RoBERTa-Base-SE2025T11A-sun-v20250110150952

This model is a fine-tuned version of w11wo/sundanese-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3350
• F1 Macro: 0.6533
• F1 Micro: 0.6746
• F1 Weighted: 0.6698
• F1 Samples: 0.6779
• F1 Label Marah: 0.576
• F1 Label Jijik: 0.6207
• F1 Label Takut: 0.5556
• F1 Label Senang: 0.8526
• F1 Label Sedih: 0.7519
• F1 Label Terkejut: 0.5593
• F1 Label Biasa: 0.6571

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:

• learning_rate: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	F1 Weighted	F1 Samples	F1 Label Marah	F1 Label Jijik	F1 Label Takut	F1 Label Senang	F1 Label Sedih	F1 Label Terkejut	F1 Label Biasa
0.4979	0.1133	100	0.4225	0.1092	0.3106	0.1653	0.2219	0.0	0.0	0.0	0.7647	0.0	0.0	0.0
0.4436	0.2265	200	0.4056	0.1122	0.3034	0.1698	0.2091	0.0	0.0	0.0	0.7854	0.0	0.0	0.0
0.4156	0.3398	300	0.4032	0.1107	0.2908	0.1676	0.1954	0.0	0.0	0.0	0.7751	0.0	0.0	0.0
0.4204	0.4530	400	0.3861	0.1304	0.3222	0.1914	0.2256	0.0	0.0	0.0	0.8073	0.0	0.1053	0.0
0.3883	0.5663	500	0.3632	0.2351	0.3741	0.2888	0.2731	0.2564	0.0	0.3284	0.7940	0.1370	0.1299	0.0
0.3911	0.6795	600	0.3478	0.3287	0.4563	0.3870	0.3525	0.2535	0.0	0.4	0.7879	0.5417	0.3182	0.0
0.337	0.7928	700	0.3364	0.3837	0.5037	0.4376	0.4164	0.4048	0.1613	0.4810	0.7964	0.5208	0.3218	0.0
0.3549	0.9060	800	0.3213	0.4178	0.5479	0.4751	0.4977	0.6154	0.0351	0.5385	0.8241	0.5155	0.3960	0.0
0.35	1.0193	900	0.3143	0.5227	0.6003	0.5591	0.5411	0.5	0.5806	0.4935	0.8173	0.7143	0.3256	0.2273
0.2836	1.1325	1000	0.3019	0.5857	0.6248	0.6058	0.5927	0.5789	0.4894	0.5517	0.8246	0.6783	0.4286	0.5484
0.2778	1.2458	1100	0.2939	0.5464	0.6178	0.5872	0.5769	0.6555	0.3947	0.5316	0.8195	0.75	0.4463	0.2273
0.2942	1.3590	1200	0.2832	0.6138	0.6492	0.6344	0.6265	0.6015	0.5818	0.5195	0.8316	0.7692	0.4444	0.5484
0.2701	1.4723	1300	0.2858	0.6261	0.6509	0.6411	0.6367	0.5985	0.5361	0.5263	0.8163	0.7213	0.5273	0.6567
0.2492	1.5855	1400	0.2999	0.5906	0.6349	0.6123	0.6198	0.6102	0.5859	0.4938	0.8325	0.6866	0.4082	0.5172
0.258	1.6988	1500	0.2898	0.6263	0.6566	0.6428	0.6425	0.5773	0.5882	0.5217	0.8358	0.7259	0.5	0.6349
0.3253	1.8120	1600	0.2801	0.6178	0.6512	0.6401	0.6196	0.6055	0.5979	0.5333	0.8272	0.7541	0.4906	0.5161
0.2788	1.9253	1700	0.3016	0.5906	0.6338	0.6181	0.6195	0.6024	0.4051	0.5610	0.8152	0.7820	0.5045	0.4643
0.2325	2.0385	1800	0.2823	0.6290	0.6582	0.6450	0.6379	0.5926	0.6286	0.5366	0.8254	0.7313	0.48	0.6087
0.182	2.1518	1900	0.2911	0.6430	0.6724	0.6583	0.6682	0.6126	0.6139	0.6	0.8374	0.7407	0.4808	0.6154
0.1947	2.2650	2000	0.3006	0.6238	0.6499	0.6377	0.6361	0.5636	0.6154	0.5	0.8068	0.725	0.5094	0.6462
