pijarcandra22/NMTIndoBaliBART

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

• Train Loss: 5.5346
• Validation Loss: 5.5566
• Epoch: 330

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': 0.02, '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
9.7885	5.6003	0
5.5737	5.5523	1
5.5346	5.5361	2
5.5189	5.5283	3
5.5149	5.5252	4
5.5123	5.5233	5
5.5116	5.5485	6
5.5095	5.5314	7
5.5120	5.5569	8
5.5137	5.5239	9
5.5170	5.5289	10
5.5180	5.5298	11
5.5217	5.5513	12
5.5219	5.5344	13
5.5248	5.5366	14
5.5268	5.5493	15
5.5260	5.5313	16
5.5290	5.5462	17
5.5299	5.5570	18
5.5293	5.5480	19
5.5378	5.5524	20
5.5317	5.5740	21
5.5328	5.5543	22
5.5327	5.5537	23
5.5330	5.5356	24
5.5304	5.5492	25
5.5355	5.5388	26
5.5337	5.5812	27
5.5355	5.5598	28
5.5348	5.5489	29
5.5373	5.5526	30
5.5357	5.5575	31
5.5377	5.5439	32
5.5404	5.5367	33
5.5383	5.5819	34
5.5359	5.5815	35
5.5370	5.5499	36
5.5340	5.5622	37
5.5373	5.5667	38
5.5360	5.5548	39
5.5327	5.5555	40
5.5365	5.5642	41
5.5375	5.5496	42
5.5336	5.5424	43
5.5359	5.5761	44
5.5360	5.5821	45
5.5362	5.5742	46
5.5352	5.5635	47
5.5335	5.5507	48
5.5340	5.5613	49
5.5368	5.5599	50
5.5375	5.5541	51
5.5368	5.5536	52
5.5366	5.5438	53
5.5363	5.5497	54
5.5364	5.5721	55
5.5388	5.5493	56
5.5361	5.5719	57
5.5372	5.5920	58
5.5346	5.5534	59
5.5354	5.5526	60
5.5357	5.5788	61
5.5370	5.5531	62
5.5374	5.5613	63
5.5366	5.5585	64
5.5370	5.5652	65
5.5354	5.5463	66
5.5354	5.5689	67
5.5355	5.5508	68
5.5350	5.5522	69
5.5334	5.5574	70
5.5354	5.5555	71
5.5354	5.5503	72
5.5368	5.5562	73
5.5373	5.5524	74
5.5356	5.5544	75
5.5365	5.5508	76
5.5357	5.5650	77
5.5355	5.5665	78
5.5365	5.5471	79
5.5356	5.5535	80
5.5357	5.5801	81
5.5354	5.5570	82
5.5361	5.5596	83
5.5377	5.5584	84
5.5333	5.5570	85
5.5348	5.5513	86
5.5367	5.5508	87
5.5354	5.5333	88
5.5375	5.5530	89
5.5353	5.5386	90
5.5372	5.5966	91
5.5365	5.5582	92
5.5349	5.5776	93
5.5348	5.5700	94
5.5356	5.5518	95
5.5371	5.5692	96
5.5374	5.5572	97
5.5369	5.5621	98
5.5343	5.5593	99
5.5372	5.5698	100
5.5367	5.5422	101
5.5366	5.5846	102
5.5387	5.5687	103
5.5377	5.5590	104
5.5307	5.5640	105
5.5360	5.5421	106
5.5355	5.5542	107
5.5346	5.5460	108
5.5375	5.5610	109
5.5332	5.5676	110
5.5355	5.5364	111
5.5332	5.5630	112
5.5363	5.5600	113
5.5362	5.5705	114
5.5358	5.5700	115
5.5368	5.5578	116
5.5364	5.5531	117
5.5345	5.5688	118
5.5350	5.5620	119
5.5336	5.5764	120
5.5364	5.5476	121
5.5358	5.5623	122
5.5364	5.5569	123
5.5337	5.5713	124
5.5346	5.5936	125
5.5357	5.5645	126
5.5358	5.5566	127
5.5399	5.5494	128
5.5362	5.5586	129
5.5394	5.5611	130
5.5355	5.5691	131
5.5361	5.5471	132
5.5343	5.5549	133
5.5379	5.5587	134
5.5380	5.5728	135
5.5366	5.5780	136
5.5363	5.5567	137
5.5395	5.5515	138
5.5337	5.5563	139
5.5341	5.5561	140
5.5336	5.5520	141
5.5340	5.5689	142
5.5363	5.5472	143
5.5356	5.5503	144
5.5338	5.5630	145
5.5357	5.5385	146
5.5349	5.5545	147
5.5363	5.5514	148
5.5361	5.5591	149
5.5379	5.5567	150
5.5340	5.5434	151
5.5362	5.5677	152
5.5350	5.5574	153
5.5356	5.5782	154
5.5371	5.5484	155
5.5330	5.5557	156
5.5340	5.5583	157
5.5350	5.5641	158
5.5350	5.5595	159
5.5356	5.5622	160
5.5386	5.5545	161
5.5347	5.5667	162
5.5318	5.5692	163
