ASAP_FineTuningBERT_AugV7_k4_task1_organization_fold2

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0196
• Qwk: 0.3789
• Mse: 1.0197
• Rmse: 1.0098

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.5	2	11.7374	0.0047	11.7370	3.4259
No log	1.0	4	10.8417	0.0	10.8413	3.2926
No log	1.5	6	10.0939	0.0	10.0936	3.1770
No log	2.0	8	8.8515	0.0	8.8512	2.9751
7.8847	2.5	10	7.4135	0.0	7.4132	2.7227
7.8847	3.0	12	6.1974	0.0003	6.1972	2.4894
7.8847	3.5	14	5.0264	0.0088	5.0261	2.2419
7.8847	4.0	16	4.0422	0.0039	4.0421	2.0105
7.8847	4.5	18	3.1093	0.0	3.1092	1.7633
3.9009	5.0	20	2.3322	0.1742	2.3322	1.5271
3.9009	5.5	22	1.8698	0.0280	1.8699	1.3674
3.9009	6.0	24	1.4077	0.0280	1.4078	1.1865
3.9009	6.5	26	1.0986	0.0174	1.0989	1.0483
3.9009	7.0	28	0.9760	0.0174	0.9763	0.9881
1.9761	7.5	30	1.0286	0.0174	1.0289	1.0143
1.9761	8.0	32	1.9547	0.0107	1.9549	1.3982
1.9761	8.5	34	1.7148	0.0268	1.7150	1.3096
1.9761	9.0	36	1.6836	0.1306	1.6837	1.2976
1.9761	9.5	38	1.8875	0.1949	1.8876	1.3739
1.9492	10.0	40	1.1698	0.1052	1.1700	1.0817
1.9492	10.5	42	1.2001	0.1444	1.2003	1.0956
1.9492	11.0	44	1.6138	0.1906	1.6141	1.2705
1.9492	11.5	46	1.1645	0.2230	1.1649	1.0793
1.9492	12.0	48	0.9915	0.3143	0.9919	0.9959
1.4736	12.5	50	1.3967	0.2211	1.3973	1.1821
1.4736	13.0	52	2.1937	0.1357	2.1946	1.4814
1.4736	13.5	54	1.4565	0.2410	1.4574	1.2072
1.4736	14.0	56	1.0509	0.3095	1.0514	1.0254
1.4736	14.5	58	1.5637	0.1961	1.5646	1.2508
1.0046	15.0	60	1.2858	0.2656	1.2866	1.1343
1.0046	15.5	62	0.8360	0.3659	0.8364	0.9146
1.0046	16.0	64	1.4151	0.2221	1.4159	1.1899
1.0046	16.5	66	1.1948	0.2496	1.1955	1.0934
1.0046	17.0	68	0.9517	0.3117	0.9519	0.9757
0.5969	17.5	70	1.9196	0.1414	1.9207	1.3859
0.5969	18.0	72	1.8568	0.1322	1.8578	1.3630
0.5969	18.5	74	0.8169	0.3803	0.8173	0.9041
0.5969	19.0	76	0.8517	0.3776	0.8522	0.9231
0.5969	19.5	78	2.1324	0.1401	2.1333	1.4606
0.4577	20.0	80	2.5669	0.1144	2.5679	1.6025
0.4577	20.5	82	1.2434	0.2914	1.2440	1.1153
0.4577	21.0	84	0.8362	0.4471	0.8365	0.9146
0.4577	21.5	86	1.3318	0.2898	1.3323	1.1542
0.4577	22.0	88	1.6572	0.2457	1.6578	1.2876
0.2873	22.5	90	1.0598	0.3879	1.0602	1.0296
0.2873	23.0	92	1.2630	0.3178	1.2633	1.1240
0.2873	23.5	94	1.6916	0.2382	1.6921	1.3008
0.2873	24.0	96	1.4347	0.2517	1.4351	1.1980
