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	2	10.0557	0.0000	10.0535	3.1707
No log	2.0	4	8.5642	0.0	8.5617	2.9260
No log	3.0	6	6.9328	0.0	6.9304	2.6326
No log	4.0	8	5.6847	0.0546	5.6825	2.3838
No log	5.0	10	4.7548	0.0143	4.7526	2.1801
No log	6.0	12	4.2288	0.0040	4.2267	2.0559
No log	7.0	14	3.3991	0.0	3.3971	1.8431
No log	8.0	16	2.6804	0.0	2.6787	1.6367
No log	9.0	18	2.2067	0.1046	2.2051	1.4849
No log	10.0	20	1.9136	0.0790	1.9120	1.3828
No log	11.0	22	1.6555	0.0315	1.6540	1.2861
No log	12.0	24	1.5304	0.0	1.5288	1.2365
No log	13.0	26	1.4483	0.0	1.4467	1.2028
No log	14.0	28	1.1697	0.0	1.1683	1.0809
No log	15.0	30	1.1136	0.0	1.1123	1.0546
No log	16.0	32	1.1065	0.0	1.1052	1.0513
No log	17.0	34	0.9407	0.0	0.9395	0.9693
No log	18.0	36	0.9333	0.0336	0.9321	0.9655
No log	19.0	38	0.8605	0.2259	0.8594	0.9270
No log	20.0	40	0.8565	0.2434	0.8553	0.9248
No log	21.0	42	0.8890	0.3223	0.8878	0.9422
No log	22.0	44	0.6903	0.5261	0.6893	0.8302
No log	23.0	46	0.6453	0.5011	0.6443	0.8027
No log	24.0	48	0.6632	0.4922	0.6622	0.8138
No log	25.0	50	0.6525	0.4959	0.6515	0.8072
No log	26.0	52	0.6075	0.5259	0.6066	0.7788
No log	27.0	54	0.5827	0.5839	0.5818	0.7627
No log	28.0	56	0.5899	0.5636	0.5890	0.7674
No log	29.0	58	0.6207	0.5439	0.6197	0.7872
No log	30.0	60	0.6325	0.4692	0.6315	0.7947
No log	31.0	62	0.7543	0.3535	0.7533	0.8679
No log	32.0	64	0.6456	0.4725	0.6447	0.8029
No log	33.0	66	0.5882	0.4955	0.5874	0.7664
No log	34.0	68	0.5880	0.4961	0.5872	0.7663
No log	35.0	70	0.5231	0.5390	0.5223	0.7227
No log	36.0	72	0.4878	0.5840	0.4871	0.6980
No log	37.0	74	0.4829	0.5910	0.4823	0.6944
No log	38.0	76	0.4991	0.5674	0.4984	0.7060
No log	39.0	78	0.5507	0.5795	0.5500	0.7416
No log	40.0	80	0.5117	0.6257	0.5111	0.7149
No log	41.0	82	0.5276	0.6410	0.5270	0.7259
No log	42.0	84	0.4934	0.6604	0.4928	0.7020
No log	43.0	86	0.5271	0.6253	0.5264	0.7255
No log	44.0	88	0.6721	0.5079	0.6715	0.8194
No log	45.0	90	0.6330	0.5388	0.6325	0.7953
No log	46.0	92	0.6479	0.5327	0.6474	0.8046
No log	47.0	94	0.9294	0.4466	0.9289	0.9638
No log	48.0	96	1.1929	0.4207	1.1925	1.0920
No log	49.0	98	0.9869	0.4923	0.9865	0.9932
No log	50.0	100	0.5317	0.6263	0.5313	0.7289
No log	51.0	102	0.5244	0.6286	0.5240	0.7239
No log	52.0	104	0.8288	0.5476	0.8285	0.9102
No log	53.0	106	0.9121	0.5220	0.9117	0.9548
No log	54.0	108	0.5747	0.6194	0.5743	0.7578
No log	55.0	110	0.5072	0.6554	0.5068	0.7119
No log	56.0	112	0.5540	0.6160	0.5536	0.7440
No log	57.0	114	0.7158	0.5629	0.7154	0.8458
No log	58.0	116	0.5778	0.6259	0.5774	0.7599
No log	59.0	118	0.5738	0.6237	0.5734	0.7573
No log	60.0	120	0.7990	0.5626	0.7986	0.8937
No log	61.0	122	0.6885	0.5980	0.6881	0.8295
No log	62.0	124	0.7074	0.6056	0.7071	0.8409
No log	63.0	126	0.8456	0.5759	0.8454	0.9195
