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	4	6.4699	0.0	6.4678	2.5432
No log	2.0	8	4.7128	0.0	4.7108	2.1704
No log	3.0	12	3.1325	0.0	3.1306	1.7694
No log	4.0	16	1.8986	0.0276	1.8970	1.3773
No log	5.0	20	1.2207	0.0	1.2192	1.1042
No log	6.0	24	0.9230	0.2347	0.9217	0.9601
No log	7.0	28	0.9393	0.1060	0.9380	0.9685
No log	8.0	32	0.9273	0.1471	0.9260	0.9623
No log	9.0	36	0.9084	0.1783	0.9072	0.9525
No log	10.0	40	0.6710	0.3512	0.6701	0.8186
No log	11.0	44	0.9460	0.2543	0.9443	0.9718
No log	12.0	48	0.7173	0.3444	0.7161	0.8462
No log	13.0	52	0.8723	0.3627	0.8707	0.9331
No log	14.0	56	0.9878	0.3658	0.9861	0.9930
No log	15.0	60	0.8127	0.3646	0.8113	0.9007
No log	16.0	64	0.7864	0.3580	0.7850	0.8860
No log	17.0	68	0.8068	0.4052	0.8057	0.8976
No log	18.0	72	0.7479	0.4135	0.7466	0.8641
No log	19.0	76	0.6870	0.4307	0.6857	0.8281
No log	20.0	80	0.8063	0.4195	0.8050	0.8972
No log	21.0	84	0.8557	0.4063	0.8552	0.9247
No log	22.0	88	0.6963	0.4238	0.6951	0.8337
No log	23.0	92	0.6597	0.4668	0.6586	0.8115
No log	24.0	96	0.6577	0.4525	0.6568	0.8104
No log	25.0	100	0.6822	0.4509	0.6811	0.8253
No log	26.0	104	0.6885	0.4504	0.6873	0.8291
No log	27.0	108	0.6618	0.4742	0.6607	0.8129
No log	28.0	112	0.6492	0.4893	0.6481	0.8050
No log	29.0	116	0.6405	0.5163	0.6395	0.7997
No log	30.0	120	0.6723	0.4792	0.6713	0.8193
No log	31.0	124	0.6688	0.4742	0.6677	0.8171
No log	32.0	128	0.6450	0.5094	0.6439	0.8024
No log	33.0	132	0.6248	0.5384	0.6238	0.7898
No log	34.0	136	0.6385	0.5166	0.6374	0.7984
No log	35.0	140	0.6769	0.4777	0.6756	0.8220
No log	36.0	144	0.6582	0.4798	0.6572	0.8107
No log	37.0	148	0.6455	0.4992	0.6443	0.8027
No log	38.0	152	0.6279	0.5253	0.6269	0.7918
No log	39.0	156	0.6207	0.4994	0.6196	0.7871
No log	40.0	160	0.6718	0.4791	0.6709	0.8191
No log	41.0	164	0.6777	0.5207	0.6767	0.8226
No log	42.0	168	0.6564	0.4910	0.6553	0.8095
No log	43.0	172	0.6732	0.4843	0.6724	0.8200
No log	44.0	176	0.6663	0.5104	0.6652	0.8156
No log	45.0	180	0.6970	0.4994	0.6962	0.8344
No log	46.0	184	0.6662	0.5116	0.6650	0.8155
No log	47.0	188	0.6446	0.5475	0.6435	0.8022
No log	48.0	192	0.6590	0.5126	0.6582	0.8113
No log	49.0	196	0.6343	0.5497	0.6333	0.7958
No log	50.0	200	0.6455	0.5621	0.6444	0.8028
No log	51.0	204	0.6235	0.5509	0.6226	0.7890
No log	52.0	208	0.6327	0.5075	0.6316	0.7947
No log	53.0	212	0.6332	0.5319	0.6323	0.7952
No log	54.0	216	0.6341	0.5639	0.6331	0.7957
No log	55.0	220	0.6198	0.5581	0.6189	0.7867
No log	56.0	224	0.6356	0.5867	0.6348	0.7968
No log	57.0	228	0.6159	0.5528	0.6152	0.7843
No log	58.0	232	0.5937	0.5828	0.5928	0.7700
No log	59.0	236	0.5997	0.5794	0.5988	0.7738