0.2873	24.5	98	1.4752	0.2554	1.4756	1.2147
0.1926	25.0	100	0.9168	0.4451	0.9169	0.9576
0.1926	25.5	102	1.0303	0.4049	1.0305	1.0151
0.1926	26.0	104	1.9201	0.2155	1.9207	1.3859
0.1926	26.5	106	1.9368	0.2008	1.9375	1.3919
0.1926	27.0	108	1.0230	0.3646	1.0233	1.0116
0.228	27.5	110	0.8185	0.4455	0.8187	0.9048
0.228	28.0	112	1.2487	0.3036	1.2491	1.1176
0.228	28.5	114	1.4179	0.2746	1.4184	1.1910
0.228	29.0	116	1.0329	0.3754	1.0333	1.0165
0.228	29.5	118	1.2263	0.3074	1.2267	1.1076
0.1586	30.0	120	1.5759	0.1986	1.5765	1.2556
0.1586	30.5	122	1.0472	0.2966	1.0475	1.0235
0.1586	31.0	124	0.8825	0.3661	0.8828	0.9396
0.1586	31.5	126	1.1678	0.2707	1.1682	1.0808
0.1586	32.0	128	1.1753	0.2916	1.1757	1.0843
0.1249	32.5	130	1.1753	0.2866	1.1757	1.0843
0.1249	33.0	132	1.0132	0.3388	1.0135	1.0067
0.1249	33.5	134	1.3667	0.2650	1.3672	1.1693
0.1249	34.0	136	1.1800	0.3160	1.1804	1.0864
0.1249	34.5	138	0.9545	0.4010	0.9548	0.9772
0.0991	35.0	140	1.3722	0.3115	1.3727	1.1716
0.0991	35.5	142	1.3875	0.3108	1.3880	1.1781
0.0991	36.0	144	1.0763	0.3500	1.0767	1.0376
0.0991	36.5	146	1.1248	0.3217	1.1253	1.0608
0.0991	37.0	148	1.2685	0.3287	1.2690	1.1265
0.0957	37.5	150	1.0030	0.3636	1.0033	1.0016
0.0957	38.0	152	1.1511	0.3224	1.1514	1.0730
0.0957	38.5	154	1.5001	0.2740	1.5005	1.2250
0.0957	39.0	156	1.1317	0.3198	1.1320	1.0640
0.0957	39.5	158	0.8326	0.4564	0.8328	0.9126
0.1088	40.0	160	0.9809	0.3467	0.9811	0.9905
0.1088	40.5	162	1.3094	0.2801	1.3097	1.1444
0.1088	41.0	164	1.0696	0.3246	1.0698	1.0343
0.1088	41.5	166	0.8232	0.4474	0.8234	0.9074
0.1088	42.0	168	0.9581	0.3636	0.9585	0.9790
0.108	42.5	170	1.4226	0.2423	1.4231	1.1929
0.108	43.0	172	1.2943	0.2840	1.2948	1.1379
0.108	43.5	174	0.9676	0.3695	0.9680	0.9838
0.108	44.0	176	1.0523	0.3145	1.0526	1.0260
0.108	44.5	178	1.1240	0.3174	1.1244	1.0604
0.074	45.0	180	1.3938	0.2612	1.3943	1.1808
0.074	45.5	182	1.1557	0.3204	1.1560	1.0752
0.074	46.0	184	0.8314	0.4058	0.8316	0.9119
0.074	46.5	186	0.8794	0.3638	0.8796	0.9379
0.074	47.0	188	1.0943	0.3265	1.0946	1.0462
0.0808	47.5	190	1.0057	0.3408	1.0060	1.0030
0.0808	48.0	192	1.1093	0.3369	1.1095	1.0533
0.0808	48.5	194	1.0900	0.3349	1.0902	1.0441
0.0808	49.0	196	0.9176	0.4021	0.9178	0.9580
0.0808	49.5	198	1.0588	0.3591	1.0590	1.0291
0.0737	50.0	200	1.2457	0.3270	1.2459	1.1162