0.1809	2.3783	2100	0.2953	0.6321	0.6583	0.6477	0.6460	0.5243	0.5833	0.5618	0.8421	0.7299	0.5263	0.6567
0.1967	2.4915	2200	0.3025	0.6259	0.6546	0.6457	0.6499	0.6154	0.6126	0.5679	0.8218	0.72	0.5079	0.5357
0.195	2.6048	2300	0.3117	0.6430	0.6675	0.6597	0.6641	0.5634	0.5532	0.5647	0.8259	0.7606	0.5882	0.6452
0.1873	2.7180	2400	0.3053	0.6452	0.6667	0.6626	0.6729	0.5556	0.6154	0.5301	0.8325	0.7660	0.5806	0.6364
0.1659	2.8313	2500	0.3036	0.6455	0.6683	0.6647	0.6594	0.5960	0.6154	0.5682	0.8254	0.7559	0.5812	0.5763
0.2041	2.9445	2600	0.3129	0.6306	0.6555	0.6470	0.6593	0.56	0.592	0.5581	0.8283	0.7463	0.5091	0.6207
0.137	3.0578	2700	0.3043	0.6316	0.6577	0.6514	0.6472	0.5827	0.5510	0.5301	0.8272	0.7656	0.5645	0.6
0.1449	3.1710	2800	0.3101	0.6272	0.6538	0.6462	0.6502	0.5690	0.6071	0.5349	0.8342	0.7244	0.5323	0.5882
0.1192	3.2843	2900	0.3101	0.6460	0.6675	0.6643	0.6658	0.6066	0.6095	0.5417	0.8287	0.7606	0.5692	0.6061
0.1165	3.3975	3000	0.3152	0.6467	0.6707	0.6636	0.6689	0.6015	0.5905	0.5618	0.8426	0.7442	0.5487	0.6377
0.1568	3.5108	3100	0.3254	0.6428	0.6635	0.6601	0.6670	0.5426	0.6071	0.5684	0.8360	0.7463	0.5739	0.625
0.1396	3.6240	3200	0.3252	0.6367	0.6618	0.6542	0.6598	0.5736	0.6038	0.5556	0.8449	0.75	0.5094	0.6197
0.1347	3.7373	3300	0.3278	0.6326	0.6611	0.6501	0.6649	0.5950	0.5984	0.5128	0.8350	0.7244	0.5357	0.6269
0.139	3.8505	3400	0.3298	0.6457	0.6651	0.6645	0.6660	0.5735	0.5785	0.5618	0.8261	0.7820	0.592	0.6061
0.1068	3.9638	3500	0.3225	0.6515	0.6730	0.6687	0.6673	0.6034	0.6126	0.5542	0.8352	0.7552	0.5714	0.6286
0.1023	4.0770	3600	0.3215	0.6526	0.6754	0.6703	0.6757	0.5833	0.6182	0.5778	0.8513	0.7368	0.5736	0.6269
0.1192	4.1903	3700	0.3264	0.6519	0.6730	0.6679	0.6768	0.5954	0.6095	0.5591	0.8377	0.7647	0.55	0.6471
0.092	4.3035	3800	0.3323	0.6436	0.6643	0.6615	0.6681	0.5588	0.6126	0.5435	0.8421	0.7612	0.56	0.6269
0.0959	4.4168	3900	0.3346	0.6475	0.6714	0.6662	0.6783	0.5736	0.6055	0.5376	0.8557	0.7591	0.5645	0.6364
0.1125	4.5300	4000	0.3364	0.6390	0.6589	0.6564	0.6679	0.5522	0.5926	0.5417	0.8404	0.7647	0.544	0.6377
0.0954	4.6433	4100	0.3336	0.6487	0.6714	0.6673	0.6765	0.5827	0.6087	0.5495	0.8646	0.7424	0.5556	0.6377
0.0875	4.7565	4200	0.3371	0.6454	0.6698	0.6643	0.6730	0.5714	0.6050	0.5333	0.8601	0.7519	0.5593	0.6364
0.0717	4.8698	4300	0.3348	0.6510	0.6730	0.6678	0.6761	0.576	0.6207	0.5393	0.8526	0.7519	0.5593	0.6571
0.0908	4.9830	4400	0.3350	0.6533	0.6746	0.6698	0.6779	0.576	0.6207	0.5556	0.8526	0.7519	0.5593	0.6571

Framework versions

• Transformers 4.48.0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0