ASAP_FineTuningBERT_AugV5_k3_task1_organization_fold1

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: 1.5770
• Qwk: -0.0102
• Mse: 1.5770
• Rmse: 1.2558

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	10.3529	0.0018	10.3505	3.2172
No log	1.3333	4	9.3009	0.0	9.2987	3.0494
No log	2.0	6	7.7248	0.0	7.7230	2.7790
No log	2.6667	8	5.9605	0.0031	5.9591	2.4411
5.5544	3.3333	10	4.5198	0.0002	4.5186	2.1257
5.5544	4.0	12	3.4780	-0.0003	3.4774	1.8648
5.5544	4.6667	14	2.5786	0.0051	2.5780	1.6056
5.5544	5.3333	16	1.8068	0.0670	1.8067	1.3441
5.5544	6.0	18	1.5352	0.0211	1.5351	1.2390
1.9982	6.6667	20	1.7358	0.0337	1.7355	1.3174
1.9982	7.3333	22	1.9982	0.0321	1.9978	1.4134
1.9982	8.0	24	1.7746	0.0223	1.7744	1.3321
1.9982	8.6667	26	1.5984	0.0211	1.5983	1.2642
1.9982	9.3333	28	1.4154	0.0	1.4154	1.1897
1.8589	10.0	30	1.4647	0.0211	1.4647	1.2102
1.8589	10.6667	32	1.5804	0.0340	1.5802	1.2571
1.8589	11.3333	34	1.5855	0.0365	1.5853	1.2591
1.8589	12.0	36	1.5530	0.0249	1.5528	1.2461
1.8589	12.6667	38	1.3469	0.0237	1.3468	1.1605
1.4702	13.3333	40	1.4722	0.0222	1.4721	1.2133
1.4702	14.0	42	1.6363	0.0229	1.6360	1.2791
1.4702	14.6667	44	1.1825	0.0278	1.1826	1.0875
1.4702	15.3333	46	1.4202	0.0190	1.4202	1.1917
1.4702	16.0	48	1.7132	-0.0079	1.7130	1.3088
1.1655	16.6667	50	1.3832	0.0418	1.3832	1.1761
1.1655	17.3333	52	1.5787	0.0300	1.5786	1.2564
1.1655	18.0	54	1.2683	0.0573	1.2684	1.1262
1.1655	18.6667	56	1.2929	0.0637	1.2930	1.1371
1.1655	19.3333	58	1.0892	0.1211	1.0896	1.0438
0.7137	20.0	60	1.9000	-0.0364	1.8998	1.3783
0.7137	20.6667	62	1.7891	-0.0182	1.7889	1.3375
0.7137	21.3333	64	1.2000	0.0342	1.2003	1.0956
0.7137	22.0	66	1.2995	-0.0064	1.2996	1.1400
0.7137	22.6667	68	1.4393	0.0176	1.4392	1.1997
0.4101	23.3333	70	1.7233	-0.0046	1.7231	1.3127
0.4101	24.0	72	1.2892	-0.0316	1.2894	1.1355
0.4101	24.6667	74	1.5396	-0.0106	1.5396	1.2408
0.4101	25.3333	76	2.5072	-0.0767	2.5065	1.5832
0.4101	26.0	78	1.6807	0.0149	1.6805	1.2963
0.2601	26.6667	80	1.1640	0.0113	1.1644	1.0791
0.2601	27.3333	82	1.4648	0.0634	1.4648	1.2103
0.2601	28.0	84	2.8001	-0.0259	2.7993	1.6731
0.2601	28.6667	86	2.1999	-0.0118	2.1994	1.4830
0.2601	29.3333	88	1.1820	0.0836	1.1823	1.0873
0.3036	30.0	90	1.1431	0.0400	1.1434	1.0693
0.3036	30.6667	92	1.7733	0.0415	1.7731	1.3316
0.3036	31.3333	94	2.0864	-0.0174	2.0861	1.4443
0.3036	32.0	96	1.3504	0.0533	1.3506	1.1622
0.3036	32.6667	98	1.1643	-0.0324	1.1647	1.0792