0.0737	50.5	202	1.1545	0.3727	1.1547	1.0746
0.0737	51.0	204	1.0858	0.3822	1.0860	1.0421
0.0737	51.5	206	1.0161	0.3662	1.0163	1.0081
0.0737	52.0	208	0.9233	0.3880	0.9235	0.9610
0.0665	52.5	210	0.8952	0.3963	0.8954	0.9463
0.0665	53.0	212	1.0286	0.3537	1.0289	1.0143
0.0665	53.5	214	1.0947	0.3238	1.0950	1.0464
0.0665	54.0	216	1.0592	0.3428	1.0595	1.0293
0.0665	54.5	218	0.9811	0.3538	0.9813	0.9906
0.0579	55.0	220	0.9720	0.3391	0.9723	0.9860
0.0579	55.5	222	0.9965	0.3470	0.9968	0.9984
0.0579	56.0	224	1.0850	0.3276	1.0853	1.0418
0.0579	56.5	226	1.0023	0.3309	1.0026	1.0013
0.0579	57.0	228	0.9501	0.3610	0.9504	0.9749
0.0563	57.5	230	0.9124	0.3701	0.9126	0.9553
0.0563	58.0	232	0.9774	0.3526	0.9777	0.9888
0.0563	58.5	234	1.1466	0.3165	1.1469	1.0709
0.0563	59.0	236	1.1491	0.3084	1.1494	1.0721
0.0563	59.5	238	0.9649	0.3479	0.9651	0.9824
0.0645	60.0	240	0.9396	0.3819	0.9398	0.9694
0.0645	60.5	242	1.1720	0.3335	1.1723	1.0827
0.0645	61.0	244	1.2109	0.3248	1.2112	1.1005
0.0645	61.5	246	0.9436	0.4005	0.9437	0.9715
0.0645	62.0	248	0.8681	0.4250	0.8682	0.9318
0.0708	62.5	250	0.9298	0.3993	0.9299	0.9643
0.0708	63.0	252	1.1080	0.3503	1.1082	1.0527
0.0708	63.5	254	1.1655	0.3393	1.1657	1.0797
0.0708	64.0	256	1.0339	0.3770	1.0341	1.0169
0.0708	64.5	258	1.0779	0.3544	1.0781	1.0383
0.0549	65.0	260	1.1479	0.3374	1.1481	1.0715
0.0549	65.5	262	1.0093	0.3678	1.0095	1.0048
0.0549	66.0	264	0.9517	0.3795	0.9518	0.9756
0.0549	66.5	266	1.0571	0.3400	1.0573	1.0282
0.0549	67.0	268	1.0585	0.3411	1.0587	1.0290
0.0466	67.5	270	0.9347	0.3693	0.9349	0.9669
0.0466	68.0	272	0.9324	0.3577	0.9326	0.9657
0.0466	68.5	274	1.0269	0.3491	1.0271	1.0135
0.0466	69.0	276	0.9460	0.3679	0.9462	0.9727
0.0466	69.5	278	0.9682	0.3686	0.9683	0.9840
0.0439	70.0	280	1.0188	0.3397	1.0189	1.0094
0.0439	70.5	282	1.1095	0.3429	1.1097	1.0534
0.0439	71.0	284	1.0016	0.3497	1.0018	1.0009
0.0439	71.5	286	0.9643	0.3559	0.9644	0.9821
0.0439	72.0	288	0.9189	0.3748	0.9191	0.9587
0.0561	72.5	290	0.9450	0.3728	0.9452	0.9722
0.0561	73.0	292	0.9677	0.3868	0.9678	0.9838
0.0561	73.5	294	1.0344	0.3652	1.0345	1.0171
0.0561	74.0	296	1.0716	0.3521	1.0718	1.0353
0.0561	74.5	298	1.0191	0.3692	1.0192	1.0095
0.0411	75.0	300	0.9071	0.3889	0.9072	0.9525
0.0411	75.5	302	0.9078	0.3902	0.9079	0.9529