No log	60.0	240	0.6191	0.5498	0.6182	0.7862
No log	61.0	244	0.6597	0.5078	0.6589	0.8118
No log	62.0	248	0.6204	0.5649	0.6193	0.7870
No log	63.0	252	0.6496	0.5890	0.6488	0.8055
No log	64.0	256	0.6005	0.5436	0.5995	0.7743
No log	65.0	260	0.6718	0.5318	0.6711	0.8192
No log	66.0	264	0.6102	0.5278	0.6093	0.7806
No log	67.0	268	0.6291	0.5462	0.6284	0.7927
No log	68.0	272	0.6138	0.5520	0.6127	0.7828
No log	69.0	276	0.5950	0.5578	0.5941	0.7708
No log	70.0	280	0.6088	0.5387	0.6078	0.7796
No log	71.0	284	0.6258	0.5372	0.6249	0.7905
No log	72.0	288	0.6279	0.5163	0.6270	0.7918
No log	73.0	292	0.6264	0.5741	0.6256	0.7909
No log	74.0	296	0.6282	0.5785	0.6273	0.7920
No log	75.0	300	0.6476	0.5580	0.6468	0.8043
No log	76.0	304	0.6378	0.5065	0.6367	0.7979
No log	77.0	308	0.6418	0.5261	0.6410	0.8006
No log	78.0	312	0.6210	0.5332	0.6201	0.7874
No log	79.0	316	0.6190	0.5473	0.6181	0.7862
No log	80.0	320	0.6511	0.5250	0.6503	0.8064
No log	81.0	324	0.6075	0.5424	0.6065	0.7788
No log	82.0	328	0.6440	0.5440	0.6432	0.8020
No log	83.0	332	0.5927	0.5752	0.5918	0.7693
No log	84.0	336	0.5904	0.5944	0.5894	0.7678
No log	85.0	340	0.5974	0.5764	0.5965	0.7723
No log	86.0	344	0.6078	0.5589	0.6070	0.7791
No log	87.0	348	0.6081	0.5555	0.6073	0.7793
No log	88.0	352	0.6000	0.5495	0.5991	0.7740
No log	89.0	356	0.6232	0.5380	0.6224	0.7889
No log	90.0	360	0.6793	0.4705	0.6786	0.8237
No log	91.0	364	0.6317	0.5446	0.6309	0.7943
No log	92.0	368	0.6164	0.5688	0.6155	0.7845
No log	93.0	372	0.6315	0.5205	0.6308	0.7942
No log	94.0	376	0.6045	0.5437	0.6036	0.7769
No log	95.0	380	0.6151	0.5318	0.6144	0.7838
No log	96.0	384	0.6011	0.5671	0.6003	0.7748
No log	97.0	388	0.5980	0.5620	0.5972	0.7728
No log	98.0	392	0.5882	0.5756	0.5874	0.7664
No log	99.0	396	0.6212	0.5417	0.6205	0.7877
No log	100.0	400	0.6009	0.5542	0.6000	0.7746
No log	101.0	404	0.6435	0.4992	0.6428	0.8017
No log	102.0	408	0.6334	0.5070	0.6326	0.7954
No log	103.0	412	0.6037	0.5470	0.6029	0.7764
No log	104.0	416	0.6064	0.5488	0.6056	0.7782
No log	105.0	420	0.6119	0.5502	0.6111	0.7817
No log	106.0	424	0.6138	0.5538	0.6131	0.7830
No log	107.0	428	0.6041	0.5637	0.6033	0.7767
No log	108.0	432	0.6450	0.5268	0.6443	0.8027
No log	109.0	436	0.6037	0.5513	0.6028	0.7764
No log	110.0	440	0.6013	0.5490	0.6005	0.7749
No log	111.0	444	0.6328	0.5396	0.6321	0.7950
No log	112.0	448	0.5964	0.5721	0.5956	0.7717
No log	113.0	452	0.6113	0.5255	0.6105	0.7813
No log	114.0	456	0.6153	0.5148	0.6145	0.7839

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold1

Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold1