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	10.9206	-0.0033	10.9205	3.3046
No log	2.0	4	9.7320	0.0	9.7321	3.1196
No log	3.0	6	8.1985	0.0	8.1986	2.8633
No log	4.0	8	6.6777	0.0	6.6779	2.5842
No log	5.0	10	5.3041	0.0209	5.3046	2.3032
No log	6.0	12	4.3084	0.0039	4.3088	2.0758
No log	7.0	14	3.4009	0.0039	3.4014	1.8443
No log	8.0	16	2.7033	0.0	2.7037	1.6443
No log	9.0	18	2.2855	0.0891	2.2859	1.5119
No log	10.0	20	1.7066	0.0280	1.7070	1.3065
No log	11.0	22	1.4833	0.0280	1.4837	1.2181
No log	12.0	24	1.1572	0.0213	1.1576	1.0759
No log	13.0	26	1.0442	0.0280	1.0446	1.0221
No log	14.0	28	1.1199	0.0602	1.1203	1.0584
No log	15.0	30	0.8674	0.1866	0.8678	0.9315
No log	16.0	32	0.7987	0.3334	0.7990	0.8939
No log	17.0	34	1.0214	0.3085	1.0218	1.0109
No log	18.0	36	1.0460	0.3426	1.0463	1.0229
No log	19.0	38	0.8044	0.3602	0.8046	0.8970
No log	20.0	40	0.8121	0.3747	0.8122	0.9012
No log	21.0	42	0.9318	0.3535	0.9319	0.9653
No log	22.0	44	1.1369	0.3194	1.1369	1.0663
No log	23.0	46	1.0501	0.3343	1.0501	1.0247
No log	24.0	48	0.8969	0.3531	0.8969	0.9471
No log	25.0	50	1.0497	0.3603	1.0496	1.0245
No log	26.0	52	0.9202	0.3783	0.9201	0.9592
No log	27.0	54	0.8295	0.4086	0.8293	0.9107
No log	28.0	56	0.9491	0.3627	0.9490	0.9742
No log	29.0	58	1.1063	0.3422	1.1062	1.0518
No log	30.0	60	0.9025	0.3802	0.9024	0.9499
No log	31.0	62	0.8427	0.4418	0.8425	0.9179
No log	32.0	64	0.8438	0.4569	0.8435	0.9184
No log	33.0	66	0.9947	0.4017	0.9944	0.9972
No log	34.0	68	0.9854	0.3955	0.9851	0.9925
No log	35.0	70	0.8514	0.4780	0.8509	0.9224
No log	36.0	72	0.8584	0.4759	0.8579	0.9262
No log	37.0	74	0.8461	0.4769	0.8457	0.9196
No log	38.0	76	0.8688	0.4534	0.8684	0.9319
No log	39.0	78	0.7345	0.5051	0.7341	0.8568
No log	40.0	80	0.7681	0.4796	0.7678	0.8762
No log	41.0	82	0.7209	0.4867	0.7206	0.8489
No log	42.0	84	0.7803	0.4437	0.7800	0.8832
No log	43.0	86	0.7957	0.4874	0.7953	0.8918
No log	44.0	88	0.8080	0.4852	0.8075	0.8986
No log	45.0	90	0.8537	0.4737	0.8531	0.9237
No log	46.0	92	0.8981	0.4553	0.8975	0.9473
No log	47.0	94	0.7722	0.5351	0.7716	0.8784
No log	48.0	96	0.7732	0.5161	0.7727	0.8790
No log	49.0	98	0.7160	0.5288	0.7155	0.8459
No log	50.0	100	0.7947	0.4855	0.7944	0.8913
No log	51.0	102	0.8287	0.4385	0.8283	0.9101
No log	52.0	104	0.6911	0.5049	0.6906	0.8310
No log	53.0	106	0.7618	0.5002	0.7613	0.8725
No log	54.0	108	0.7862	0.4812	0.7857	0.8864
No log	55.0	110	0.8086	0.4714	0.8081	0.8989
No log	56.0	112	0.8271	0.4766	0.8265	0.9091
No log	57.0	114	0.8174	0.4661	0.8169	0.9038
No log	58.0	116	0.7882	0.4747	0.7877	0.8875
No log	59.0	118	0.7266	0.5165	0.7261	0.8521
No log	60.0	120	0.7079	0.5172	0.7074	0.8410
No log	61.0	122	0.6791	0.5214	0.6786	0.8238
No log	62.0	124	0.6759	0.5500	0.6754	0.8218
No log	63.0	126	0.6654	0.5402	0.6649	0.8154
No log	64.0	128	0.6828	0.5248	0.6823	0.8260
No log	65.0	130	0.7007	0.5226	0.7001	0.8367
No log	66.0	132	0.7021	0.5525	0.7015	0.8376
No log	67.0	134	0.7331	0.5562	0.7325	0.8559
No log	68.0	136	0.7144	0.5717	0.7138	0.8449
No log	69.0	138	0.7043	0.5180	0.7037	0.8389
No log	70.0	140	0.7138	0.5078	0.7132	0.8445
No log	71.0	142	0.7185	0.5142	0.7179	0.8473
No log	72.0	144	0.6729	0.5386	0.6723	0.8199
No log	73.0	146	0.6677	0.5716	0.6670	0.8167
No log	74.0	148	0.6797	0.5854	0.6791	0.8241
No log	75.0	150	0.6692	0.5454	0.6686	0.8177
No log	76.0	152	0.7016	0.5417	0.7010	0.8373
No log	77.0	154	0.6865	0.5449	0.6859	0.8282
No log	78.0	156	0.6837	0.5921	0.6832	0.8265
No log	79.0	158	0.7114	0.5741	0.7108	0.8431
No log	80.0	160	0.6889	0.5804	0.6884	0.8297
No log	81.0	162	0.6707	0.5601	0.6702	0.8187
No log	82.0	164	0.6755	0.5468	0.6750	0.8216
No log	83.0	166	0.6636	0.5490	0.6631	0.8143
No log	84.0	168	0.6630	0.5906	0.6624	0.8139
No log	85.0	170	0.6572	0.5816	0.6567	0.8104
No log	86.0	172	0.6499	0.5717	0.6494	0.8058
No log	87.0	174	0.6440	0.5535	0.6435	0.8022
No log	88.0	176	0.6427	0.5582	0.6422	0.8014
No log	89.0	178	0.6417	0.5623	0.6412	0.8008
No log	90.0	180	0.6407	0.5625	0.6403	0.8002
No log	91.0	182	0.6446	0.5467	0.6442	0.8026
No log	92.0	184	0.6491	0.5471	0.6486	0.8054
No log	93.0	186	0.6519	0.5461	0.6514	0.8071
No log	94.0	188	0.6535	0.5428	0.6530	0.8081
No log	95.0	190	0.6530	0.5428	0.6526	0.8078
No log	96.0	192	0.6514	0.5551	0.6509	0.8068
No log	97.0	194	0.6517	0.5644	0.6512	0.8070
No log	98.0	196	0.6517	0.5644	0.6512	0.8070

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV17_k1_task1_organization_k1_k1_fold2

ASAP_FineTuningBERT_AugV17_k1_task1_organization_k1_k1_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV17_k1_task1_organization_k1_k1_fold2