ASAP_FineTuningBERT_AugV5_k5_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: 1.4852
• Qwk: -0.0042
• Mse: 1.4855
• Rmse: 1.2188

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	11.8763	0.0009	11.8760	3.4462
No log	1.0	4	10.7359	0.0	10.7356	3.2765
No log	1.5	6	9.6523	0.0	9.6520	3.1068
No log	2.0	8	8.2248	0.0	8.2244	2.8678
7.119	2.5	10	6.7082	0.0	6.7079	2.5900
7.119	3.0	12	5.4057	0.0175	5.4055	2.3250
7.119	3.5	14	4.3645	0.0	4.3644	2.0891
7.119	4.0	16	3.3058	0.0	3.3057	1.8182
7.119	4.5	18	2.5812	0.0	2.5812	1.6066
2.651	5.0	20	2.4649	-0.0039	2.4650	1.5700
2.651	5.5	22	1.5727	0.0107	1.5728	1.2541
2.651	6.0	24	1.9704	-0.0078	1.9704	1.4037
2.651	6.5	26	1.3790	0.0	1.3791	1.1744
2.651	7.0	28	0.9704	0.0148	0.9706	0.9852
1.8394	7.5	30	1.7472	0.0	1.7473	1.3219
1.8394	8.0	32	1.6862	0.0	1.6864	1.2986
1.8394	8.5	34	1.0668	0.0060	1.0671	1.0330
1.8394	9.0	36	2.3213	-0.0157	2.3216	1.5237
1.8394	9.5	38	2.0945	0.0170	2.0948	1.4473
1.7437	10.0	40	0.8312	0.2639	0.8315	0.9119
1.7437	10.5	42	0.8256	0.2474	0.8258	0.9088
1.7437	11.0	44	1.0948	0.0020	1.0950	1.0464
1.7437	11.5	46	2.5768	0.0131	2.5770	1.6053
1.7437	12.0	48	2.2378	0.0183	2.2380	1.4960
1.7109	12.5	50	1.1503	0.0020	1.1505	1.0726
1.7109	13.0	52	1.0304	0.0139	1.0306	1.0152
1.7109	13.5	54	1.5776	0.0195	1.5779	1.2561
1.7109	14.0	56	2.2739	0.0008	2.2743	1.5081
1.7109	14.5	58	1.3161	0.0454	1.3165	1.1474
1.3649	15.0	60	1.0224	0.0829	1.0227	1.0113
1.3649	15.5	62	1.8539	0.0088	1.8545	1.3618
1.3649	16.0	64	1.7880	0.0328	1.7887	1.3374
1.3649	16.5	66	0.9277	0.1088	0.9281	0.9634
1.3649	17.0	68	1.4719	0.0081	1.4724	1.2134
0.8133	17.5	70	1.9911	-0.0239	1.9917	1.4113
0.8133	18.0	72	1.0399	0.0182	1.0402	1.0199
0.8133	18.5	74	1.3717	-0.0577	1.3719	1.1713
0.8133	19.0	76	2.2099	-0.0518	2.2104	1.4867
0.8133	19.5	78	1.1462	0.0011	1.1462	1.0706
0.4608	20.0	80	1.2812	0.0711	1.2812	1.1319
0.4608	20.5	82	2.3508	-0.0062	2.3511	1.5333
0.4608	21.0	84	1.2563	0.0762	1.2560	1.1207
0.4608	21.5	86	1.2902	0.0655	1.2899	1.1358
0.4608	22.0	88	1.9311	-0.0105	1.9311	1.3897
0.2855	22.5	90	1.2444	0.0124	1.2442	1.1154
0.2855	23.0	92	1.3341	-0.0059	1.3340	1.1550
0.2855	23.5	94	1.8019	-0.0501	1.8019	1.3423
0.2855	24.0	96	1.2545	-0.0151	1.2542	1.1199
0.2855	24.5	98	1.3238	-0.0174	1.3235	1.1504
0.2192	25.0	100	2.6024	-0.0816	2.6024	1.6132