No log	64.0	128	0.6762	0.6440	0.6759	0.8221
No log	65.0	130	0.5887	0.6527	0.5884	0.7671
No log	66.0	132	0.8177	0.5761	0.8174	0.9041
No log	67.0	134	0.7735	0.5740	0.7732	0.8793
No log	68.0	136	0.5226	0.6324	0.5223	0.7227
No log	69.0	138	0.4944	0.6662	0.4940	0.7029
No log	70.0	140	0.5496	0.6367	0.5494	0.7412
No log	71.0	142	0.9055	0.5466	0.9053	0.9515
No log	72.0	144	1.0161	0.5282	1.0158	1.0079
No log	73.0	146	0.8356	0.5488	0.8353	0.9140
No log	74.0	148	0.5681	0.6222	0.5678	0.7535
No log	75.0	150	0.5227	0.6311	0.5224	0.7228
No log	76.0	152	0.5287	0.6244	0.5285	0.7270
No log	77.0	154	0.5613	0.6436	0.5611	0.7491
No log	78.0	156	0.6863	0.5884	0.6862	0.8284
No log	79.0	158	0.6820	0.5998	0.6819	0.8258
No log	80.0	160	0.5282	0.6572	0.5280	0.7266
No log	81.0	162	0.5209	0.6566	0.5207	0.7216
No log	82.0	164	0.6147	0.6414	0.6147	0.7840
No log	83.0	166	0.7138	0.6054	0.7138	0.8449
No log	84.0	168	0.6585	0.6300	0.6585	0.8115
No log	85.0	170	0.5396	0.6540	0.5394	0.7345
No log	86.0	172	0.5319	0.6631	0.5317	0.7292
No log	87.0	174	0.6457	0.6218	0.6455	0.8035
No log	88.0	176	0.8856	0.5616	0.8854	0.9410
No log	89.0	178	0.9323	0.5529	0.9322	0.9655
No log	90.0	180	0.7451	0.5815	0.7449	0.8631
No log	91.0	182	0.5481	0.6471	0.5478	0.7402
No log	92.0	184	0.4983	0.6707	0.4980	0.7057
No log	93.0	186	0.5742	0.6372	0.5740	0.7576
No log	94.0	188	0.6799	0.6099	0.6797	0.8244
No log	95.0	190	0.6937	0.6138	0.6936	0.8328
No log	96.0	192	0.5993	0.6280	0.5992	0.7741
No log	97.0	194	0.5667	0.6410	0.5666	0.7527
No log	98.0	196	0.5844	0.6453	0.5842	0.7643
No log	99.0	198	0.5914	0.6437	0.5913	0.7689
No log	100.0	200	0.6396	0.6099	0.6394	0.7996
No log	101.0	202	0.6742	0.5954	0.6740	0.8210
No log	102.0	204	0.5790	0.6291	0.5787	0.7608
No log	103.0	206	0.5644	0.6238	0.5641	0.7511
No log	104.0	208	0.6716	0.5955	0.6714	0.8194
No log	105.0	210	0.8978	0.5601	0.8976	0.9474
No log	106.0	212	0.9338	0.5436	0.9336	0.9662
No log	107.0	214	0.8140	0.5629	0.8138	0.9021
No log	108.0	216	0.6073	0.6105	0.6070	0.7791
No log	109.0	218	0.5467	0.6251	0.5464	0.7392
No log	110.0	220	0.5769	0.6216	0.5767	0.7594
No log	111.0	222	0.6508	0.6073	0.6505	0.8066
No log	112.0	224	0.6681	0.6004	0.6679	0.8172
No log	113.0	226	0.6505	0.6000	0.6502	0.8064
No log	114.0	228	0.6268	0.6099	0.6266	0.7916
No log	115.0	230	0.6406	0.5978	0.6404	0.8002
No log	116.0	232	0.5763	0.6100	0.5761	0.7590
No log	117.0	234	0.5006	0.6305	0.5003	0.7073
No log	118.0	236	0.4902	0.6453	0.4898	0.6999
No log	119.0	238	0.5090	0.6262	0.5087	0.7133
No log	120.0	240	0.5516	0.6185	0.5514	0.7425
No log	121.0	242	0.5753	0.6216	0.5750	0.7583
No log	122.0	244	0.6324	0.6081	0.6321	0.7951

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold1

Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold1