ASAP_FineTuningBERT_AugV5_k3_task1_organization_fold4

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: 1.6695
• Qwk: 0.0090
• Mse: 1.6695
• Rmse: 1.2921

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	0.6667	2	10.5988	0.0023	10.5988	3.2556
No log	1.3333	4	9.5438	0.0005	9.5438	3.0893
No log	2.0	6	7.9266	0.0	7.9266	2.8154
No log	2.6667	8	6.1629	0.0	6.1629	2.4825
5.7044	3.3333	10	4.5874	0.0186	4.5874	2.1418
5.7044	4.0	12	4.1725	0.0315	4.1725	2.0427
5.7044	4.6667	14	2.6912	-0.0034	2.6912	1.6405
5.7044	5.3333	16	2.0821	0.0953	2.0821	1.4430
5.7044	6.0	18	1.5019	0.0420	1.5019	1.2255
2.0115	6.6667	20	1.4309	0.0420	1.4309	1.1962
2.0115	7.3333	22	1.7121	0.0408	1.7121	1.3085
2.0115	8.0	24	1.6904	0.0344	1.6904	1.3002
2.0115	8.6667	26	1.5511	0.0317	1.5511	1.2455
2.0115	9.3333	28	1.4209	0.0317	1.4209	1.1920
1.8824	10.0	30	1.4146	0.0317	1.4146	1.1894
1.8824	10.6667	32	1.5123	0.0317	1.5123	1.2297
1.8824	11.3333	34	1.5810	0.0317	1.5810	1.2574
1.8824	12.0	36	1.7113	0.0331	1.7113	1.3082
1.8824	12.6667	38	1.6690	0.0280	1.6690	1.2919
1.6664	13.3333	40	1.6783	0.0331	1.6783	1.2955
1.6664	14.0	42	1.3630	0.0239	1.3630	1.1675
1.6664	14.6667	44	1.3497	0.0253	1.3497	1.1618
1.6664	15.3333	46	2.0739	0.0005	2.0739	1.4401
1.6664	16.0	48	2.2954	-0.0288	2.2954	1.5151
1.5158	16.6667	50	1.6121	0.0136	1.6121	1.2697
1.5158	17.3333	52	1.6540	0.0077	1.6540	1.2861
1.5158	18.0	54	2.1624	-0.0017	2.1624	1.4705
1.5158	18.6667	56	1.4823	0.0300	1.4823	1.2175
1.5158	19.3333	58	0.9635	0.1368	0.9635	0.9816
1.1275	20.0	60	1.1617	0.0615	1.1617	1.0778
1.1275	20.6667	62	2.4893	-0.0226	2.4893	1.5778
1.1275	21.3333	64	2.0219	0.0207	2.0219	1.4219
1.1275	22.0	66	1.1316	0.0358	1.1316	1.0637
1.1275	22.6667	68	1.4830	0.0274	1.4830	1.2178
0.6554	23.3333	70	3.0604	-0.0459	3.0604	1.7494
0.6554	24.0	72	2.1846	-0.0054	2.1846	1.4781
0.6554	24.6667	74	1.1924	0.0076	1.1924	1.0920
0.6554	25.3333	76	1.4607	-0.0033	1.4607	1.2086
0.6554	26.0	78	2.4428	-0.0204	2.4428	1.5629
0.4807	26.6667	80	1.7171	-0.0062	1.7171	1.3104
0.4807	27.3333	82	1.3570	0.0024	1.3570	1.1649
0.4807	28.0	84	1.9503	-0.0194	1.9503	1.3965
0.4807	28.6667	86	1.6694	-0.0232	1.6694	1.2921
0.4807	29.3333	88	1.4297	-0.0291	1.4297	1.1957
0.2636	30.0	90	2.3734	-0.0390	2.3734	1.5406
0.2636	30.6667	92	2.2928	-0.0253	2.2928	1.5142
0.2636	31.3333	94	1.5061	-0.0296	1.5061	1.2272
0.2636	32.0	96	1.7690	-0.0368	1.7690	1.3300
