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	5	6.4415	0.0001	6.4415	2.5380
No log	2.0	10	3.5960	0.0079	3.5960	1.8963
No log	3.0	15	2.0749	0.1307	2.0749	1.4404
No log	4.0	20	1.2818	0.0509	1.2818	1.1322
No log	5.0	25	1.5192	0.1236	1.5192	1.2326
No log	6.0	30	1.1616	0.1964	1.1616	1.0778
No log	7.0	35	1.1014	0.2685	1.1014	1.0495
No log	8.0	40	0.7682	0.4501	0.7682	0.8765
No log	9.0	45	1.5972	0.2671	1.5972	1.2638
No log	10.0	50	0.8350	0.4151	0.8350	0.9138
No log	11.0	55	0.6592	0.4850	0.6592	0.8119
No log	12.0	60	1.2997	0.3583	1.2997	1.1400
No log	13.0	65	0.8738	0.4252	0.8738	0.9348
No log	14.0	70	0.9234	0.4575	0.9234	0.9609
No log	15.0	75	0.8433	0.4523	0.8433	0.9183
No log	16.0	80	1.0331	0.4008	1.0331	1.0164
No log	17.0	85	0.8675	0.4715	0.8675	0.9314
No log	18.0	90	1.3914	0.3609	1.3914	1.1796
No log	19.0	95	1.1679	0.3914	1.1679	1.0807
No log	20.0	100	0.9148	0.4106	0.9148	0.9564
No log	21.0	105	0.7493	0.4598	0.7493	0.8656
No log	22.0	110	0.8396	0.4606	0.8396	0.9163
No log	23.0	115	0.9157	0.4431	0.9157	0.9569
No log	24.0	120	0.8621	0.4384	0.8621	0.9285
No log	25.0	125	0.9933	0.4326	0.9933	0.9967
No log	26.0	130	0.7497	0.5092	0.7497	0.8658
No log	27.0	135	1.0817	0.4061	1.0817	1.0401
No log	28.0	140	0.7755	0.4526	0.7755	0.8806
No log	29.0	145	0.8250	0.4694	0.8250	0.9083
No log	30.0	150	0.9010	0.4517	0.9010	0.9492
No log	31.0	155	0.9045	0.4566	0.9045	0.9511
No log	32.0	160	0.8956	0.4382	0.8956	0.9463
No log	33.0	165	0.7899	0.4306	0.7899	0.8888
No log	34.0	170	0.7645	0.4310	0.7645	0.8743
No log	35.0	175	1.0332	0.4153	1.0332	1.0165
No log	36.0	180	0.7561	0.4338	0.7561	0.8696
No log	37.0	185	0.7050	0.4536	0.7050	0.8397
No log	38.0	190	1.1110	0.4055	1.1110	1.0540
No log	39.0	195	0.7175	0.4637	0.7175	0.8471
No log	40.0	200	0.8152	0.4596	0.8152	0.9029
No log	41.0	205	0.7787	0.4781	0.7787	0.8824
No log	42.0	210	0.6487	0.5117	0.6487	0.8054
No log	43.0	215	0.8734	0.4426	0.8734	0.9346
No log	44.0	220	0.6645	0.5053	0.6645	0.8152
No log	45.0	225	0.7868	0.4952	0.7868	0.8870
No log	46.0	230	0.8741	0.4593	0.8741	0.9349
No log	47.0	235	0.6701	0.5088	0.6701	0.8186
No log	48.0	240	1.0190	0.4121	1.0190	1.0095
No log	49.0	245	0.6543	0.5173	0.6543	0.8089
No log	50.0	250	0.7958	0.4730	0.7958	0.8921
No log	51.0	255	0.7281	0.4919	0.7281	0.8533
No log	52.0	260	0.6535	0.5265	0.6535	0.8084
No log	53.0	265	0.7914	0.4533	0.7914	0.8896
No log	54.0	270	0.6916	0.4868	0.6916	0.8316
No log	55.0	275	0.6915	0.5032	0.6915	0.8315
No log	56.0	280	0.8551	0.4545	0.8551	0.9247
No log	57.0	285	0.7546	0.4753	0.7546	0.8687
No log	58.0	290	0.6653	0.5059	0.6653	0.8157
No log	59.0	295	0.8104	0.4539	0.8104	0.9002
No log	60.0	300	0.7595	0.4712	0.7595	0.8715
No log	61.0	305	0.6900	0.4928	0.6900	0.8307
No log	62.0	310	0.7538	0.4829	0.7538	0.8682
No log	63.0	315	0.6874	0.4860	0.6874	0.8291
No log	64.0	320	0.6741	0.5139	0.6741	0.8210
No log	65.0	325	0.6863	0.5143	0.6863	0.8284
No log	66.0	330	0.6944	0.5087	0.6944	0.8333
No log	67.0	335	0.7359	0.4666	0.7359	0.8579
No log	68.0	340	0.6938	0.5014	0.6938	0.8330
No log	69.0	345	0.6738	0.5180	0.6738	0.8209
No log	70.0	350	0.6574	0.5327	0.6574	0.8108
No log	71.0	355	0.6721	0.5191	0.6721	0.8198
No log	72.0	360	0.6284	0.5288	0.6284	0.7927
No log	73.0	365	0.7548	0.4998	0.7548	0.8688
No log	74.0	370	0.6402	0.5253	0.6402	0.8001
No log	75.0	375	0.7444	0.4907	0.7444	0.8628
No log	76.0	380	0.6742	0.5121	0.6742	0.8211
No log	77.0	385	0.6737	0.5222	0.6737	0.8208
No log	78.0	390	0.7162	0.5055	0.7162	0.8463
No log	79.0	395	0.7296	0.4993	0.7296	0.8542
No log	80.0	400	0.6687	0.5203	0.6687	0.8177
No log	81.0	405	0.6535	0.5148	0.6535	0.8084
No log	82.0	410	0.7062	0.4835	0.7062	0.8404
No log	83.0	415	0.6591	0.5272	0.6591	0.8119
No log	84.0	420	0.6454	0.5051	0.6454	0.8033
No log	85.0	425	0.6927	0.4960	0.6927	0.8323
No log	86.0	430	0.6659	0.5157	0.6659	0.8160
No log	87.0	435	0.6846	0.4952	0.6846	0.8274
No log	88.0	440	0.7172	0.4989	0.7172	0.8469
No log	89.0	445	0.6771	0.5217	0.6771	0.8228
No log	90.0	450	0.7084	0.4859	0.7084	0.8417

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4

ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4