0.2192	25.5	102	2.4496	-0.0650	2.4497	1.5651
0.2192	26.0	104	1.2284	0.0141	1.2282	1.1082
0.2192	26.5	106	1.2064	0.0491	1.2062	1.0983
0.2192	27.0	108	1.4935	-0.0079	1.4935	1.2221
0.3413	27.5	110	2.3217	-0.0562	2.3220	1.5238
0.3413	28.0	112	1.5922	-0.0370	1.5923	1.2619
0.3413	28.5	114	1.1728	0.0291	1.1727	1.0829
0.3413	29.0	116	1.2217	0.0215	1.2217	1.1053
0.3413	29.5	118	1.7645	-0.0408	1.7647	1.3284
0.2401	30.0	120	1.4475	-0.0167	1.4476	1.2032
0.2401	30.5	122	1.1728	0.0591	1.1727	1.0829
0.2401	31.0	124	1.5074	0.0268	1.5075	1.2278
0.2401	31.5	126	1.4937	0.0320	1.4938	1.2222
0.2401	32.0	128	1.2809	0.0479	1.2809	1.1318
0.1554	32.5	130	1.5515	-0.0275	1.5516	1.2456
0.1554	33.0	132	1.5708	-0.0350	1.5710	1.2534
0.1554	33.5	134	1.5117	-0.0159	1.5118	1.2295
0.1554	34.0	136	1.4684	0.0074	1.4685	1.2118
0.1554	34.5	138	1.6740	-0.0085	1.6742	1.2939
0.1028	35.0	140	1.4070	0.0152	1.4071	1.1862
0.1028	35.5	142	1.4596	-0.0037	1.4597	1.2082
0.1028	36.0	144	1.3547	-0.0406	1.3548	1.1639
0.1028	36.5	146	1.6595	-0.0762	1.6597	1.2883
0.1028	37.0	148	1.5575	-0.0616	1.5577	1.2481
0.1158	37.5	150	1.6870	-0.0749	1.6872	1.2989
0.1158	38.0	152	1.4428	-0.0574	1.4430	1.2012
0.1158	38.5	154	1.6117	-0.0593	1.6119	1.2696
0.1158	39.0	156	2.0997	-0.0441	2.1000	1.4491
0.1158	39.5	158	1.5408	-0.0222	1.5410	1.2414
0.1092	40.0	160	1.5296	-0.0086	1.5298	1.2368
0.1092	40.5	162	1.8495	-0.0343	1.8498	1.3601
0.1092	41.0	164	1.3725	-0.0409	1.3726	1.1716
0.1092	41.5	166	1.3758	-0.0168	1.3758	1.1730
0.1092	42.0	168	1.7392	-0.0530	1.7394	1.3189
0.0917	42.5	170	1.4577	-0.0229	1.4579	1.2074
0.0917	43.0	172	1.3837	-0.0072	1.3838	1.1764
0.0917	43.5	174	1.6090	-0.0143	1.6093	1.2686
0.0917	44.0	176	1.4142	-0.0249	1.4144	1.1893
0.0917	44.5	178	1.5240	-0.0337	1.5242	1.2346
0.0843	45.0	180	1.7093	-0.0802	1.7096	1.3075
0.0843	45.5	182	1.4922	-0.0629	1.4924	1.2216
0.0843	46.0	184	1.5010	-0.0574	1.5012	1.2252
0.0843	46.5	186	1.3308	-0.0473	1.3309	1.1537
0.0843	47.0	188	1.4318	-0.0322	1.4320	1.1967
0.0836	47.5	190	1.8419	-0.0629	1.8425	1.3574
0.0836	48.0	192	1.5278	-0.0065	1.5283	1.2362
0.0836	48.5	194	1.2978	-0.0342	1.2980	1.1393
0.0836	49.0	196	1.5664	-0.0278	1.5668	1.2517
0.0836	49.5	198	1.5636	-0.0272	1.5639	1.2506
0.0896	50.0	200	1.3666	-0.0471	1.3668	1.1691
