learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	3	8.5702	0.0	8.5703	2.9275
No log	2.0	6	6.9325	0.0	6.9327	2.6330
No log	3.0	9	4.9382	0.0039	4.9385	2.2223
No log	4.0	12	3.3982	0.0	3.3986	1.8435
No log	5.0	15	2.5286	0.0018	2.5290	1.5903
No log	6.0	18	1.9985	0.0501	1.9989	1.4138
No log	7.0	21	1.4483	0.0107	1.4488	1.2037
No log	8.0	24	1.1627	0.0	1.1631	1.0785
No log	9.0	27	0.9722	0.0107	0.9726	0.9862
No log	10.0	30	0.8727	0.0398	0.8730	0.9343
No log	11.0	33	0.7694	0.3941	0.7696	0.8773
No log	12.0	36	1.3762	0.2282	1.3768	1.1734
No log	13.0	39	0.6278	0.5267	0.6280	0.7925
No log	14.0	42	1.0484	0.3164	1.0488	1.0241
No log	15.0	45	0.5582	0.5355	0.5583	0.7472
No log	16.0	48	1.0586	0.3467	1.0589	1.0290
No log	17.0	51	0.5378	0.4981	0.5379	0.7334
No log	18.0	54	0.5605	0.5407	0.5605	0.7487
No log	19.0	57	0.5133	0.5278	0.5132	0.7164
No log	20.0	60	0.6734	0.5101	0.6732	0.8205
No log	21.0	63	0.5220	0.5128	0.5217	0.7223
No log	22.0	66	0.6123	0.5469	0.6120	0.7823
No log	23.0	69	0.6061	0.5370	0.6057	0.7782
No log	24.0	72	0.5775	0.5462	0.5769	0.7596
No log	25.0	75	0.8076	0.4697	0.8074	0.8985
No log	26.0	78	0.5965	0.5452	0.5961	0.7721
No log	27.0	81	0.9408	0.4234	0.9407	0.9699
No log	28.0	84	0.5426	0.5524	0.5423	0.7364
No log	29.0	87	0.5638	0.5281	0.5635	0.7506
No log	30.0	90	0.6180	0.5503	0.6176	0.7859
No log	31.0	93	0.5653	0.5270	0.5649	0.7516
No log	32.0	96	0.5635	0.5410	0.5632	0.7505
No log	33.0	99	0.5674	0.5158	0.5672	0.7531
No log	34.0	102	0.5942	0.5176	0.5941	0.7708
No log	35.0	105	0.6031	0.5392	0.6030	0.7766
No log	36.0	108	0.6130	0.5460	0.6128	0.7828
No log	37.0	111	0.6843	0.5259	0.6843	0.8272
No log	38.0	114	0.6204	0.5310	0.6202	0.7875
No log	39.0	117	0.6525	0.4986	0.6525	0.8078
No log	40.0	120	0.6006	0.5275	0.6005	0.7749
No log	41.0	123	0.7999	0.4426	0.7998	0.8943
No log	42.0	126	0.6515	0.5317	0.6514	0.8071
No log	43.0	129	0.5992	0.5428	0.5991	0.7740
No log	44.0	132	0.8589	0.4703	0.8590	0.9268
No log	45.0	135	0.6096	0.5337	0.6097	0.7808
No log	46.0	138	0.6652	0.5373	0.6652	0.8156
No log	47.0	141	0.6729	0.5229	0.6730	0.8204
No log	48.0	144	0.6515	0.5439	0.6514	0.8071
No log	49.0	147	0.5728	0.5362	0.5727	0.7568
No log	50.0	150	0.6002	0.5431	0.6001	0.7747
No log	51.0	153	0.5455	0.5402	0.5454	0.7385
No log	52.0	156	0.6260	0.5536	0.6258	0.7911
No log	53.0	159	0.5542	0.5570	0.5541	0.7444
No log	54.0	162	0.5304	0.5277	0.5303	0.7282
No log	55.0	165	0.5814	0.5331	0.5813	0.7624
No log	56.0	168	0.5784	0.5316	0.5783	0.7604
No log	57.0	171	0.5603	0.5190	0.5603	0.7485
No log	58.0	174	0.5829	0.5401	0.5827	0.7634
No log	59.0	177	0.5608	0.5467	0.5607	0.7488
No log	60.0	180	0.5384	0.5397	0.5383	0.7337
No log	61.0	183	0.5789	0.5463	0.5789	0.7608
No log	62.0	186	0.5525	0.5456	0.5524	0.7432
No log	63.0	189	0.5929	0.5644	0.5928	0.7699
No log	64.0	192	0.5574	0.5485	0.5573	0.7465
No log	65.0	195	0.5408	0.5426	0.5406	0.7353
No log	66.0	198	0.5445	0.5373	0.5444	0.7378
No log	67.0	201	0.5472	0.5287	0.5470	0.7396
No log	68.0	204	0.5585	0.5548	0.5583	0.7472
No log	69.0	207	0.5299	0.5349	0.5297	0.7278
No log	70.0	210	0.5515	0.5598	0.5513	0.7425
No log	71.0	213	0.5871	0.5634	0.5868	0.7660