0.2108	33.3333	100	1.5013	0.0502	1.5014	1.2253
0.2108	34.0	102	2.0350	-0.0116	2.0348	1.4265
0.2108	34.6667	104	1.5465	0.0435	1.5466	1.2436
0.2108	35.3333	106	1.1923	0.0177	1.1927	1.0921
0.2108	36.0	108	1.3776	0.0574	1.3778	1.1738
0.1923	36.6667	110	1.9453	0.0188	1.9451	1.3947
0.1923	37.3333	112	1.5885	0.0627	1.5885	1.2604
0.1923	38.0	114	1.3998	0.0537	1.3999	1.1832
0.1923	38.6667	116	1.7313	0.0330	1.7312	1.3157
0.1923	39.3333	118	2.0607	-0.0048	2.0604	1.4354
0.1707	40.0	120	1.5160	0.0697	1.5160	1.2313
0.1707	40.6667	122	1.4577	0.0436	1.4578	1.2074
0.1707	41.3333	124	1.5136	0.0651	1.5136	1.2303
0.1707	42.0	126	1.8107	0.0316	1.8105	1.3455
0.1707	42.6667	128	1.4629	0.0223	1.4630	1.2095
0.1369	43.3333	130	1.2161	-0.0179	1.2166	1.1030
0.1369	44.0	132	1.3854	-0.0178	1.3856	1.1771
0.1369	44.6667	134	2.0171	-0.0423	2.0167	1.4201
0.1369	45.3333	136	1.9181	-0.0324	1.9178	1.3848
0.1369	46.0	138	1.4862	-0.0199	1.4862	1.2191
0.16	46.6667	140	1.5928	-0.0171	1.5928	1.2621
0.16	47.3333	142	1.6610	-0.0143	1.6609	1.2887
0.16	48.0	144	1.5311	-0.0193	1.5311	1.2374
0.16	48.6667	146	1.7505	-0.0011	1.7504	1.3230
0.16	49.3333	148	1.5794	-0.0024	1.5794	1.2567
0.104	50.0	150	1.3268	-0.0149	1.3271	1.1520
0.104	50.6667	152	1.5172	-0.0027	1.5173	1.2318
0.104	51.3333	154	2.0114	-0.0448	2.0111	1.4181
0.104	52.0	156	1.8186	0.0071	1.8185	1.3485
0.104	52.6667	158	1.2853	-0.0310	1.2857	1.1339
0.1268	53.3333	160	1.1946	-0.0212	1.1951	1.0932
0.1268	54.0	162	1.4147	0.0106	1.4149	1.1895
0.1268	54.6667	164	1.8034	0.0091	1.8032	1.3428
0.1268	55.3333	166	1.6681	0.0300	1.6679	1.2915
0.1268	56.0	168	1.3279	0.0	1.3281	1.1524
0.1468	56.6667	170	1.3805	0.0193	1.3806	1.1750
0.1468	57.3333	172	1.6188	0.0304	1.6187	1.2723
0.1468	58.0	174	1.5785	0.0336	1.5784	1.2563
0.1468	58.6667	176	1.4164	0.0090	1.4166	1.1902
0.1468	59.3333	178	1.5242	0.0149	1.5242	1.2346
0.0636	60.0	180	2.0118	-0.0540	2.0114	1.4183
0.0636	60.6667	182	2.0617	-0.0679	2.0612	1.4357
0.0636	61.3333	184	1.6843	-0.0068	1.6841	1.2977
0.0636	62.0	186	1.2469	-0.0510	1.2472	1.1168
0.0636	62.6667	188	1.1903	-0.0510	1.1908	1.0912
0.1687	63.3333	190	1.3409	-0.0253	1.3411	1.1581
0.1687	64.0	192	1.8264	-0.0497	1.8261	1.3513
0.1687	64.6667	194	1.9955	-0.0373	1.9950	1.4124
0.1687	65.3333	196	1.7072	-0.0174	1.7069	1.3065
0.1687	66.0	198	1.3133	-0.0287	1.3135	1.1461
0.1208	66.6667	200	1.2528	-0.0263	1.2530	1.1194