0.0896	50.5	202	1.6588	-0.0436	1.6591	1.2881
0.0896	51.0	204	1.6191	-0.0455	1.6194	1.2726
0.0896	51.5	206	1.4623	-0.0318	1.4626	1.2094
0.0896	52.0	208	1.4966	-0.0172	1.4968	1.2235
0.0773	52.5	210	1.6613	-0.0095	1.6617	1.2891
0.0773	53.0	212	1.4049	0.0021	1.4052	1.1854
0.0773	53.5	214	1.4373	-0.0065	1.4376	1.1990
0.0773	54.0	216	1.5958	-0.0291	1.5961	1.2634
0.0773	54.5	218	1.4454	-0.0333	1.4457	1.2024
0.0687	55.0	220	1.4091	-0.0386	1.4094	1.1872
0.0687	55.5	222	1.6484	-0.0576	1.6488	1.2841
0.0687	56.0	224	1.5496	-0.0409	1.5499	1.2450
0.0687	56.5	226	1.2905	-0.0325	1.2907	1.1361
0.0687	57.0	228	1.4019	-0.0125	1.4022	1.1841
0.0696	57.5	230	1.6977	-0.0504	1.6982	1.3031
0.0696	58.0	232	1.4809	0.0042	1.4812	1.2171
0.0696	58.5	234	1.3361	-0.0090	1.3363	1.1560
0.0696	59.0	236	1.5811	-0.0226	1.5814	1.2575
0.0696	59.5	238	1.6554	-0.0376	1.6557	1.2867
0.0664	60.0	240	1.4235	-0.0326	1.4237	1.1932
0.0664	60.5	242	1.4047	-0.0227	1.4049	1.1853
0.0664	61.0	244	1.5051	0.0014	1.5053	1.2269
0.0664	61.5	246	1.5080	0.0014	1.5083	1.2281
0.0664	62.0	248	1.4394	-0.0235	1.4396	1.1998
0.0665	62.5	250	1.5026	-0.0187	1.5028	1.2259
0.0665	63.0	252	1.7206	-0.0586	1.7209	1.3118
0.0665	63.5	254	1.6201	-0.0271	1.6204	1.2730
0.0665	64.0	256	1.2826	-0.0270	1.2828	1.1326
0.0665	64.5	258	1.2206	-0.0399	1.2207	1.1049
0.0802	65.0	260	1.3948	-0.0601	1.3951	1.1812
0.0802	65.5	262	1.7397	-0.0563	1.7402	1.3191
0.0802	66.0	264	1.6850	-0.0595	1.6854	1.2982
0.0802	66.5	266	1.3559	-0.0449	1.3561	1.1645
0.0802	67.0	268	1.2955	-0.0319	1.2957	1.1383
0.0774	67.5	270	1.4937	-0.0124	1.4940	1.2223
0.0774	68.0	272	1.5489	-0.0068	1.5492	1.2447
0.0774	68.5	274	1.3468	-0.0152	1.3470	1.1606
0.0774	69.0	276	1.3488	-0.0273	1.3490	1.1615
0.0774	69.5	278	1.5682	-0.0331	1.5685	1.2524
0.061	70.0	280	1.5731	-0.0243	1.5734	1.2544
0.061	70.5	282	1.4411	-0.0135	1.4414	1.2006
0.061	71.0	284	1.3983	-0.0371	1.3986	1.1826
0.061	71.5	286	1.4600	-0.0049	1.4603	1.2084
0.061	72.0	288	1.4482	-0.0010	1.4485	1.2035
0.0524	72.5	290	1.3803	-0.0141	1.3806	1.1750
0.0524	73.0	292	1.3863	-0.0161	1.3866	1.1775
0.0524	73.5	294	1.4000	0.0028	1.4003	1.1834
0.0524	74.0	296	1.5445	-0.0195	1.5448	1.2429
0.0524	74.5	298	1.5321	-0.0089	1.5325	1.2379
0.0522	75.0	300	1.3178	0.0002	1.3181	1.1481
0.0522	75.5	302	1.2816	-0.0076	1.2819	1.1322
0.0522	76.0	304	1.4289	0.0101	1.4292	1.1955
0.0522	76.5	306	1.5266	0.0065	1.5270	1.2357
0.0522	77.0	308	1.4589	0.0170	1.4593	1.2080
0.0561	77.5	310	1.4046	0.0252	1.4050	1.1853
0.0561	78.0	312	1.4922	0.0166	1.4926	1.2217
0.0561	78.5	314	1.4925	0.0131	1.4929	1.2218
0.0561	79.0	316	1.5638	-0.0034	1.5642	1.2507
0.0561	79.5	318	1.4874	0.0139	1.4879	1.2198
0.0507	80.0	320	1.3243	0.0131	1.3247	1.1509
0.0507	80.5	322	1.3136	0.0181	1.3139	1.1463
0.0507	81.0	324	1.4468	0.0013	1.4472	1.2030
0.0507	81.5	326	1.5821	-0.0123	1.5825	1.2580
0.0507	82.0	328	1.5234	-0.0071	1.5238	1.2344
0.0578	82.5	330	1.4058	0.0036	1.4061	1.1858
0.0578	83.0	332	1.3699	0.0021	1.3703	1.1706
0.0578	83.5	334	1.4275	0.0055	1.4278	1.1949
0.0578	84.0	336	1.4520	0.0147	1.4523	1.2051
0.0578	84.5	338	1.4216	-0.0058	1.4219	1.1924
0.0482	85.0	340	1.4180	0.0113	1.4184	1.1910
0.0482	85.5	342	1.4686	0.0004	1.4689	1.2120
0.0482	86.0	344	1.4491	0.0051	1.4495	1.2039
0.0482	86.5	346	1.3732	0.0097	1.3735	1.1720
0.0482	87.0	348	1.3105	-0.0057	1.3108	1.1449
0.0459	87.5	350	1.3560	0.0187	1.3563	1.1646
0.0459	88.0	352	1.4969	-0.0043	1.4973	1.2236
0.0459	88.5	354	1.5637	-0.0183	1.5641	1.2506
0.0459	89.0	356	1.5045	-0.0079	1.5048	1.2267
0.0459	89.5	358	1.3803	0.0192	1.3806	1.1750
0.0491	90.0	360	1.3400	0.0244	1.3403	1.1577
0.0491	90.5	362	1.3608	0.0222	1.3611	1.1667
0.0491	91.0	364	1.4394	0.0164	1.4398	1.1999
0.0491	91.5	366	1.4648	0.0069	1.4651	1.2104
0.0491	92.0	368	1.4396	0.0137	1.4400	1.2000
0.0462	92.5	370	1.3863	0.0202	1.3866	1.1775
0.0462	93.0	372	1.3325	0.0033	1.3327	1.1544
0.0462	93.5	374	1.3454	0.0002	1.3457	1.1600
0.0462	94.0	376	1.4026	0.0199	1.4029	1.1845
0.0462	94.5	378	1.4780	-0.0079	1.4783	1.2159
0.0453	95.0	380	1.5155	-0.0079	1.5159	1.2312
0.0453	95.5	382	1.5046	-0.0061	1.5050	1.2268
0.0453	96.0	384	1.4737	-0.0023	1.4740	1.2141
0.0453	96.5	386	1.4432	0.0156	1.4436	1.2015
0.0453	97.0	388	1.4165	0.0190	1.4168	1.1903
0.0427	97.5	390	1.4103	0.0200	1.4106	1.1877
0.0427	98.0	392	1.4242	0.0101	1.4245	1.1935
0.0427	98.5	394	1.4478	0.0147	1.4482	1.2034
0.0427	99.0	396	1.4695	0.0004	1.4698	1.2124
0.0427	99.5	398	1.4819	-0.0042	1.4823	1.2175
0.0392	100.0	400	1.4852	-0.0042	1.4855	1.2188

Framework versions

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