ArabicNewSplits7_usingALLEssays_FineTuningAraBERT_run1_AugV5_k1_task2_organization

This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1502
• Qwk: 0.1345
• Mse: 1.1502
• Rmse: 1.0725

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: 8
• eval_batch_size: 8
• 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	4.6210	0.0010	4.6210	2.1496
No log	1.3333	4	2.6617	0.0104	2.6617	1.6315
No log	2.0	6	2.0269	-0.0370	2.0269	1.4237
No log	2.6667	8	1.4865	-0.0105	1.4865	1.2192
No log	3.3333	10	1.2872	0.1144	1.2872	1.1346
No log	4.0	12	1.2461	0.0682	1.2461	1.1163
No log	4.6667	14	1.2396	0.1397	1.2396	1.1134
No log	5.3333	16	1.2110	0.1237	1.2110	1.1005
No log	6.0	18	1.1717	0.1507	1.1717	1.0825
No log	6.6667	20	1.1461	0.2619	1.1461	1.0705
No log	7.3333	22	1.2260	0.1053	1.2260	1.1072
No log	8.0	24	1.1934	0.2097	1.1934	1.0924
No log	8.6667	26	1.1107	0.2417	1.1107	1.0539
No log	9.3333	28	1.0975	0.3122	1.0975	1.0476
No log	10.0	30	1.2062	0.1952	1.2062	1.0983
No log	10.6667	32	1.2289	0.2224	1.2289	1.1086
No log	11.3333	34	1.1098	0.2743	1.1098	1.0535
No log	12.0	36	1.0546	0.3045	1.0546	1.0269
No log	12.6667	38	1.0733	0.3119	1.0733	1.0360
No log	13.3333	40	1.0415	0.3169	1.0415	1.0205
No log	14.0	42	1.0791	0.2439	1.0791	1.0388
No log	14.6667	44	1.0457	0.2972	1.0457	1.0226
No log	15.3333	46	1.0374	0.3838	1.0374	1.0185
No log	16.0	48	1.0453	0.2772	1.0453	1.0224
No log	16.6667	50	1.3067	0.2506	1.3067	1.1431
No log	17.3333	52	1.4714	0.2592	1.4714	1.2130
No log	18.0	54	1.2602	0.2065	1.2602	1.1226
No log	18.6667	56	1.1739	0.1703	1.1739	1.0834
No log	19.3333	58	1.0857	0.1541	1.0857	1.0420
No log	20.0	60	1.1069	0.1541	1.1069	1.0521
No log	20.6667	62	1.2565	0.1371	1.2565	1.1210
No log	21.3333	64	1.3314	0.1655	1.3314	1.1539
No log	22.0	66	1.1654	0.1801	1.1654	1.0795
No log	22.6667	68	1.0989	0.2056	1.0989	1.0483
No log	23.3333	70	1.0891	0.2056	1.0891	1.0436
No log	24.0	72	1.1516	0.2439	1.1516	1.0731
No log	24.6667	74	1.1333	0.2402	1.1333	1.0646
No log	25.3333	76	1.1744	0.2439	1.1744	1.0837
No log	26.0	78	1.0872	0.2454	1.0872	1.0427
No log	26.6667	80	1.0884	0.2850	1.0884	1.0433
No log	27.3333	82	1.1321	0.2381	1.1321	1.0640
No log	28.0	84	1.0809	0.2782	1.0809	1.0396
No log	28.6667	86	1.0645	0.2752	1.0645	1.0317
No log	29.3333	88	1.1686	0.2275	1.1686	1.0810
No log	30.0	90	1.2475	0.1795	1.2475	1.1169
No log	30.6667	92	1.2015	0.1795	1.2015	1.0961
No log	31.3333	94	1.1453	0.2152	1.1453	1.0702
No log	32.0	96	1.0513	0.2056	1.0513	1.0253
No log	32.6667	98	1.0157	0.3045	1.0157	1.0078
No log	33.3333	100	1.0075	0.4242	1.0075	1.0038
No log	34.0	102	1.0291	0.2357	1.0291	1.0144
No log	34.6667	104	1.1076	0.2195	1.1076	1.0524
No log	35.3333	106	1.1683	0.2046	1.1683	1.0809
No log	36.0	108	1.1329	0.1903	1.1329	1.0644
No log	36.6667	110	1.0765	0.2056	1.0765	1.0375
No log	37.3333	112	1.0263	0.2313	1.0263	1.0130
No log	38.0	114	1.0245	0.3621	1.0245	1.0122
No log	38.6667	116	1.0496	0.2357	1.0496	1.0245
No log	39.3333	118	1.1186	0.1650	1.1186	1.0576
No log	40.0	120	1.1450	0.1345	1.1450	1.0700
No log	40.6667	122	1.1817	0.1188	1.1817	1.0871
No log	41.3333	124	1.1665	0.1188	1.1665	1.0800
No log	42.0	126	1.1058	0.1188	1.1058	1.0515
No log	42.6667	128	1.0989	0.1696	1.0989	1.0483
No log	43.3333	130	1.1066	0.1596	1.1066	1.0519
No log	44.0	132	1.1059	0.1596	1.1059	1.0516
No log	44.6667	134	1.0470	0.2056	1.0470	1.0232
No log	45.3333	136	0.9953	0.3289	0.9953	0.9976
No log	46.0	138	0.9762	0.4141	0.9762	0.9880
No log	46.6667	140	0.9804	0.4282	0.9804	0.9902
No log	47.3333	142	0.9984	0.3666	0.9984	0.9992
No log	48.0	144	1.0422	0.2263	1.0422	1.0209
No log	48.6667	146	1.0789	0.1596	1.0789	1.0387
