ASAP_FineTuningBERT_AugV6_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: 1.0766
• Qwk: 0.2613
• Mse: 1.0762
• Rmse: 1.0374

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	11.5179	0.0035	11.5153	3.3934
No log	1.0	4	10.3416	0.0	10.3391	3.2155
No log	1.5	6	9.2539	0.0	9.2516	3.0416
No log	2.0	8	7.8692	0.0	7.8673	2.8049
7.2099	2.5	10	6.4911	0.0	6.4896	2.5475
7.2099	3.0	12	5.2107	0.0220	5.2095	2.2824
7.2099	3.5	14	4.2342	0.0	4.2331	2.0575
7.2099	4.0	16	3.3514	0.0	3.3506	1.8305
7.2099	4.5	18	2.5474	0.0	2.5469	1.5959
3.1832	5.0	20	1.9189	0.0379	1.9187	1.3852
3.1832	5.5	22	1.4923	0.0	1.4923	1.2216
3.1832	6.0	24	1.2567	0.0	1.2569	1.1211
3.1832	6.5	26	1.3788	0.0315	1.3788	1.1742
3.1832	7.0	28	1.2334	0.0315	1.2335	1.1106
1.8059	7.5	30	1.1130	0.0211	1.1133	1.0551
1.8059	8.0	32	1.4164	0.0506	1.4163	1.1901
1.8059	8.5	34	1.6915	0.1031	1.6912	1.3005
1.8059	9.0	36	1.4600	0.1031	1.4598	1.2082
1.8059	9.5	38	1.0667	0.0604	1.0667	1.0328
1.6593	10.0	40	1.4704	0.0905	1.4700	1.2124
1.6593	10.5	42	2.0622	0.0278	2.0613	1.4357
1.6593	11.0	44	1.1178	0.1293	1.1177	1.0572
1.6593	11.5	46	1.0675	0.1569	1.0675	1.0332
1.6593	12.0	48	1.7319	0.0233	1.7311	1.3157
0.9406	12.5	50	1.1601	0.0989	1.1600	1.0770
0.9406	13.0	52	1.1285	0.1132	1.1284	1.0623
0.9406	13.5	54	1.9055	0.0717	1.9044	1.3800
0.9406	14.0	56	1.5538	0.1221	1.5530	1.2462
0.9406	14.5	58	1.0473	0.1962	1.0473	1.0234
0.4446	15.0	60	1.7170	0.1096	1.7159	1.3099
0.4446	15.5	62	2.2852	0.0988	2.2835	1.5111
0.4446	16.0	64	1.2850	0.1886	1.2845	1.1334
0.4446	16.5	66	1.2828	0.1859	1.2823	1.1324
0.4446	17.0	68	1.9245	0.1151	1.9232	1.3868
0.2695	17.5	70	1.2476	0.2057	1.2470	1.1167
0.2695	18.0	72	1.2669	0.2054	1.2663	1.1253
0.2695	18.5	74	1.8286	0.1431	1.8273	1.3518
0.2695	19.0	76	1.1509	0.2636	1.1504	1.0726
0.2695	19.5	78	1.1317	0.2793	1.1312	1.0636
0.1936	20.0	80	1.5627	0.1653	1.5617	1.2497
0.1936	20.5	82	1.0072	0.3157	1.0069	1.0034
0.1936	21.0	84	1.0543	0.3252	1.0539	1.0266
0.1936	21.5	86	1.8059	0.1342	1.8046	1.3434
0.1936	22.0	88	1.6296	0.1594	1.6284	1.2761
0.1757	22.5	90	1.0916	0.2885	1.0910	1.0445
0.1757	23.0	92	1.4155	0.1841	1.4145	1.1893
0.1757	23.5	94	1.7248	0.1444	1.7235	1.3128
0.1757	24.0	96	1.1246	0.2392	1.1240	1.0602
0.1757	24.5	98	1.1301	0.2557	1.1296	1.0628