0.2636	32.6667	98	1.7113	-0.0436	1.7113	1.3082
0.2179	33.3333	100	1.5797	-0.0220	1.5797	1.2569
0.2179	34.0	102	2.3265	-0.0343	2.3265	1.5253
0.2179	34.6667	104	1.8528	-0.0116	1.8528	1.3612
0.2179	35.3333	106	1.2853	0.0029	1.2853	1.1337
0.2179	36.0	108	1.6370	-0.0003	1.6370	1.2794
0.2354	36.6667	110	2.1306	-0.0450	2.1306	1.4597
0.2354	37.3333	112	1.5763	-0.0043	1.5763	1.2555
0.2354	38.0	114	1.2895	0.0221	1.2895	1.1356
0.2354	38.6667	116	1.8594	-0.0053	1.8594	1.3636
0.2354	39.3333	118	2.3424	-0.0188	2.3424	1.5305
0.1866	40.0	120	1.6384	0.0274	1.6384	1.2800
0.1866	40.6667	122	1.5347	0.0309	1.5347	1.2388
0.1866	41.3333	124	1.6808	0.0158	1.6808	1.2964
0.1866	42.0	126	1.9078	-0.0093	1.9078	1.3812
0.1866	42.6667	128	1.5209	-0.0018	1.5209	1.2332
0.1327	43.3333	130	1.3266	0.0206	1.3266	1.1518
0.1327	44.0	132	1.8916	-0.0048	1.8916	1.3753
0.1327	44.6667	134	1.9784	0.0031	1.9784	1.4065
0.1327	45.3333	136	1.6576	-0.0080	1.6576	1.2875
0.1327	46.0	138	1.4757	0.0056	1.4757	1.2148
0.1257	46.6667	140	1.6314	0.0192	1.6314	1.2773
0.1257	47.3333	142	1.6237	0.0228	1.6237	1.2743
0.1257	48.0	144	1.5986	0.0129	1.5986	1.2644
0.1257	48.6667	146	1.7375	0.0002	1.7375	1.3181
0.1257	49.3333	148	1.5309	0.0028	1.5309	1.2373
0.0827	50.0	150	1.7916	0.0047	1.7916	1.3385
0.0827	50.6667	152	1.9833	0.0135	1.9833	1.4083
0.0827	51.3333	154	1.6587	0.0051	1.6587	1.2879
0.0827	52.0	156	1.3197	0.0339	1.3197	1.1488
0.0827	52.6667	158	1.5885	0.0188	1.5885	1.2604
0.1036	53.3333	160	2.1758	0.0146	2.1758	1.4751
0.1036	54.0	162	1.8611	-0.0077	1.8611	1.3642
0.1036	54.6667	164	1.3877	0.0092	1.3877	1.1780
0.1036	55.3333	166	1.4176	0.0332	1.4176	1.1906
0.1036	56.0	168	1.8384	0.0106	1.8384	1.3559
0.1169	56.6667	170	1.7077	-0.0020	1.7077	1.3068
0.1169	57.3333	172	1.5188	0.0142	1.5188	1.2324
0.1169	58.0	174	1.5466	0.0124	1.5466	1.2436
0.1169	58.6667	176	1.7099	0.0002	1.7099	1.3076
0.1169	59.3333	178	1.8646	0.0009	1.8646	1.3655
0.0701	60.0	180	1.8398	-0.0012	1.8398	1.3564
0.0701	60.6667	182	1.6811	0.0117	1.6811	1.2966
0.0701	61.3333	184	1.7058	-0.0095	1.7058	1.3061
0.0701	62.0	186	1.4829	0.0026	1.4829	1.2177
0.0701	62.6667	188	1.5136	0.0036	1.5136	1.2303
0.0897	63.3333	190	1.7987	-0.0105	1.7987	1.3412
0.0897	64.0	192	1.6390	0.0103	1.6390	1.2802
0.0897	64.6667	194	1.6387	0.0112	1.6387	1.2801
0.0897	65.3333	196	1.5059	0.0080	1.5059	1.2272
0.0897	66.0	198	1.5833	0.0119	1.5833	1.2583
0.0856	66.6667	200	1.5582	0.0050	1.5582	1.2483
