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	4	10.6065	0.0	10.6044	3.2564
No log	2.0	8	8.3175	0.0	8.3157	2.8837
No log	3.0	12	7.8062	0.0	7.8044	2.7936
No log	4.0	16	6.5655	0.0005	6.5640	2.5620
No log	5.0	20	4.5750	0.0	4.5740	2.1387
No log	6.0	24	2.4435	0.1806	2.4428	1.5629
No log	7.0	28	1.4697	0.0	1.4691	1.2121
No log	8.0	32	1.0039	0.0	1.0036	1.0018
No log	9.0	36	0.9694	0.1261	0.9692	0.9845
No log	10.0	40	1.1776	0.0	1.1773	1.0850
No log	11.0	44	0.8021	0.3925	0.8019	0.8955
No log	12.0	48	1.0753	0.0541	1.0750	1.0368
No log	13.0	52	0.8736	0.2651	0.8735	0.9346
No log	14.0	56	0.8680	0.2810	0.8683	0.9318
No log	15.0	60	0.8773	0.3361	0.8778	0.9369
No log	16.0	64	0.8004	0.4077	0.8012	0.8951
No log	17.0	68	0.8311	0.4125	0.8319	0.9121
No log	18.0	72	1.0200	0.3691	1.0208	1.0104
No log	19.0	76	1.0781	0.3581	1.0791	1.0388
No log	20.0	80	1.3191	0.3017	1.3200	1.1489
No log	21.0	84	1.0907	0.3817	1.0916	1.0448
No log	22.0	88	1.3203	0.3399	1.3213	1.1495
No log	23.0	92	1.1623	0.3590	1.1633	1.0786
No log	24.0	96	0.7761	0.4797	0.7769	0.8814
No log	25.0	100	0.9072	0.4220	0.9082	0.9530
No log	26.0	104	1.0968	0.3623	1.0978	1.0477
No log	27.0	108	0.7329	0.5131	0.7336	0.8565
No log	28.0	112	0.8682	0.4516	0.8691	0.9322
No log	29.0	116	1.0181	0.3928	1.0191	1.0095
No log	30.0	120	0.7312	0.5074	0.7319	0.8555
No log	31.0	124	1.3127	0.2978	1.3136	1.1461
No log	32.0	128	0.8265	0.4939	0.8273	0.9096
No log	33.0	132	1.2423	0.3168	1.2433	1.1150
No log	34.0	136	0.7126	0.4934	0.7132	0.8445
No log	35.0	140	0.9764	0.4189	0.9774	0.9886
No log	36.0	144	0.8547	0.4672	0.8556	0.9250
No log	37.0	148	0.7721	0.5036	0.7729	0.8791
No log	38.0	152	0.9459	0.4040	0.9467	0.9730
No log	39.0	156	0.9032	0.4440	0.9040	0.9508
No log	40.0	160	0.8385	0.4705	0.8392	0.9161
No log	41.0	164	0.9478	0.4185	0.9486	0.9740
No log	42.0	168	0.7910	0.4967	0.7918	0.8899
No log	43.0	172	0.9920	0.3929	0.9930	0.9965
No log	44.0	176	0.7834	0.5202	0.7842	0.8856
No log	45.0	180	1.0293	0.3786	1.0302	1.0150
No log	46.0	184	0.9809	0.3950	0.9817	0.9908
No log	47.0	188	0.8300	0.4709	0.8308	0.9115
No log	48.0	192	0.7675	0.4843	0.7682	0.8765
No log	49.0	196	0.8553	0.4172	0.8561	0.9252
No log	50.0	200	0.7521	0.4796	0.7528	0.8677
No log	51.0	204	0.8566	0.4297	0.8573	0.9259
No log	52.0	208	0.8573	0.4420	0.8581	0.9263
No log	53.0	212	0.8758	0.4419	0.8766	0.9363
No log	54.0	216	0.7746	0.5018	0.7754	0.8805
No log	55.0	220	0.8463	0.4541	0.8470	0.9203
No log	56.0	224	0.7109	0.5055	0.7115	0.8435
No log	57.0	228	0.9674	0.3602	0.9681	0.9839
No log	58.0	232	0.8515	0.4430	0.8522	0.9232
No log	59.0	236	0.8170	0.4924	0.8177	0.9042
No log	60.0	240	0.8250	0.4928	0.8258	0.9087
No log	61.0	244	0.8479	0.4791	0.8487	0.9212
No log	62.0	248	0.7429	0.5246	0.7436	0.8623
No log	63.0	252	0.9379	0.4057	0.9387	0.9689
No log	64.0	256	0.6687	0.5249	0.6693	0.8181
No log	65.0	260	0.6742	0.5251	0.6747	0.8214
No log	66.0	264	0.9373	0.4123	0.9381	0.9686
No log	67.0	268	0.7287	0.5190	0.7294	0.8540
No log	68.0	272	0.8932	0.4401	0.8939	0.9455
No log	69.0	276	0.8500	0.4765	0.8507	0.9223
No log	70.0	280	0.7412	0.5159	0.7419	0.8613
No log	71.0	284	0.8414	0.4731	0.8421	0.9177
No log	72.0	288	0.7432	0.5007	0.7439	0.8625
No log	73.0	292	0.7514	0.4858	0.7521	0.8672
No log	74.0	296	0.8728	0.4369	0.8735	0.9346
No log	75.0	300	0.8391	0.4745	0.8397	0.9164
No log	76.0	304	0.8314	0.4829	0.8320	0.9122
No log	77.0	308	0.8260	0.4798	0.8267	0.9093
No log	78.0	312	0.7691	0.5034	0.7698	0.8774
No log	79.0	316	0.8072	0.4683	0.8079	0.8988
No log	80.0	320	0.6913	0.5092	0.6919	0.8318
No log	81.0	324	0.7687	0.4980	0.7694	0.8771
No log	82.0	328	0.7606	0.4957	0.7613	0.8725
No log	83.0	332	0.8133	0.4715	0.8139	0.9022
No log	84.0	336	0.7828	0.4833	0.7835	0.8851
No log	85.0	340	0.7586	0.4975	0.7593	0.8714

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold3

ASAP_nosemantic_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_nosemantic_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold3