ASAP_FineTuningBERT_AugV5_k5_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: 1.6166
• Qwk: -0.1582
• Mse: 1.6183
• Rmse: 1.2721

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	11.5647	0.0035	11.5621	3.4003
No log	1.0	4	10.2947	0.0	10.2922	3.2082
No log	1.5	6	9.0405	0.0	9.0383	3.0064
No log	2.0	8	7.5117	0.0	7.5098	2.7404
6.7382	2.5	10	6.1602	0.0	6.1587	2.4817
6.7382	3.0	12	4.8371	0.0079	4.8359	2.1991
6.7382	3.5	14	3.7753	0.0	3.7744	1.9428
6.7382	4.0	16	2.9009	0.0	2.9002	1.7030
6.7382	4.5	18	2.2367	0.1238	2.2363	1.4954
2.4735	5.0	20	1.7328	0.0482	1.7327	1.3163
2.4735	5.5	22	1.9272	0.0172	1.9271	1.3882
2.4735	6.0	24	1.4921	0.0	1.4922	1.2215
2.4735	6.5	26	1.5170	0.0	1.5171	1.2317
2.4735	7.0	28	2.3766	-0.0400	2.3764	1.5416
1.8305	7.5	30	2.3210	-0.0522	2.3210	1.5235
1.8305	8.0	32	1.6197	0.0	1.6199	1.2727
1.8305	8.5	34	1.5999	0.0	1.6001	1.2649
1.8305	9.0	36	1.7838	0.0	1.7838	1.3356
1.8305	9.5	38	2.3189	-0.0065	2.3186	1.5227
1.886	10.0	40	2.0015	0.0618	2.0014	1.4147
1.886	10.5	42	1.5807	0.0	1.5808	1.2573
1.886	11.0	44	1.6351	0.0067	1.6352	1.2788
1.886	11.5	46	1.7611	0.0285	1.7613	1.3271
1.886	12.0	48	2.6442	-0.0192	2.6440	1.6260
1.6533	12.5	50	2.6906	-0.0204	2.6905	1.6403
1.6533	13.0	52	1.9880	0.0011	1.9883	1.4101
1.6533	13.5	54	1.5667	0.0106	1.5673	1.2519
1.6533	14.0	56	2.5662	-0.0206	2.5661	1.6019
1.6533	14.5	58	2.6220	-0.0343	2.6220	1.6193
1.4469	15.0	60	1.2531	-0.0227	1.2540	1.1198
1.4469	15.5	62	1.0764	0.0079	1.0775	1.0380
1.4469	16.0	64	2.0646	-0.0398	2.0650	1.4370
1.4469	16.5	66	2.5642	-0.0576	2.5643	1.6014
1.4469	17.0	68	1.3323	-0.0714	1.3334	1.1547
1.2134	17.5	70	1.2480	-0.0448	1.2493	1.1177
1.2134	18.0	72	2.0983	-0.0892	2.0989	1.4488
1.2134	18.5	74	1.7854	-0.0821	1.7863	1.3365
1.2134	19.0	76	1.4471	-0.0898	1.4484	1.2035
1.2134	19.5	78	1.8720	-0.1119	1.8730	1.3686
0.7057	20.0	80	1.4689	-0.1198	1.4705	1.2126
0.7057	20.5	82	1.8390	-0.0857	1.8403	1.3566
0.7057	21.0	84	1.5746	-0.1205	1.5762	1.2555
0.7057	21.5	86	1.5987	-0.1062	1.6004	1.2650
0.7057	22.0	88	1.7665	-0.0564	1.7679	1.3296
0.3914	22.5	90	1.5196	-0.0795	1.5214	1.2334
0.3914	23.0	92	1.8290	-0.0593	1.8304	1.3529
0.3914	23.5	94	1.5312	-0.0940	1.5332	1.2382
0.3914	24.0	96	1.7650	-0.1177	1.7669	1.3292
0.3914	24.5	98	1.7662	-0.1120	1.7682	1.3297
0.2337	25.0	100	2.0956	-0.0755	2.0973	1.4482