0.1435	25.0	100	1.4698	0.1675	1.4688	1.2120
0.1435	25.5	102	1.1794	0.2319	1.1788	1.0857
0.1435	26.0	104	1.2644	0.1958	1.2637	1.1241
0.1435	26.5	106	1.3988	0.1900	1.3978	1.1823
0.1435	27.0	108	1.2669	0.2205	1.2662	1.1252
0.1064	27.5	110	1.3188	0.1915	1.3180	1.1481
0.1064	28.0	112	1.1184	0.2552	1.1180	1.0574
0.1064	28.5	114	1.3648	0.1738	1.3640	1.1679
0.1064	29.0	116	1.2235	0.1877	1.2229	1.1058
0.1064	29.5	118	1.1423	0.2555	1.1418	1.0685
0.1042	30.0	120	1.5102	0.1436	1.5091	1.2285
0.1042	30.5	122	1.3685	0.1879	1.3676	1.1694
0.1042	31.0	124	1.0236	0.2863	1.0233	1.0116
0.1042	31.5	126	1.2725	0.2107	1.2718	1.1278
0.1042	32.0	128	1.5572	0.1830	1.5561	1.2474
0.1128	32.5	130	1.1167	0.2680	1.1162	1.0565
0.1128	33.0	132	1.0258	0.2855	1.0255	1.0127
0.1128	33.5	134	1.4305	0.1770	1.4295	1.1956
0.1128	34.0	136	1.5033	0.1699	1.5023	1.2257
0.1128	34.5	138	1.1300	0.2671	1.1294	1.0627
0.0909	35.0	140	1.0883	0.2727	1.0878	1.0430
0.0909	35.5	142	1.3422	0.2095	1.3413	1.1582
0.0909	36.0	144	1.2493	0.2234	1.2486	1.1174
0.0909	36.5	146	1.2679	0.2323	1.2672	1.1257
0.0909	37.0	148	1.0689	0.2558	1.0685	1.0337
0.0786	37.5	150	1.1103	0.2547	1.1098	1.0534
0.0786	38.0	152	1.1539	0.2544	1.1533	1.0739
0.0786	38.5	154	1.1014	0.2763	1.1009	1.0493
0.0786	39.0	156	1.2973	0.2249	1.2965	1.1387
0.0786	39.5	158	1.3139	0.2355	1.3132	1.1459
0.0805	40.0	160	1.1488	0.2477	1.1483	1.0716
0.0805	40.5	162	1.3403	0.2200	1.3396	1.1574
0.0805	41.0	164	1.1800	0.2344	1.1794	1.0860
0.0805	41.5	166	1.0805	0.2524	1.0802	1.0393
0.0805	42.0	168	1.1986	0.2250	1.1981	1.0946
0.071	42.5	170	1.2703	0.2248	1.2697	1.1268
0.071	43.0	172	1.1719	0.2208	1.1714	1.0823
0.071	43.5	174	1.2463	0.2211	1.2457	1.1161
0.071	44.0	176	1.1079	0.2483	1.1076	1.0524
0.071	44.5	178	1.1958	0.2365	1.1953	1.0933
0.0709	45.0	180	1.0433	0.2659	1.0430	1.0213
0.0709	45.5	182	1.2236	0.2302	1.2230	1.1059
0.0709	46.0	184	1.2420	0.2449	1.2414	1.1142
0.0709	46.5	186	1.1445	0.2697	1.1440	1.0696
0.0709	47.0	188	1.2237	0.2481	1.2232	1.1060
0.0584	47.5	190	1.1379	0.2774	1.1375	1.0665
0.0584	48.0	192	1.1539	0.2779	1.1534	1.0740
0.0584	48.5	194	1.1608	0.2704	1.1603	1.0772
0.0584	49.0	196	0.9639	0.3012	0.9637	0.9817
0.0584	49.5	198	0.9822	0.2936	0.9820	0.9909
0.0664	50.0	200	1.2809	0.2215	1.2802	1.1315