0.1208	67.3333	202	1.3887	0.0025	1.3887	1.1784
0.1208	68.0	204	1.8347	-0.0338	1.8343	1.3544
0.1208	68.6667	206	2.0260	-0.0506	2.0255	1.4232
0.1208	69.3333	208	1.7850	-0.0370	1.7847	1.3359
0.1477	70.0	210	1.3946	-0.0017	1.3947	1.1810
0.1477	70.6667	212	1.2925	-0.0429	1.2927	1.1370
0.1477	71.3333	214	1.3883	0.0108	1.3884	1.1783
0.1477	72.0	216	1.6016	0.0103	1.6015	1.2655
0.1477	72.6667	218	1.5561	0.0145	1.5561	1.2474
0.0812	73.3333	220	1.3847	0.0027	1.3848	1.1768
0.0812	74.0	222	1.3654	-0.0054	1.3655	1.1686
0.0812	74.6667	224	1.4933	0.0175	1.4932	1.2220
0.0812	75.3333	226	1.6526	-0.0016	1.6524	1.2855
0.0812	76.0	228	1.5718	0.0111	1.5717	1.2537
0.0714	76.6667	230	1.4865	-0.0031	1.4865	1.2192
0.0714	77.3333	232	1.3780	0.0015	1.3781	1.1739
0.0714	78.0	234	1.4161	-0.0057	1.4162	1.1900
0.0714	78.6667	236	1.5134	-0.0003	1.5134	1.2302
0.0714	79.3333	238	1.5992	-0.0004	1.5991	1.2646
0.0651	80.0	240	1.5982	0.0006	1.5981	1.2642
0.0651	80.6667	242	1.4708	-0.0116	1.4709	1.2128
0.0651	81.3333	244	1.4396	-0.0134	1.4397	1.1999
0.0651	82.0	246	1.4504	-0.0120	1.4505	1.2044
0.0651	82.6667	248	1.5901	0.0051	1.5901	1.2610
0.0617	83.3333	250	1.6817	-0.0054	1.6815	1.2967
0.0617	84.0	252	1.6204	-0.0027	1.6203	1.2729
0.0617	84.6667	254	1.4757	-0.0089	1.4758	1.2148
0.0617	85.3333	256	1.3973	-0.0247	1.3975	1.1822
0.0617	86.0	258	1.4005	-0.0202	1.4007	1.1835
0.061	86.6667	260	1.4672	0.0091	1.4673	1.2113
0.061	87.3333	262	1.5403	0.0158	1.5403	1.2411
0.061	88.0	264	1.5752	0.0137	1.5752	1.2551
0.061	88.6667	266	1.5596	0.0147	1.5596	1.2488
0.061	89.3333	268	1.4976	-0.0003	1.4977	1.2238
0.0589	90.0	270	1.5031	-0.0004	1.5032	1.2260
0.0589	90.6667	272	1.5352	-0.0049	1.5353	1.2391
0.0589	91.3333	274	1.5372	-0.0086	1.5372	1.2398
0.0589	92.0	276	1.5574	0.0119	1.5574	1.2480
0.0589	92.6667	278	1.5288	-0.0111	1.5289	1.2365
0.062	93.3333	280	1.5116	-0.0054	1.5116	1.2295
0.062	94.0	282	1.4902	-0.0083	1.4902	1.2208
0.062	94.6667	284	1.5044	-0.0118	1.5045	1.2266
0.062	95.3333	286	1.5377	-0.0084	1.5378	1.2401
0.062	96.0	288	1.5616	-0.0165	1.5616	1.2497
0.0657	96.6667	290	1.5927	-0.0101	1.5927	1.2620
0.0657	97.3333	292	1.6092	0.0078	1.6092	1.2686
0.0657	98.0	294	1.6031	0.0030	1.6031	1.2661
0.0657	98.6667	296	1.5915	-0.0101	1.5915	1.2616
0.0657	99.3333	298	1.5822	-0.0090	1.5823	1.2579
0.053	100.0	300	1.5770	-0.0102	1.5770	1.2558

Framework versions

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