0.0856	67.3333	202	1.4557	0.0082	1.4557	1.2065
0.0856	68.0	204	1.7197	0.0070	1.7197	1.3114
0.0856	68.6667	206	1.6998	0.0085	1.6998	1.3038
0.0856	69.3333	208	1.5210	0.0014	1.5210	1.2333
0.0796	70.0	210	1.5423	-0.0078	1.5423	1.2419
0.0796	70.6667	212	1.5661	0.0060	1.5661	1.2514
0.0796	71.3333	214	1.7493	0.0074	1.7493	1.3226
0.0796	72.0	216	1.6922	0.0200	1.6922	1.3008
0.0796	72.6667	218	1.4751	0.0040	1.4751	1.2145
0.0713	73.3333	220	1.3377	0.0061	1.3377	1.1566
0.0713	74.0	222	1.4249	0.0070	1.4249	1.1937
0.0713	74.6667	224	1.7430	0.0132	1.7430	1.3202
0.0713	75.3333	226	1.8524	0.0067	1.8524	1.3610
0.0713	76.0	228	1.7285	0.0155	1.7285	1.3147
0.0608	76.6667	230	1.6409	0.0295	1.6409	1.2810
0.0608	77.3333	232	1.5394	0.0114	1.5394	1.2407
0.0608	78.0	234	1.5399	0.0105	1.5399	1.2409
0.0608	78.6667	236	1.5815	0.0039	1.5815	1.2576
0.0608	79.3333	238	1.5948	0.0067	1.5948	1.2629
0.0529	80.0	240	1.5860	0.0226	1.5860	1.2594
0.0529	80.6667	242	1.5170	0.0238	1.5170	1.2316
0.0529	81.3333	244	1.4719	0.0250	1.4719	1.2132
0.0529	82.0	246	1.4999	0.0349	1.4999	1.2247
0.0529	82.6667	248	1.6012	0.0196	1.6012	1.2654
0.0479	83.3333	250	1.5851	0.0048	1.5851	1.2590
0.0479	84.0	252	1.5209	0.0139	1.5209	1.2332
0.0479	84.6667	254	1.5712	-0.0030	1.5712	1.2535
0.0479	85.3333	256	1.5356	0.0094	1.5356	1.2392
0.0479	86.0	258	1.5277	0.0104	1.5277	1.2360
0.0507	86.6667	260	1.5911	0.0113	1.5911	1.2614
0.0507	87.3333	262	1.6339	0.0093	1.6339	1.2782
0.0507	88.0	264	1.5916	0.0150	1.5916	1.2616
0.0507	88.6667	266	1.5888	0.0127	1.5888	1.2605
0.0507	89.3333	268	1.5978	0.0136	1.5978	1.2640
0.0518	90.0	270	1.6048	0.0071	1.6048	1.2668
0.0518	90.6667	272	1.6775	0.0108	1.6775	1.2952
0.0518	91.3333	274	1.7382	0.0044	1.7382	1.3184
0.0518	92.0	276	1.7267	0.0044	1.7267	1.3140
0.0518	92.6667	278	1.6363	0.0084	1.6363	1.2792
0.0418	93.3333	280	1.5515	0.0065	1.5515	1.2456
0.0418	94.0	282	1.4887	0.0001	1.4887	1.2201
0.0418	94.6667	284	1.4783	0.0001	1.4783	1.2159
0.0418	95.3333	286	1.5102	0.0049	1.5102	1.2289
0.0418	96.0	288	1.5810	0.0152	1.5810	1.2574
0.0507	96.6667	290	1.6551	0.0064	1.6551	1.2865
0.0507	97.3333	292	1.7034	-0.0024	1.7034	1.3051
0.0507	98.0	294	1.7112	-0.0002	1.7112	1.3081
0.0507	98.6667	296	1.6976	0.0057	1.6976	1.3029
0.0507	99.3333	298	1.6793	0.0071	1.6793	1.2959
0.0378	100.0	300	1.6695	0.0090	1.6695	1.2921

Framework versions

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