0.2337	25.5	102	1.7598	-0.1129	1.7618	1.3273
0.2337	26.0	104	1.6137	-0.0962	1.6158	1.2711
0.2337	26.5	106	1.4413	-0.0515	1.4437	1.2015
0.2337	27.0	108	2.0157	-0.0884	2.0172	1.4203
0.2313	27.5	110	2.0397	-0.0860	2.0412	1.4287
0.2313	28.0	112	1.4370	-0.0737	1.4394	1.1997
0.2313	28.5	114	1.4610	-0.0875	1.4634	1.2097
0.2313	29.0	116	2.1808	-0.1157	2.1821	1.4772
0.2313	29.5	118	2.1224	-0.1251	2.1240	1.4574
0.2362	30.0	120	1.5798	-0.1429	1.5822	1.2579
0.2362	30.5	122	1.7971	-0.1248	1.7991	1.3413
0.2362	31.0	124	2.1158	-0.1348	2.1172	1.4551
0.2362	31.5	126	1.8306	-0.1243	1.8323	1.3536
0.2362	32.0	128	1.5257	-0.1292	1.5280	1.2361
0.1815	32.5	130	1.8271	-0.1418	1.8288	1.3523
0.1815	33.0	132	2.2833	-0.1243	2.2844	1.5114
0.1815	33.5	134	1.7140	-0.1497	1.7158	1.3099
0.1815	34.0	136	1.5116	-0.1003	1.5142	1.2305
0.1815	34.5	138	1.5894	-0.1802	1.5918	1.2617
0.1837	35.0	140	2.1056	-0.1129	2.1070	1.4515
0.1837	35.5	142	1.8147	-0.1342	1.8164	1.3478
0.1837	36.0	144	1.4955	-0.1479	1.4979	1.2239
0.1837	36.5	146	1.5876	-0.1395	1.5896	1.2608
0.1837	37.0	148	2.0363	-0.0824	2.0376	1.4275
0.1677	37.5	150	1.6954	-0.1445	1.6972	1.3028
0.1677	38.0	152	1.5357	-0.1300	1.5382	1.2403
0.1677	38.5	154	1.6481	-0.1516	1.6504	1.2847
0.1677	39.0	156	2.0011	-0.1168	2.0025	1.4151
0.1677	39.5	158	1.7431	-0.1456	1.7447	1.3209
0.1616	40.0	160	1.4668	-0.1519	1.4687	1.2119
0.1616	40.5	162	1.6069	-0.1521	1.6086	1.2683
0.1616	41.0	164	2.0608	-0.1228	2.0620	1.4360
0.1616	41.5	166	1.7958	-0.1341	1.7976	1.3408
0.1616	42.0	168	1.5077	-0.1394	1.5100	1.2288
0.1464	42.5	170	1.5856	-0.1635	1.5875	1.2600
0.1464	43.0	172	2.0362	-0.0852	2.0373	1.4273
0.1464	43.5	174	1.8051	-0.0987	1.8064	1.3440
0.1464	44.0	176	1.5122	-0.1358	1.5142	1.2305
0.1464	44.5	178	1.6482	-0.1679	1.6499	1.2845
0.1192	45.0	180	1.6906	-0.1826	1.6924	1.3009
0.1192	45.5	182	1.6477	-0.1868	1.6497	1.2844
0.1192	46.0	184	1.7975	-0.1925	1.7993	1.3414
0.1192	46.5	186	1.7748	-0.1905	1.7766	1.3329
0.1192	47.0	188	1.6441	-0.1741	1.6461	1.2830
0.0905	47.5	190	1.8150	-0.1495	1.8166	1.3478
0.0905	48.0	192	1.7069	-0.1711	1.7087	1.3072
0.0905	48.5	194	1.6030	-0.1498	1.6052	1.2670
0.0905	49.0	196	1.8305	-0.1302	1.8323	1.3536
0.0905	49.5	198	1.8072	-0.1350	1.8090	1.3450
0.0933	50.0	200	1.5568	-0.1192	1.5591	1.2486