0.0411	76.0	304	0.9828	0.3740	0.9829	0.9914
0.0411	76.5	306	1.0503	0.3703	1.0504	1.0249
0.0411	77.0	308	0.9994	0.3898	0.9994	0.9997
0.0393	77.5	310	0.9303	0.3833	0.9303	0.9645
0.0393	78.0	312	0.9532	0.3703	0.9533	0.9764
0.0393	78.5	314	1.0539	0.3581	1.0540	1.0267
0.0393	79.0	316	1.0735	0.3566	1.0737	1.0362
0.0393	79.5	318	0.9878	0.3622	0.9879	0.9939
0.0486	80.0	320	0.9411	0.3842	0.9413	0.9702
0.0486	80.5	322	0.9669	0.3638	0.9670	0.9834
0.0486	81.0	324	1.0752	0.3578	1.0753	1.0370
0.0486	81.5	326	1.0590	0.3600	1.0592	1.0292
0.0486	82.0	328	0.9616	0.3695	0.9617	0.9807
0.0409	82.5	330	0.8923	0.3947	0.8924	0.9447
0.0409	83.0	332	0.8921	0.3941	0.8922	0.9446
0.0409	83.5	334	0.9858	0.3630	0.9859	0.9929
0.0409	84.0	336	1.0948	0.3503	1.0950	1.0464
0.0409	84.5	338	1.0994	0.3399	1.0996	1.0486
0.0538	85.0	340	1.0496	0.3482	1.0497	1.0246
0.0538	85.5	342	0.9728	0.3648	0.9729	0.9864
0.0538	86.0	344	0.9470	0.3642	0.9472	0.9732
0.0538	86.5	346	0.9229	0.3714	0.9230	0.9607
0.0538	87.0	348	0.8861	0.3765	0.8862	0.9414
0.0444	87.5	350	0.8904	0.3731	0.8905	0.9437
0.0444	88.0	352	0.9320	0.3716	0.9322	0.9655
0.0444	88.5	354	0.9789	0.3602	0.9790	0.9895
0.0444	89.0	356	0.9866	0.3601	0.9868	0.9934
0.0444	89.5	358	1.0002	0.3685	1.0003	1.0002
0.0467	90.0	360	0.9722	0.3700	0.9723	0.9861
0.0467	90.5	362	0.9687	0.3678	0.9688	0.9843
0.0467	91.0	364	0.9361	0.3811	0.9363	0.9676
0.0467	91.5	366	0.9329	0.3838	0.9330	0.9659
0.0467	92.0	368	0.9614	0.3747	0.9616	0.9806
0.036	92.5	370	0.9800	0.3705	0.9802	0.9900
0.036	93.0	372	0.9883	0.3714	0.9884	0.9942
0.036	93.5	374	0.9541	0.3749	0.9542	0.9769
0.036	94.0	376	0.9375	0.3835	0.9376	0.9683
0.036	94.5	378	0.9277	0.3860	0.9278	0.9632
0.0446	95.0	380	0.9326	0.3860	0.9328	0.9658
0.0446	95.5	382	0.9425	0.3743	0.9427	0.9709
0.0446	96.0	384	0.9666	0.3733	0.9668	0.9832
0.0446	96.5	386	0.9996	0.3680	0.9998	0.9999
0.0446	97.0	388	1.0402	0.3731	1.0403	1.0200
0.0427	97.5	390	1.0567	0.3633	1.0568	1.0280
0.0427	98.0	392	1.0526	0.3644	1.0527	1.0260
0.0427	98.5	394	1.0413	0.3731	1.0415	1.0205
0.0427	99.0	396	1.0296	0.3721	1.0298	1.0148
0.0427	99.5	398	1.0215	0.3789	1.0216	1.0108
0.0396	100.0	400	1.0196	0.3789	1.0197	1.0098

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.2.0
• Tokenizers 0.19.1