0.0664	50.5	202	1.3618	0.2134	1.3610	1.1666
0.0664	51.0	204	1.0904	0.2619	1.0901	1.0441
0.0664	51.5	206	1.0317	0.2595	1.0314	1.0156
0.0664	52.0	208	1.2551	0.2192	1.2545	1.1200
0.0699	52.5	210	1.3032	0.2108	1.3026	1.1413
0.0699	53.0	212	1.0506	0.2546	1.0503	1.0249
0.0699	53.5	214	0.9993	0.2822	0.9991	0.9995
0.0699	54.0	216	1.1964	0.2474	1.1959	1.0936
0.0699	54.5	218	1.3903	0.2190	1.3895	1.1788
0.0681	55.0	220	1.2136	0.2489	1.2130	1.1014
0.0681	55.5	222	1.0081	0.3051	1.0078	1.0039
0.0681	56.0	224	1.0627	0.2628	1.0624	1.0307
0.0681	56.5	226	1.2546	0.2574	1.2540	1.1198
0.0681	57.0	228	1.1267	0.2554	1.1263	1.0613
0.0659	57.5	230	0.9882	0.2809	0.9880	0.9940
0.0659	58.0	232	1.0832	0.2623	1.0828	1.0406
0.0659	58.5	234	1.2577	0.2263	1.2572	1.1212
0.0659	59.0	236	1.1881	0.2324	1.1877	1.0898
0.0659	59.5	238	0.9637	0.2981	0.9637	0.9817
0.0621	60.0	240	0.9270	0.2871	0.9270	0.9628
0.0621	60.5	242	1.0485	0.2589	1.0483	1.0238
0.0621	61.0	244	1.2958	0.2172	1.2952	1.1381
0.0621	61.5	246	1.2702	0.2374	1.2697	1.1268
0.0621	62.0	248	1.0769	0.2570	1.0766	1.0376
0.0674	62.5	250	1.0694	0.2525	1.0691	1.0340
0.0674	63.0	252	1.1505	0.2311	1.1500	1.0724
0.0674	63.5	254	1.0362	0.2648	1.0359	1.0178
0.0674	64.0	256	1.0223	0.2747	1.0220	1.0110
0.0674	64.5	258	1.1850	0.2549	1.1845	1.0883
0.0505	65.0	260	1.1966	0.2554	1.1961	1.0937
0.0505	65.5	262	1.0224	0.2845	1.0220	1.0110
0.0505	66.0	264	0.9985	0.2950	0.9982	0.9991
0.0505	66.5	266	1.1537	0.2445	1.1532	1.0739
0.0505	67.0	268	1.1707	0.2492	1.1701	1.0817
0.0585	67.5	270	1.0478	0.2661	1.0474	1.0234
0.0585	68.0	272	1.0185	0.2787	1.0182	1.0090
0.0585	68.5	274	1.1008	0.2584	1.1004	1.0490
0.0585	69.0	276	1.1749	0.2511	1.1744	1.0837
0.0585	69.5	278	1.1383	0.2657	1.1379	1.0667
0.0488	70.0	280	1.0424	0.2703	1.0421	1.0208
0.0488	70.5	282	1.0927	0.2507	1.0923	1.0451
0.0488	71.0	284	1.0573	0.2666	1.0569	1.0281
0.0488	71.5	286	1.0507	0.2709	1.0503	1.0248
0.0488	72.0	288	1.1098	0.2729	1.1094	1.0533
0.0539	72.5	290	1.0573	0.2789	1.0569	1.0281
0.0539	73.0	292	1.0879	0.2860	1.0875	1.0428
0.0539	73.5	294	1.2489	0.2517	1.2483	1.1173
0.0539	74.0	296	1.3002	0.2237	1.2995	1.1400
0.0539	74.5	298	1.1797	0.2539	1.1793	1.0859
0.0499	75.0	300	1.0292	0.2642	1.0289	1.0144
0.0499	75.5	302	1.0321	0.2642	1.0319	1.0158