0.0933	50.5	202	1.5744	-0.1558	1.5764	1.2556
0.0933	51.0	204	1.7797	-0.1157	1.7813	1.3347
0.0933	51.5	206	1.6194	-0.1742	1.6213	1.2733
0.0933	52.0	208	1.6065	-0.1744	1.6084	1.2682
0.0801	52.5	210	1.6284	-0.1829	1.6302	1.2768
0.0801	53.0	212	1.7295	-0.1537	1.7311	1.3157
0.0801	53.5	214	1.7426	-0.1578	1.7444	1.3208
0.0801	54.0	216	1.7369	-0.1811	1.7389	1.3187
0.0801	54.5	218	1.7221	-0.1498	1.7240	1.3130
0.0865	55.0	220	1.5254	-0.1477	1.5275	1.2359
0.0865	55.5	222	1.5156	-0.1525	1.5175	1.2319
0.0865	56.0	224	1.7015	-0.0860	1.7029	1.3049
0.0865	56.5	226	1.5354	-0.1586	1.5372	1.2398
0.0865	57.0	228	1.5181	-0.1068	1.5201	1.2329
0.0914	57.5	230	1.6984	-0.1319	1.7001	1.3039
0.0914	58.0	232	1.6806	-0.1383	1.6823	1.2970
0.0914	58.5	234	1.5330	-0.1416	1.5349	1.2389
0.0914	59.0	236	1.5850	-0.1320	1.5866	1.2596
0.0914	59.5	238	1.6766	-0.1261	1.6780	1.2954
0.0841	60.0	240	1.6079	-0.1642	1.6095	1.2687
0.0841	60.5	242	1.5571	-0.1507	1.5590	1.2486
0.0841	61.0	244	1.6647	-0.1555	1.6665	1.2909
0.0841	61.5	246	1.6859	-0.1555	1.6877	1.2991
0.0841	62.0	248	1.6482	-0.1593	1.6502	1.2846
0.0716	62.5	250	1.7877	-0.1411	1.7894	1.3377
0.0716	63.0	252	1.7370	-0.1426	1.7387	1.3186
0.0716	63.5	254	1.5754	-0.1724	1.5773	1.2559
0.0716	64.0	256	1.6682	-0.1469	1.6700	1.2923
0.0716	64.5	258	1.6549	-0.1445	1.6567	1.2871
0.072	65.0	260	1.6259	-0.1603	1.6278	1.2759
0.072	65.5	262	1.7018	-0.1217	1.7035	1.3052
0.072	66.0	264	1.6397	-0.1240	1.6414	1.2812
0.072	66.5	266	1.5339	-0.1589	1.5358	1.2393
0.072	67.0	268	1.5325	-0.1572	1.5344	1.2387
0.0633	67.5	270	1.5390	-0.1534	1.5408	1.2413
0.0633	68.0	272	1.6272	-0.1317	1.6289	1.2763
0.0633	68.5	274	1.6251	-0.1347	1.6268	1.2755
0.0633	69.0	276	1.6184	-0.1456	1.6202	1.2729
0.0633	69.5	278	1.6925	-0.1262	1.6942	1.3016
0.0608	70.0	280	1.5904	-0.1343	1.5922	1.2618
0.0608	70.5	282	1.5616	-0.1500	1.5633	1.2503
0.0608	71.0	284	1.6800	-0.1166	1.6815	1.2967
0.0608	71.5	286	1.6569	-0.1264	1.6585	1.2878
0.0608	72.0	288	1.5280	-0.1442	1.5298	1.2369
0.0608	72.5	290	1.5257	-0.1473	1.5276	1.2360
0.0608	73.0	292	1.6153	-0.1414	1.6170	1.2716
0.0608	73.5	294	1.6603	-0.1301	1.6619	1.2892
0.0608	74.0	296	1.6194	-0.1426	1.6210	1.2732
0.0608	74.5	298	1.6376	-0.1422	1.6394	1.2804
0.0567	75.0	300	1.6955	-0.1421	1.6971	1.3027
0.0567	75.5	302	1.6128	-0.1644	1.6147	1.2707
