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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	2	11.1400	-0.0149	11.1400	3.3377
No log	2.0	4	10.2578	0.0018	10.2578	3.2028
No log	3.0	6	8.9269	0.0	8.9269	2.9878
No log	4.0	8	8.0360	0.0	8.0360	2.8348
No log	5.0	10	7.7713	0.0	7.7713	2.7877
No log	6.0	12	7.5819	0.0	7.5819	2.7535
No log	7.0	14	7.3674	0.0	7.3674	2.7143
No log	8.0	16	7.0881	0.0	7.0881	2.6624
No log	9.0	18	6.6408	0.0	6.6408	2.5770
No log	10.0	20	5.7820	-0.0201	5.7820	2.4046
No log	11.0	22	4.6482	0.0079	4.6482	2.1560
No log	12.0	24	4.0080	0.0040	4.0080	2.0020
No log	13.0	26	3.3040	0.0040	3.3040	1.8177
No log	14.0	28	2.3739	0.0928	2.3739	1.5408
No log	15.0	30	1.8480	0.0593	1.8480	1.3594
No log	16.0	32	1.5898	0.0432	1.5898	1.2609
No log	17.0	34	1.3127	0.0266	1.3127	1.1457
No log	18.0	36	1.4553	0.0230	1.4553	1.2064
No log	19.0	38	1.1327	0.0331	1.1327	1.0643
No log	20.0	40	1.2871	0.0253	1.2871	1.1345
No log	21.0	42	1.0815	0.0491	1.0815	1.0399
No log	22.0	44	1.2038	0.1499	1.2038	1.0972
No log	23.0	46	1.0968	0.1784	1.0968	1.0473
No log	24.0	48	1.0056	0.1322	1.0056	1.0028
No log	25.0	50	0.9736	0.2095	0.9736	0.9867
No log	26.0	52	0.9515	0.2517	0.9515	0.9755
No log	27.0	54	0.9492	0.2732	0.9492	0.9743
No log	28.0	56	0.9128	0.3001	0.9128	0.9554
No log	29.0	58	0.8941	0.2912	0.8941	0.9456
No log	30.0	60	0.8690	0.2737	0.8690	0.9322
No log	31.0	62	0.9522	0.3340	0.9522	0.9758
No log	32.0	64	0.8582	0.3094	0.8582	0.9264
No log	33.0	66	0.8290	0.2885	0.8290	0.9105
No log	34.0	68	0.7887	0.2857	0.7887	0.8881
No log	35.0	70	0.8026	0.2798	0.8026	0.8959
No log	36.0	72	0.7753	0.3098	0.7753	0.8805
No log	37.0	74	0.8708	0.3329	0.8708	0.9332
No log	38.0	76	0.7958	0.3340	0.7958	0.8921
No log	39.0	78	0.8120	0.3319	0.8120	0.9011
No log	40.0	80	0.7979	0.3336	0.7979	0.8932
No log	41.0	82	0.8623	0.3490	0.8623	0.9286
No log	42.0	84	0.8285	0.3593	0.8285	0.9102
No log	43.0	86	0.7942	0.3525	0.7942	0.8912
No log	44.0	88	0.8016	0.3672	0.8016	0.8953
No log	45.0	90	0.7824	0.3826	0.7824	0.8845
No log	46.0	92	0.7820	0.4263	0.7820	0.8843
No log	47.0	94	0.7823	0.4209	0.7823	0.8845
No log	48.0	96	0.8434	0.3910	0.8434	0.9184
No log	49.0	98	0.8721	0.3699	0.8721	0.9339
No log	50.0	100	0.8781	0.4348	0.8781	0.9371
No log	51.0	102	0.9193	0.4335	0.9193	0.9588
No log	52.0	104	0.9099	0.4027	0.9099	0.9539
No log	53.0	106	0.8979	0.3946	0.8979	0.9476
No log	54.0	108	0.9171	0.4763	0.9171	0.9577
No log	55.0	110	0.9348	0.4575	0.9348	0.9669
No log	56.0	112	0.8865	0.4424	0.8865	0.9415
No log	57.0	114	0.8764	0.4614	0.8764	0.9361
No log	58.0	116	0.8809	0.4580	0.8809	0.9385
No log	59.0	118	0.9024	0.4642	0.9024	0.9499
No log	60.0	120	0.9312	0.4710	0.9312	0.9650
No log	61.0	122	0.9577	0.4745	0.9577	0.9786
No log	62.0	124	0.9683	0.4866	0.9683	0.9840
No log	63.0	126	0.9621	0.5005	0.9621	0.9809
No log	64.0	128	0.9552	0.4904	0.9552	0.9773
No log	65.0	130	0.9469	0.5015	0.9469	0.9731
No log	66.0	132	0.9636	0.4951	0.9636	0.9816
No log	67.0	134	1.0067	0.4888	1.0067	1.0033
No log	68.0	136	1.0176	0.4780	1.0176	1.0088
No log	69.0	138	0.9940	0.4764	0.9940	0.9970
No log	70.0	140	0.9866	0.4802	0.9866	0.9933
No log	71.0	142	0.9686	0.5070	0.9686	0.9842
No log	72.0	144	0.9445	0.5129	0.9445	0.9718
No log	73.0	146	0.9292	0.4998	0.9292	0.9640
No log	74.0	148	0.9390	0.5097	0.9390	0.9690
No log	75.0	150	0.9877	0.5107	0.9877	0.9939
No log	76.0	152	1.0510	0.4711	1.0510	1.0252
No log	77.0	154	1.0773	0.4767	1.0773	1.0379
No log	78.0	156	1.0733	0.4777	1.0733	1.0360
No log	79.0	158	1.0679	0.4831	1.0679	1.0334
No log	80.0	160	1.0517	0.4842	1.0517	1.0255
No log	81.0	162	1.0192	0.5050	1.0192	1.0095
No log	82.0	164	1.0043	0.5191	1.0043	1.0021
No log	83.0	166	1.0168	0.5031	1.0168	1.0084
No log	84.0	168	1.0382	0.4927	1.0382	1.0189
No log	85.0	170	1.0565	0.5005	1.0565	1.0279
No log	86.0	172	1.0615	0.4953	1.0615	1.0303
No log	87.0	174	1.0628	0.4863	1.0628	1.0309
No log	88.0	176	1.0580	0.4882	1.0580	1.0286
No log	89.0	178	1.0600	0.4853	1.0600	1.0296
No log	90.0	180	1.0522	0.4852	1.0522	1.0258
No log	91.0	182	1.0607	0.4817	1.0607	1.0299
No log	92.0	184	1.0614	0.4786	1.0614	1.0303
No log	93.0	186	1.0484	0.4893	1.0484	1.0239
No log	94.0	188	1.0298	0.4934	1.0298	1.0148
No log	95.0	190	1.0142	0.5007	1.0142	1.0071
No log	96.0	192	1.0059	0.4937	1.0059	1.0029
No log	97.0	194	1.0012	0.4929	1.0012	1.0006
No log	98.0	196	1.0040	0.4943	1.0040	1.0020
No log	99.0	198	1.0079	0.4924	1.0079	1.0039
No log	100.0	200	1.0096	0.4924	1.0096	1.0048

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version3ASAP_FineTuningBERT_AugV14_k1_task1_organization_k1_k1_fold4

Version3ASAP_FineTuningBERT_AugV14_k1_task1_organization_k1_k1_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version3ASAP_FineTuningBERT_AugV14_k1_task1_organization_k1_k1_fold4