learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
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: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9053	0.0120	3	1.0672	0.4851
1.1016	0.0239	6	0.9759	0.4931
0.8911	0.0359	9	0.9359	0.5207
0.9685	0.0478	12	0.9348	0.5563
0.9589	0.0598	15	0.9609	0.5529
0.9283	0.0717	18	0.9581	0.5494
1.0205	0.0837	21	0.9475	0.5471
0.7105	0.0956	24	0.9582	0.5379
1.0178	0.1076	27	0.9957	0.5299
1.0645	0.1195	30	1.0076	0.5333
0.8359	0.1315	33	0.9997	0.5356
0.8526	0.1434	36	0.9759	0.5287
0.9672	0.1554	39	0.9646	0.5356
0.972	0.1673	42	0.9563	0.5299
1.1935	0.1793	45	0.9433	0.5333
0.8012	0.1912	48	0.9415	0.5299
1.1007	0.2032	51	0.9415	0.5368
0.8461	0.2151	54	0.9306	0.5437
0.8329	0.2271	57	0.9292	0.5322
0.6564	0.2390	60	0.9432	0.5310
1.0364	0.2510	63	0.9627	0.5276
0.8999	0.2629	66	0.9646	0.5115
0.813	0.2749	69	0.9957	0.5023
1.0287	0.2869	72	1.0307	0.4805
1.1745	0.2988	75	0.9998	0.4862
0.7797	0.3108	78	0.9556	0.5080
0.68	0.3227	81	0.9344	0.5276
0.6929	0.3347	84	0.9413	0.5356
1.0455	0.3466	87	0.9584	0.5195
0.8388	0.3586	90	0.9676	0.5230
0.8741	0.3705	93	0.9797	0.5218
0.7958	0.3825	96	0.9849	0.5218
1.1008	0.3944	99	0.9723	0.5230
0.7522	0.4064	102	0.9543	0.5310
0.782	0.4183	105	0.9364	0.5310
0.7891	0.4303	108	0.9358	0.5414
0.9653	0.4422	111	0.9430	0.5356
0.6824	0.4542	114	0.9444	0.5414
1.0553	0.4661	117	0.9396	0.5414
0.9817	0.4781	120	0.9284	0.5460
0.9246	0.4900	123	0.9276	0.5471
0.9104	0.5020	126	0.9308	0.5517
0.968	0.5139	129	0.9407	0.5575
0.8514	0.5259	132	0.9508	0.5552
0.8625	0.5378	135	0.9539	0.5494
0.8176	0.5498	138	0.9444	0.5586
0.899	0.5618	141	0.9273	0.5540
0.7019	0.5737	144	0.9215	0.5471
0.7463	0.5857	147	0.9254	0.5483
0.7975	0.5976	150	0.9315	0.5448
0.8744	0.6096	153	0.9393	0.5379
1.0331	0.6215	156	0.9505	0.5356
1.261	0.6335	159	0.9463	0.5368
0.8668	0.6454	162	0.9384	0.5379
0.8954	0.6574	165	0.9303	0.5460
0.9757	0.6693	168	0.9264	0.5437
0.9377	0.6813	171	0.9239	0.5471
0.9672	0.6932	174	0.9170	0.5437
0.753	0.7052	177	0.9132	0.5471
0.8249	0.7171	180	0.9123	0.5471
0.9821	0.7291	183	0.9103	0.5529
0.5874	0.7410	186	0.9115	0.5437
1.2268	0.7530	189	0.9120	0.5448
0.9057	0.7649	192	0.9115	0.5471
0.7629	0.7769	195	0.9128	0.5460
0.7118	0.7888	198	0.9192	0.5517
0.7853	0.8008	201	0.9266	0.5414
1.0594	0.8127	204	0.9273	0.5448
0.9289	0.8247	207	0.9273	0.5402
0.925	0.8367	210	0.9251	0.5517
1.0229	0.8486	213	0.9218	0.5483
0.7402	0.8606	216	0.9193	0.5471
0.8425	0.8725	219	0.9185	0.5448
0.7125	0.8845	222	0.9191	0.5517
0.7757	0.8964	225	0.9198	0.5529
0.8713	0.9084	228	0.9202	0.5529
0.7946	0.9203	231	0.9205	0.5494
0.6899	0.9323	234	0.9213	0.5471
0.8105	0.9442	237	0.9223	0.5448
0.9919	0.9562	240	0.9225	0.5448
0.7766	0.9681	243	0.9222	0.5471
1.0589	0.9801	246	0.9218	0.5471
0.779	0.9920	249	0.9217	0.5460

Framework versions

Transformers 4.57.1
Pytorch 2.9.0+cpu
Datasets 4.4.1
Tokenizers 0.22.1

karlitoxz/ServiModelV2

ServiModelV2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

karlitoxz/
ServiModelV2