No log	49.3333	148	1.0934	0.1750	1.0934	1.0457
No log	50.0	150	1.1187	0.1750	1.1187	1.0577
No log	50.6667	152	1.0836	0.2056	1.0836	1.0410
No log	51.3333	154	1.0678	0.2056	1.0678	1.0333
No log	52.0	156	1.0909	0.2056	1.0909	1.0445
No log	52.6667	158	1.1652	0.1500	1.1652	1.0794
No log	53.3333	160	1.2063	0.1283	1.2063	1.0983
No log	54.0	162	1.2202	0.1283	1.2202	1.1046
No log	54.6667	164	1.2427	0.1283	1.2427	1.1147
No log	55.3333	166	1.2305	0.1345	1.2305	1.1093
No log	56.0	168	1.1970	0.1345	1.1970	1.0941
No log	56.6667	170	1.1175	0.2009	1.1175	1.0571
No log	57.3333	172	1.0951	0.1701	1.0951	1.0465
No log	58.0	174	1.0918	0.1701	1.0918	1.0449
No log	58.6667	176	1.0843	0.1602	1.0843	1.0413
No log	59.3333	178	1.0849	0.1279	1.0849	1.0416
No log	60.0	180	1.1077	0.1114	1.1077	1.0525
No log	60.6667	182	1.1133	0.1214	1.1133	1.0551
No log	61.3333	184	1.1151	0.1379	1.1151	1.0560
No log	62.0	186	1.1098	0.1279	1.1098	1.0535
No log	62.6667	188	1.0991	0.1911	1.0991	1.0484
No log	63.3333	190	1.0851	0.2065	1.0851	1.0417
No log	64.0	192	1.0839	0.1761	1.0839	1.0411
No log	64.6667	194	1.0940	0.1911	1.0940	1.0459
No log	65.3333	196	1.1271	0.1701	1.1271	1.0616
No log	66.0	198	1.1431	0.1316	1.1431	1.0692
No log	66.6667	200	1.1309	0.1541	1.1309	1.0634
No log	67.3333	202	1.1261	0.2009	1.1261	1.0612
No log	68.0	204	1.1109	0.2009	1.1109	1.0540
No log	68.6667	206	1.0888	0.1911	1.0888	1.0435
No log	69.3333	208	1.0831	0.1911	1.0831	1.0407
No log	70.0	210	1.0849	0.1911	1.0849	1.0416
No log	70.6667	212	1.1028	0.1911	1.1028	1.0502
No log	71.3333	214	1.1120	0.1853	1.1120	1.0545
No log	72.0	216	1.1120	0.1903	1.1120	1.0545
No log	72.6667	218	1.1304	0.1750	1.1304	1.0632
No log	73.3333	220	1.1234	0.1750	1.1234	1.0599
No log	74.0	222	1.1223	0.1750	1.1223	1.0594
No log	74.6667	224	1.1334	0.1345	1.1334	1.0646
No log	75.3333	226	1.1311	0.1345	1.1311	1.0635
No log	76.0	228	1.1145	0.1345	1.1145	1.0557
No log	76.6667	230	1.0957	0.1441	1.0957	1.0467
No log	77.3333	232	1.0822	0.1755	1.0822	1.0403
No log	78.0	234	1.0889	0.1441	1.0889	1.0435
No log	78.6667	236	1.0989	0.1441	1.0989	1.0483
No log	79.3333	238	1.1106	0.1500	1.1106	1.0538
No log	80.0	240	1.1255	0.1500	1.1255	1.0609
No log	80.6667	242	1.1267	0.1500	1.1267	1.0615
No log	81.3333	244	1.1207	0.1500	1.1207	1.0587
No log	82.0	246	1.1165	0.1500	1.1165	1.0567
No log	82.6667	248	1.1330	0.1345	1.1330	1.0644
No log	83.3333	250	1.1478	0.1345	1.1478	1.0714
No log	84.0	252	1.1658	0.1345	1.1658	1.0797
No log	84.6667	254	1.1828	0.1345	1.1828	1.0875
No log	85.3333	256	1.1877	0.1345	1.1877	1.0898
No log	86.0	258	1.1937	0.1345	1.1937	1.0925
No log	86.6667	260	1.2015	0.1345	1.2015	1.0961
No log	87.3333	262	1.2084	0.1345	1.2084	1.0993
No log	88.0	264	1.1966	0.1345	1.1966	1.0939
No log	88.6667	266	1.1755	0.1345	1.1755	1.0842
No log	89.3333	268	1.1477	0.1345	1.1477	1.0713
No log	90.0	270	1.1275	0.1345	1.1275	1.0619
No log	90.6667	272	1.1154	0.1500	1.1154	1.0561
No log	91.3333	274	1.1098	0.1500	1.1098	1.0535
No log	92.0	276	1.1087	0.1500	1.1087	1.0529
No log	92.6667	278	1.1138	0.1500	1.1138	1.0554
No log	93.3333	280	1.1192	0.1500	1.1192	1.0579
No log	94.0	282	1.1246	0.1345	1.1246	1.0605
No log	94.6667	284	1.1298	0.1345	1.1298	1.0629
No log	95.3333	286	1.1375	0.1345	1.1375	1.0665
No log	96.0	288	1.1443	0.1345	1.1443	1.0697
No log	96.6667	290	1.1461	0.1345	1.1461	1.0706
No log	97.3333	292	1.1473	0.1345	1.1473	1.0711
No log	98.0	294	1.1483	0.1345	1.1483	1.0716
No log	98.6667	296	1.1493	0.1345	1.1493	1.0720
No log	99.3333	298	1.1501	0.1345	1.1501	1.0724
No log	100.0	300	1.1502	0.1345	1.1502	1.0725

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu118
• Datasets 2.21.0
• Tokenizers 0.19.1