
0.0001	9.3480	422
0.0001	9.3491	423
0.0001	9.3502	424
0.0001	9.3509	425
0.0001	9.3517	426
0.0001	9.3526	427
0.0001	9.3537	428
0.0001	9.3550	429
0.0001	9.3560	430
0.0001	9.3571	431
0.0001	9.3579	432
0.0001	9.3585	433
0.0001	9.3593	434
0.0003	9.3602	435
0.0002	9.3611	436
0.0001	9.3619	437
0.0001	9.3629	438
0.0001	9.3641	439
0.0001	9.3650	440
0.0001	9.3659	441
0.0001	9.3666	442
0.0001	9.3673	443
0.0001	9.3679	444
0.0004	9.3693	445
0.0001	9.3704	446
0.0002	9.3719	447
0.0001	9.3737	448
0.0001	9.3753	449
0.0001	9.3764	450
0.0001	9.3772	451
0.0001	9.3780	452
0.0001	9.3787	453
0.0001	9.3792	454
0.0001	9.3798	455
0.0080	9.3369	456
0.0001	9.2235	457
0.0001	9.1362	458
0.0001	9.0715	459
0.0001	9.0246	460
0.0003	8.9910	461
0.0001	8.9672	462
0.0001	8.9506	463
0.0001	8.9390	464
0.0001	8.9310	465
0.0001	8.9257	466
0.0001	8.9222	467
0.0001	8.9199	468
0.0001	8.9187	469
0.0002	8.9186	470
0.0001	8.9191	471
0.0001	8.9196	472
0.0001	8.9201	473
0.0004	8.9214	474
0.0001	8.9236	475
0.0002	8.9255	476
0.0002	8.9280	477
0.0001	8.9305	478
0.0001	8.9323	479
0.0001	8.9337	480
0.0001	8.9347	481
0.0001	8.9356	482
0.0002	8.9365	483
0.0001	8.9374	484
0.0001	8.9380	485
0.0001	8.9386	486
0.0002	8.9392	487
0.0001	8.9397	488
0.0001	8.9402	489
0.0002	8.9406	490
0.0002	8.9411	491
0.0001	8.9416	492

Framework versions

• Transformers 4.44.2
• TensorFlow 2.17.0
• Datasets 3.0.1
• Tokenizers 0.19.1