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	1	11.6290	0.0215	11.6274	3.4099
No log	2.0	2	9.9686	0.0055	9.9672	3.1571
No log	3.0	3	8.6595	0.0	8.6581	2.9425
No log	4.0	4	7.3304	0.0	7.3291	2.7072
No log	5.0	5	6.2254	0.0477	6.2242	2.4948
No log	6.0	6	5.4633	0.0506	5.4622	2.3371
No log	7.0	7	4.9088	0.0324	4.9079	2.2154
No log	8.0	8	4.5768	0.0134	4.5757	2.1391
No log	9.0	9	4.2506	0.0114	4.2495	2.0614
No log	10.0	10	3.7083	0.0	3.7073	1.9254
No log	11.0	11	3.1501	0.0	3.1492	1.7746
No log	12.0	12	2.7845	0.0	2.7836	1.6684
No log	13.0	13	2.4767	0.1539	2.4758	1.5735
No log	14.0	14	2.2204	0.1391	2.2196	1.4898
No log	15.0	15	2.0612	0.0868	2.0604	1.4354
No log	16.0	16	1.8881	0.0514	1.8874	1.3738
No log	17.0	17	1.6485	0.0488	1.6478	1.2837
No log	18.0	18	1.4074	0.0401	1.4067	1.1860
No log	19.0	19	1.2488	0.0302	1.2481	1.1172
No log	20.0	20	1.1333	0.0202	1.1328	1.0643
No log	21.0	21	1.0548	0.0202	1.0543	1.0268
No log	22.0	22	1.0044	0.0202	1.0039	1.0019
No log	23.0	23	0.9212	0.1361	0.9207	0.9595
No log	24.0	24	0.8493	0.3593	0.8488	0.9213
No log	25.0	25	0.8206	0.2655	0.8202	0.9056
No log	26.0	26	0.7858	0.2701	0.7854	0.8862
No log	27.0	27	0.7427	0.2738	0.7423	0.8616
No log	28.0	28	0.7302	0.2675	0.7298	0.8543
No log	29.0	29	0.7435	0.2339	0.7432	0.8621
No log	30.0	30	0.7289	0.2377	0.7286	0.8536
No log	31.0	31	0.7053	0.2562	0.7050	0.8397
No log	32.0	32	0.6871	0.2881	0.6868	0.8287
No log	33.0	33	0.6908	0.3097	0.6906	0.8310
No log	34.0	34	0.6544	0.3295	0.6542	0.8088
No log	35.0	35	0.6337	0.3629	0.6335	0.7959
No log	36.0	36	0.6031	0.4187	0.6029	0.7765
No log	37.0	37	0.6189	0.4784	0.6188	0.7867
No log	38.0	38	0.5612	0.5416	0.5612	0.7491
No log	39.0	39	0.5672	0.6041	0.5671	0.7531
No log	40.0	40	0.5827	0.6020	0.5826	0.7633
No log	41.0	41	0.5501	0.6451	0.5501	0.7417
No log	42.0	42	0.5527	0.6400	0.5526	0.7434
No log	43.0	43	0.5807	0.6434	0.5806	0.7620
No log	44.0	44	0.5910	0.6384	0.5909	0.7687
No log	45.0	45	0.6344	0.6415	0.6343	0.7964
No log	46.0	46	0.6234	0.6403	0.6233	0.7895
No log	47.0	47	0.6650	0.6448	0.6648	0.8154
No log	48.0	48	0.6518	0.6431	0.6516	0.8072
No log	49.0	49	0.6705	0.6336	0.6703	0.8187
No log	50.0	50	0.6845	0.6379	0.6842	0.8272
No log	51.0	51	0.6495	0.6371	0.6493	0.8058
No log	52.0	52	0.6153	0.6357	0.6151	0.7843
No log	53.0	53	0.6149	0.6335	0.6147	0.7840
No log	54.0	54	0.6997	0.6468	0.6993	0.8362
No log	55.0	55	0.7346	0.6359	0.7342	0.8568
No log	56.0	56	0.6425	0.6475	0.6421	0.8013
No log	57.0	57	0.5769	0.6467	0.5766	0.7593
No log	58.0	58	0.5732	0.6450	0.5729	0.7569
No log	59.0	59	0.6136	0.6563	0.6133	0.7831
No log	60.0	60	0.6510	0.6581	0.6506	0.8066
No log	61.0	61	0.6128	0.6586	0.6124	0.7825
No log	62.0	62	0.5946	0.6546	0.5942	0.7709
No log	63.0	63	0.6129	0.6603	0.6125	0.7826
No log	64.0	64	0.6277	0.6683	0.6273	0.7920
No log	65.0	65	0.6513	0.6672	0.6508	0.8067
No log	66.0	66	0.6180	0.6613	0.6176	0.7859
No log	67.0	67	0.6205	0.6616	0.6201	0.7875
No log	68.0	68	0.6558	0.6599	0.6554	0.8096
No log	69.0	69	0.6312	0.6672	0.6308	0.7942
No log	70.0	70	0.6038	0.6515	0.6035	0.7768
No log	71.0	71	0.6040	0.6502	0.6037	0.7770
No log	72.0	72	0.6295	0.6704	0.6291	0.7931
No log	73.0	73	0.6253	0.6692	0.6249	0.7905
No log	74.0	74	0.6124	0.6726	0.6120	0.7823
No log	75.0	75	0.6213	0.6738	0.6210	0.7880
No log	76.0	76	0.6617	0.6619	0.6612	0.8132
No log	77.0	77	0.6643	0.6616	0.6639	0.8148
No log	78.0	78	0.6264	0.6673	0.6260	0.7912
No log	79.0	79	0.6158	0.6776	0.6154	0.7845
No log	80.0	80	0.5966	0.6764	0.5962	0.7721
No log	81.0	81	0.6092	0.6791	0.6088	0.7803
No log	82.0	82	0.6546	0.6596	0.6541	0.8088
No log	83.0	83	0.6614	0.6580	0.6610	0.8130
No log	84.0	84	0.6914	0.6586	0.6909	0.8312
No log	85.0	85	0.6787	0.6594	0.6782	0.8235
No log	86.0	86	0.6383	0.6764	0.6379	0.7987
No log	87.0	87	0.6336	0.6780	0.6332	0.7958
No log	88.0	88	0.6334	0.6757	0.6330	0.7956
No log	89.0	89	0.6390	0.6681	0.6386	0.7991
No log	90.0	90	0.6598	0.6689	0.6594	0.8120
No log	91.0	91	0.6951	0.6591	0.6946	0.8335
No log	92.0	92	0.7050	0.6468	0.7045	0.8394
No log	93.0	93	0.6931	0.6591	0.6926	0.8323
No log	94.0	94	0.6695	0.6597	0.6691	0.8180
No log	95.0	95	0.6428	0.6614	0.6425	0.8015
No log	96.0	96	0.6286	0.6647	0.6282	0.7926
No log	97.0	97	0.6242	0.6652	0.6238	0.7898
No log	98.0	98	0.6249	0.6652	0.6245	0.7903
No log	99.0	99	0.6280	0.6697	0.6276	0.7922
No log	100.0	100	0.6285	0.6697	0.6281	0.7925

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version_weird_ASAP_FineTuningBERT_AugV12_k2_task1_organization_k2_k2_fold3

Version_weird_ASAP_FineTuningBERT_AugV12_k2_task1_organization_k2_k2_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version_weird_ASAP_FineTuningBERT_AugV12_k2_task1_organization_k2_k2_fold3