No log	72.0	216	0.5458	0.5641	0.5454	0.7385
No log	73.0	219	0.5327	0.5571	0.5324	0.7297
No log	74.0	222	0.5312	0.5524	0.5309	0.7286
No log	75.0	225	0.5551	0.5641	0.5548	0.7448
No log	76.0	228	0.5370	0.5630	0.5367	0.7326
No log	77.0	231	0.5612	0.5602	0.5609	0.7489
No log	78.0	234	0.5435	0.5614	0.5432	0.7370
No log	79.0	237	0.5440	0.5559	0.5437	0.7374
No log	80.0	240	0.5432	0.5550	0.5430	0.7369
No log	81.0	243	0.5367	0.5631	0.5365	0.7325
No log	82.0	246	0.5956	0.5696	0.5953	0.7715
No log	83.0	249	0.5697	0.5622	0.5693	0.7546
No log	84.0	252	0.5416	0.5626	0.5414	0.7358
No log	85.0	255	0.5589	0.5551	0.5586	0.7474
No log	86.0	258	0.5389	0.5434	0.5386	0.7339
No log	87.0	261	0.5346	0.5315	0.5343	0.7310
No log	88.0	264	0.5379	0.5291	0.5376	0.7332
No log	89.0	267	0.5467	0.5356	0.5464	0.7392
No log	90.0	270	0.5346	0.5398	0.5344	0.7310
No log	91.0	273	0.5278	0.5364	0.5275	0.7263
No log	92.0	276	0.5400	0.5483	0.5397	0.7347
No log	93.0	279	0.5552	0.5655	0.5549	0.7449
No log	94.0	282	0.5493	0.5486	0.5491	0.7410
No log	95.0	285	0.5425	0.5645	0.5423	0.7364
No log	96.0	288	0.5330	0.5696	0.5328	0.7299
No log	97.0	291	0.5328	0.5530	0.5326	0.7298
No log	98.0	294	0.5648	0.5621	0.5645	0.7513
No log	99.0	297	0.5346	0.5461	0.5344	0.7310
No log	100.0	300	0.5414	0.5595	0.5412	0.7356
No log	101.0	303	0.5389	0.5645	0.5387	0.7340
No log	102.0	306	0.5534	0.5734	0.5532	0.7438
No log	103.0	309	0.5386	0.5700	0.5384	0.7337
No log	104.0	312	0.5354	0.5743	0.5352	0.7316
No log	105.0	315	0.5476	0.5673	0.5474	0.7399
No log	106.0	318	0.5223	0.5731	0.5221	0.7225
No log	107.0	321	0.5171	0.5666	0.5168	0.7189
No log	108.0	324	0.5234	0.5533	0.5232	0.7233
No log	109.0	327	0.5375	0.5547	0.5373	0.7330
No log	110.0	330	0.5256	0.5672	0.5254	0.7248
No log	111.0	333	0.5304	0.5695	0.5301	0.7281
No log	112.0	336	0.5219	0.5453	0.5217	0.7223
No log	113.0	339	0.5268	0.5490	0.5266	0.7256
No log	114.0	342	0.5458	0.5622	0.5455	0.7386
No log	115.0	345	0.5377	0.5510	0.5374	0.7331
No log	116.0	348	0.5335	0.5458	0.5332	0.7302
No log	117.0	351	0.5327	0.5507	0.5325	0.7297
No log	118.0	354	0.5204	0.5499	0.5202	0.7213
No log	119.0	357	0.5219	0.5464	0.5217	0.7223
No log	120.0	360	0.5249	0.5676	0.5247	0.7244
No log	121.0	363	0.5451	0.5650	0.5449	0.7382
No log	122.0	366	0.5300	0.5585	0.5298	0.7279
No log	123.0	369	0.5209	0.5456	0.5207	0.7216
No log	124.0	372	0.5227	0.5463	0.5224	0.7228
No log	125.0	375	0.5282	0.5471	0.5279	0.7266
No log	126.0	378	0.5401	0.5555	0.5398	0.7347
No log	127.0	381	0.5466	0.5462	0.5463	0.7392
No log	128.0	384	0.5405	0.5411	0.5403	0.7350
No log	129.0	387	0.5354	0.5396	0.5351	0.7315
No log	130.0	390	0.5355	0.5309	0.5353	0.7317
No log	131.0	393	0.5318	0.5356	0.5315	0.7291
No log	132.0	396	0.5315	0.5491	0.5313	0.7289
No log	133.0	399	0.5327	0.5415	0.5325	0.7297
No log	134.0	402	0.5311	0.5411	0.5309	0.7286

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version25NewTestASAP_FineTuningBERT_AugV25_k3_task1_organization_k3_k3_fold2

Version25NewTestASAP_FineTuningBERT_AugV25_k3_task1_organization_k3_k3_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version25NewTestASAP_FineTuningBERT_AugV25_k3_task1_organization_k3_k3_fold2