0.0567	76.0	304	1.5957	-0.1744	1.5977	1.2640
0.0567	76.5	306	1.7191	-0.1481	1.7208	1.3118
0.0567	77.0	308	1.7538	-0.1169	1.7555	1.3249
0.057	77.5	310	1.6954	-0.1475	1.6971	1.3027
0.057	78.0	312	1.6927	-0.1303	1.6944	1.3017
0.057	78.5	314	1.6641	-0.1358	1.6658	1.2907
0.057	79.0	316	1.6643	-0.1393	1.6661	1.2908
0.057	79.5	318	1.6539	-0.1383	1.6556	1.2867
0.0551	80.0	320	1.6737	-0.1381	1.6755	1.2944
0.0551	80.5	322	1.6046	-0.1579	1.6064	1.2674
0.0551	81.0	324	1.5694	-0.1599	1.5712	1.2535
0.0551	81.5	326	1.6347	-0.1418	1.6364	1.2792
0.0551	82.0	328	1.7387	-0.1006	1.7402	1.3192
0.0519	82.5	330	1.7356	-0.0999	1.7371	1.3180
0.0519	83.0	332	1.6334	-0.1520	1.6351	1.2787
0.0519	83.5	334	1.5602	-0.1516	1.5620	1.2498
0.0519	84.0	336	1.5992	-0.1650	1.6009	1.2653
0.0519	84.5	338	1.7205	-0.1016	1.7221	1.3123
0.0602	85.0	340	1.7420	-0.1129	1.7435	1.3204
0.0602	85.5	342	1.6844	-0.1173	1.6861	1.2985
0.0602	86.0	344	1.6561	-0.1335	1.6578	1.2876
0.0602	86.5	346	1.6614	-0.1231	1.6630	1.2896
0.0602	87.0	348	1.6373	-0.1298	1.6390	1.2802
0.0524	87.5	350	1.5984	-0.1458	1.6000	1.2649
0.0524	88.0	352	1.6263	-0.1210	1.6279	1.2759
0.0524	88.5	354	1.6193	-0.1352	1.6209	1.2731
0.0524	89.0	356	1.5902	-0.1458	1.5918	1.2617
0.0524	89.5	358	1.6152	-0.1361	1.6168	1.2715
0.0495	90.0	360	1.6213	-0.1361	1.6230	1.2740
0.0495	90.5	362	1.6301	-0.1327	1.6318	1.2774
0.0495	91.0	364	1.6393	-0.1267	1.6409	1.2810
0.0495	91.5	366	1.6419	-0.1303	1.6436	1.2820
0.0495	92.0	368	1.6349	-0.1347	1.6366	1.2793
0.0507	92.5	370	1.6425	-0.1197	1.6441	1.2822
0.0507	93.0	372	1.6277	-0.1356	1.6294	1.2765
0.0507	93.5	374	1.5936	-0.1662	1.5953	1.2630
0.0507	94.0	376	1.5941	-0.1662	1.5958	1.2632
0.0507	94.5	378	1.6239	-0.1305	1.6255	1.2750
0.0527	95.0	380	1.6358	-0.1224	1.6374	1.2796
0.0527	95.5	382	1.6605	-0.1083	1.6621	1.2892
0.0527	96.0	384	1.6792	-0.1066	1.6808	1.2964
0.0527	96.5	386	1.6662	-0.1115	1.6678	1.2914
0.0527	97.0	388	1.6451	-0.1234	1.6467	1.2832
0.0477	97.5	390	1.6212	-0.1567	1.6229	1.2739
0.0477	98.0	392	1.6103	-0.1661	1.6120	1.2697
0.0477	98.5	394	1.6111	-0.1661	1.6128	1.2700
0.0477	99.0	396	1.6145	-0.1614	1.6162	1.2713
0.0477	99.5	398	1.6154	-0.1582	1.6171	1.2716
0.0516	100.0	400	1.6166	-0.1582	1.6183	1.2721

Framework versions

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