0.0499	76.0	304	1.0854	0.2803	1.0851	1.0417
0.0499	76.5	306	1.0956	0.2804	1.0952	1.0465
0.0499	77.0	308	1.1515	0.2640	1.1511	1.0729
0.0447	77.5	310	1.1040	0.2794	1.1036	1.0505
0.0447	78.0	312	1.0201	0.2719	1.0198	1.0099
0.0447	78.5	314	1.0599	0.2652	1.0596	1.0293
0.0447	79.0	316	1.1569	0.2520	1.1564	1.0754
0.0447	79.5	318	1.1613	0.2508	1.1609	1.0774
0.0476	80.0	320	1.0588	0.2586	1.0585	1.0288
0.0476	80.5	322	0.9929	0.2750	0.9926	0.9963
0.0476	81.0	324	1.0213	0.2658	1.0210	1.0104
0.0476	81.5	326	1.1019	0.2615	1.1015	1.0495
0.0476	82.0	328	1.1252	0.2538	1.1248	1.0606
0.0466	82.5	330	1.0909	0.2603	1.0905	1.0443
0.0466	83.0	332	1.1140	0.2677	1.1136	1.0553
0.0466	83.5	334	1.1539	0.2534	1.1534	1.0740
0.0466	84.0	336	1.1634	0.2497	1.1630	1.0784
0.0466	84.5	338	1.1437	0.2505	1.1433	1.0692
0.0476	85.0	340	1.0883	0.2583	1.0879	1.0430
0.0476	85.5	342	1.1007	0.2654	1.1003	1.0489
0.0476	86.0	344	1.1217	0.2636	1.1213	1.0589
0.0476	86.5	346	1.0700	0.2591	1.0697	1.0342
0.0476	87.0	348	1.0319	0.2748	1.0316	1.0157
0.0439	87.5	350	1.0604	0.2490	1.0600	1.0296
0.0439	88.0	352	1.1403	0.2602	1.1398	1.0676
0.0439	88.5	354	1.1794	0.2509	1.1789	1.0858
0.0439	89.0	356	1.1796	0.2521	1.1791	1.0859
0.0439	89.5	358	1.1215	0.2674	1.1211	1.0588
0.0482	90.0	360	1.0594	0.2627	1.0590	1.0291
0.0482	90.5	362	1.0581	0.2577	1.0577	1.0285
0.0482	91.0	364	1.0718	0.2566	1.0714	1.0351
0.0482	91.5	366	1.1206	0.2614	1.1202	1.0584
0.0482	92.0	368	1.1432	0.2611	1.1427	1.0690
0.0425	92.5	370	1.1300	0.2614	1.1296	1.0628
0.0425	93.0	372	1.0886	0.2626	1.0882	1.0432
0.0425	93.5	374	1.0443	0.2615	1.0440	1.0218
0.0425	94.0	376	1.0373	0.2745	1.0370	1.0183
0.0425	94.5	378	1.0571	0.2661	1.0567	1.0280
0.0443	95.0	380	1.0948	0.2569	1.0944	1.0461
0.0443	95.5	382	1.1465	0.2581	1.1460	1.0705
0.0443	96.0	384	1.1750	0.2533	1.1745	1.0837
0.0443	96.5	386	1.1768	0.2533	1.1763	1.0846
0.0443	97.0	388	1.1592	0.2489	1.1587	1.0764
0.0425	97.5	390	1.1317	0.2559	1.1313	1.0636
0.0425	98.0	392	1.1041	0.2639	1.1037	1.0506
0.0425	98.5	394	1.0893	0.2626	1.0890	1.0435
0.0425	99.0	396	1.0807	0.2613	1.0803	1.0394
0.0425	99.5	398	1.0777	0.2613	1.0773	1.0379
0.0408	100.0	400	1.0766	0.2613	1.0762	1.0374

Framework versions

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