5.5370	5.5704	164
5.5361	5.5654	165
5.5358	5.5645	166
5.5337	5.5518	167
5.5356	5.5574	168
5.5364	5.5621	169
5.5360	5.5573	170
5.5388	5.5438	171
5.5343	5.5623	172
5.5368	5.5505	173
5.5345	5.5798	174
5.5369	5.5449	175
5.5364	5.5664	176
5.5365	5.5530	177
5.5369	5.5533	178
5.5348	5.5432	179
5.5379	5.5875	180
5.5370	5.5531	181
5.5340	5.5695	182
5.5372	5.5529	183
5.5356	5.5778	184
5.5371	5.5465	185
5.5370	5.5459	186
5.5356	5.5457	187
5.5375	5.5761	188
5.5338	5.5409	189
5.5369	5.5698	190
5.5373	5.5665	191
5.5361	5.5677	192
5.5355	5.5775	193
5.5372	5.5649	194
5.5355	5.5477	195
5.5328	5.5492	196
5.5342	5.5575	197
5.5331	5.5774	198
5.5362	5.5631	199
5.5350	5.5539	200
5.5365	5.5799	201
5.5372	5.5630	202
5.5341	5.5584	203
5.5353	5.5616	204
5.5351	5.5764	205
5.5374	5.5692	206
5.5363	5.5608	207
5.5345	5.5611	208
5.5381	5.5643	209
5.5363	5.5719	210
5.5386	5.5536	211
5.5329	5.5757	212
5.5360	5.5405	213
5.5356	5.5525	214
5.5354	5.5423	215
5.5382	5.5476	216
5.5353	5.5623	217
5.5344	5.5716	218
5.5361	5.5569	219
5.5369	5.5536	220
5.5370	5.5726	221
5.5366	5.5520	222
5.5370	5.5698	223
5.5342	5.5522	224
5.5367	5.5438	225
5.5373	5.5474	226
5.5317	5.5634	227
5.5350	5.5669	228
5.5360	5.5631	229
5.5370	5.5553	230
5.5347	5.5452	231
5.5347	5.5600	232
5.5351	5.5551	233
5.5360	5.5625	234
5.5409	5.5640	235
5.5362	5.5596	236
5.5340	5.5506	237
5.5372	5.5549	238
5.5340	5.5879	239
5.5355	5.5609	240
5.5376	5.5627	241
5.5354	5.5903	242
5.5358	5.5591	243
5.5327	5.5638	244
5.5334	5.5449	245
5.5330	5.5552	246
5.5338	5.5721	247
5.5359	5.5736	248
5.5361	5.5440	249
5.5377	5.5656	250
5.5353	5.5690	251
5.5375	5.5540	252
5.5357	5.5555	253
5.5349	5.5658	254
5.5365	5.5563	255
5.5327	5.5544	256
5.5346	5.5851	257
5.5372	5.5556	258
5.5373	5.5504	259
5.5361	5.5657	260
5.5348	5.5585	261
5.5349	5.5664	262
5.5343	5.5454	263
5.5351	5.5820	264
5.5334	5.5521	265
5.5361	5.5648	266
5.5375	5.5596	267
5.5363	5.5525	268
5.5377	5.5752	269
5.5359	5.5523	270
5.5347	5.5662	271
5.5363	5.5613	272
5.5370	5.5529	273
5.5360	5.5523	274
5.5363	5.5548	275
5.5342	5.5523	276
5.5318	5.5659	277
5.5376	5.5582	278
5.5327	5.5649	279
5.5339	5.5665	280
5.5373	5.5693	281
5.5324	5.5660	282
5.5352	5.5580	283
5.5362	5.5770	284
5.5383	5.5431	285
5.5337	5.5632	286
5.5376	5.5549	287
5.5336	5.5519	288
5.5334	5.5506	289
5.5349	5.5608	290
5.5356	5.5589	291
5.5360	5.5628	292
5.5360	5.5700	293
5.5372	5.5418	294
5.5354	5.5559	295
5.5349	5.5524	296
5.5370	5.5663	297
5.5354	5.5955	298
5.5340	5.5563	299
5.5389	5.5476	300
5.5359	5.5566	301
5.5363	5.5587	302
5.5350	5.5561	303
5.5336	5.5498	304
5.5357	5.5522	305
5.5382	5.5501	306
5.5351	5.5579	307
5.5311	5.5555	308
5.5369	5.5627	309
5.5333	5.5700	310
5.5341	5.5649	311
5.5359	5.5884	312
5.5367	5.5592	313
5.5356	5.5457	314
5.5363	5.5478	315
5.5368	5.5638	316
5.5346	5.5500	317
5.5341	5.5568	318
5.5343	5.5598	319
5.5350	5.5446	320
5.5366	5.5618	321
5.5373	5.5705	322
5.5387	5.5586	323
5.5339	5.5681	324
5.5342	5.5401	325
5.5360	5.5953	326
5.5367	5.5694	327
5.5369	5.5517	328
5.5352	5.5792	329
5.5346	5.5566	330

Framework versions

• Transformers 4.40.2
• TensorFlow 2.15.0
• Datasets 2.19.1
• Tokenizers 0.19.1