learning_rate: 1.4286519916884536e-06
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.1527	1.0	7	1.1737	0.3256	0.1060	0.3256	0.1599
1.099	2.0	14	1.1661	0.3256	0.1060	0.3256	0.1599
1.1234	3.0	21	1.1555	0.3256	0.1060	0.3256	0.1599
1.1072	4.0	28	1.1431	0.3256	0.1085	0.3256	0.1628
1.1029	5.0	35	1.1338	0.3256	0.1112	0.3256	0.1658
1.0976	6.0	42	1.1234	0.3256	0.1140	0.3256	0.1688
1.0904	7.0	49	1.1139	0.2791	0.1493	0.2791	0.1750
1.0961	8.0	56	1.1039	0.3256	0.2082	0.3256	0.2197
1.088	9.0	63	1.0938	0.3256	0.1957	0.3256	0.1964
1.0647	10.0	70	1.0801	0.3488	0.2580	0.3488	0.2514
1.0795	11.0	77	1.0672	0.4186	0.3072	0.4186	0.3372
1.0446	12.0	84	1.0541	0.3953	0.2928	0.3953	0.3058
1.0252	13.0	91	1.0369	0.4186	0.3072	0.4186	0.3372
1.0213	14.0	98	1.0178	0.4419	0.3295	0.4419	0.3545
1.0095	15.0	105	0.9921	0.4651	0.6528	0.4651	0.4064
1.0214	16.0	112	0.9691	0.5349	0.6781	0.5349	0.5079
0.9795	17.0	119	0.9463	0.6047	0.6877	0.6047	0.6085
0.9442	18.0	126	0.9217	0.6279	0.6932	0.6279	0.6367
0.9203	19.0	133	0.8933	0.6047	0.6474	0.6047	0.6085
0.8526	20.0	140	0.8684	0.6512	0.6753	0.6512	0.6584
0.8544	21.0	147	0.8451	0.6512	0.6753	0.6512	0.6584
0.8414	22.0	154	0.8171	0.6512	0.6778	0.6512	0.6550
0.7938	23.0	161	0.7938	0.6512	0.6778	0.6512	0.6550
0.7906	24.0	168	0.7730	0.6512	0.6758	0.6512	0.6579
0.7953	25.0	175	0.7561	0.6744	0.7011	0.6744	0.6805
0.7221	26.0	182	0.7374	0.6744	0.6965	0.6744	0.6806
0.719	27.0	189	0.7226	0.6977	0.7201	0.6977	0.7031
0.734	28.0	196	0.7090	0.6744	0.7011	0.6744	0.6805
0.6773	29.0	203	0.6931	0.7209	0.7393	0.7209	0.7251
0.659	30.0	210	0.6823	0.7209	0.7417	0.7209	0.7238
0.6654	31.0	217	0.6694	0.7442	0.7669	0.7442	0.7450
0.6625	32.0	224	0.6537	0.7442	0.7593	0.7442	0.7466
0.6448	33.0	231	0.6422	0.7442	0.7601	0.7442	0.7443
0.6181	34.0	238	0.6350	0.7674	0.7851	0.7674	0.7684
0.601	35.0	245	0.6313	0.7442	0.7669	0.7442	0.7450
0.5925	36.0	252	0.6131	0.8140	0.8236	0.8140	0.8135
0.605	37.0	259	0.6060	0.8372	0.8423	0.8372	0.8375
0.5754	38.0	266	0.6058	0.7674	0.7851	0.7674	0.7684
0.5546	39.0	273	0.6100	0.7209	0.7487	0.7209	0.7208
0.5655	40.0	280	0.5857	0.7907	0.8015	0.7907	0.7909
0.5631	41.0	287	0.5795	0.8140	0.8210	0.8140	0.8147
0.615	42.0	294	0.5834	0.7442	0.7669	0.7442	0.7450
0.582	43.0	301	0.5766	0.8140	0.8210	0.8140	0.8147
0.5756	44.0	308	0.5686	0.8140	0.8210	0.8140	0.8147
0.4988	45.0	315	0.5661	0.8140	0.8210	0.8140	0.8147
0.5374	46.0	322	0.5636	0.7907	0.8008	0.7907	0.7915
0.5252	47.0	329	0.5712	0.7674	0.7813	0.7674	0.7677
0.5201	48.0	336	0.5688	0.7674	0.7813	0.7674	0.7677
0.4884	49.0	343	0.5453	0.8140	0.8210	0.8140	0.8147
0.4681	50.0	350	0.5449	0.7907	0.8008	0.7907	0.7915
0.5129	51.0	357	0.5624	0.7907	0.8008	0.7907	0.7915
0.4872	52.0	364	0.5488	0.7907	0.8008	0.7907	0.7915
0.4953	53.0	371	0.5389	0.7907	0.8008	0.7907	0.7915
0.4781	54.0	378	0.5336	0.7907	0.8008	0.7907	0.7915
0.4787	55.0	385	0.5411	0.7907	0.8008	0.7907	0.7915
0.4831	56.0	392	0.5364	0.7907	0.8008	0.7907	0.7915
0.4341	57.0	399	0.5289	0.7907	0.8008	0.7907	0.7915
0.4604	58.0	406	0.5392	0.7907	0.8008	0.7907	0.7915
0.4512	59.0	413	0.5379	0.7907	0.8008	0.7907	0.7915
0.4574	60.0	420	0.5355	0.7907	0.8008	0.7907	0.7915
0.4302	61.0	427	0.5176	0.7907	0.8008	0.7907	0.7915
0.4524	62.0	434	0.5235	0.7907	0.8008	0.7907	0.7915
0.4394	63.0	441	0.5267	0.7907	0.8008	0.7907	0.7915
0.4462	64.0	448	0.5283	0.7907	0.8008	0.7907	0.7915
0.4776	65.0	455	0.5239	0.7907	0.8008	0.7907	0.7915
0.4944	66.0	462	0.5271	0.7907	0.8008	0.7907	0.7915

Framework versions

Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Noctuaru/tone-berita-v1

results

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Noctuaru/
tone-berita-v1