ArabicNewSplits6_FineTuningAraBERT_run1_AugV5_k5_task5_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: 1.1132
• Qwk: 0.6103
• Mse: 1.1132
• Rmse: 1.0551

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.1	2	2.3598	0.0110	2.3598	1.5362
No log	0.2	4	1.6906	0.0702	1.6906	1.3002
No log	0.3	6	1.6714	0.0611	1.6714	1.2928
No log	0.4	8	1.4616	0.1443	1.4616	1.2090
No log	0.5	10	1.3717	0.1903	1.3717	1.1712
No log	0.6	12	1.4341	0.2395	1.4341	1.1975
No log	0.7	14	1.4639	0.3671	1.4639	1.2099
No log	0.8	16	1.5606	0.3765	1.5606	1.2492
No log	0.9	18	1.5658	0.3532	1.5658	1.2513
No log	1.0	20	1.4245	0.3449	1.4245	1.1935
No log	1.1	22	1.3157	0.2604	1.3157	1.1471
No log	1.2	24	1.2495	0.2221	1.2495	1.1178
No log	1.3	26	1.2326	0.2251	1.2326	1.1102
No log	1.4	28	1.2385	0.2762	1.2385	1.1129
No log	1.5	30	1.3241	0.3784	1.3241	1.1507
No log	1.6	32	1.4244	0.3962	1.4244	1.1935
No log	1.7	34	1.3868	0.4017	1.3868	1.1776
No log	1.8	36	1.3091	0.4257	1.3091	1.1442
No log	1.9	38	1.1437	0.4229	1.1437	1.0695
No log	2.0	40	1.0413	0.4461	1.0413	1.0204
No log	2.1	42	1.0249	0.4059	1.0249	1.0124
No log	2.2	44	1.0624	0.3664	1.0624	1.0307
No log	2.3	46	1.1439	0.4247	1.1439	1.0695
No log	2.4	48	1.2789	0.4929	1.2789	1.1309
No log	2.5	50	1.2888	0.5045	1.2888	1.1352
No log	2.6	52	1.0747	0.5031	1.0747	1.0367
No log	2.7	54	1.1192	0.4653	1.1192	1.0579
No log	2.8	56	1.0433	0.4800	1.0433	1.0214
No log	2.9	58	1.0013	0.5441	1.0013	1.0006
No log	3.0	60	0.9986	0.5150	0.9986	0.9993
No log	3.1	62	1.0451	0.4946	1.0451	1.0223
No log	3.2	64	1.1036	0.4564	1.1036	1.0505
No log	3.3	66	1.1586	0.4189	1.1586	1.0764
No log	3.4	68	1.2310	0.4461	1.2310	1.1095
No log	3.5	70	1.2883	0.4643	1.2883	1.1350
No log	3.6	72	1.2793	0.4540	1.2793	1.1310
No log	3.7	74	1.3383	0.4691	1.3383	1.1569
No log	3.8	76	1.2943	0.4956	1.2943	1.1377
No log	3.9	78	1.2474	0.5078	1.2474	1.1168
No log	4.0	80	1.3682	0.4722	1.3682	1.1697
No log	4.1	82	1.4803	0.4660	1.4803	1.2167
No log	4.2	84	1.5564	0.4530	1.5564	1.2476
No log	4.3	86	1.5295	0.4764	1.5295	1.2367
No log	4.4	88	1.6599	0.4249	1.6599	1.2884
No log	4.5	90	1.6837	0.4356	1.6837	1.2976
No log	4.6	92	1.5190	0.4454	1.5190	1.2325
No log	4.7	94	1.3830	0.4634	1.3830	1.1760
No log	4.8	96	1.2530	0.4636	1.2530	1.1194
No log	4.9	98	1.2295	0.4997	1.2295	1.1088
No log	5.0	100	1.2807	0.5113	1.2807	1.1317
No log	5.1	102	1.3723	0.5154	1.3723	1.1714
No log	5.2	104	1.2977	0.5215	1.2977	1.1392
No log	5.3	106	1.2739	0.5222	1.2739	1.1287
No log	5.4	108	1.3221	0.5123	1.3221	1.1498
No log	5.5	110	1.4438	0.5119	1.4438	1.2016
No log	5.6	112	1.5354	0.4941	1.5354	1.2391
No log	5.7	114	1.5808	0.4722	1.5808	1.2573
No log	5.8	116	1.5144	0.5094	1.5144	1.2306
No log	5.9	118	1.3164	0.5327	1.3164	1.1474
No log	6.0	120	1.1617	0.5645	1.1617	1.0778
No log	6.1	122	1.1430	0.5487	1.1430	1.0691
No log	6.2	124	1.2175	0.5567	1.2175	1.1034
No log	6.3	126	1.2619	0.5435	1.2619	1.1233
No log	6.4	128	1.3216	0.5379	1.3216	1.1496
No log	6.5	130	1.4251	0.5319	1.4251	1.1938
No log	6.6	132	1.4921	0.5344	1.4921	1.2215
No log	6.7	134	1.4343	0.5475	1.4343	1.1976
No log	6.8	136	1.3034	0.5428	1.3034	1.1417
No log	6.9	138	1.2234	0.5606	1.2234	1.1061
No log	7.0	140	1.1134	0.5925	1.1134	1.0552
No log	7.1	142	1.0821	0.5789	1.0821	1.0402
No log	7.2	144	1.1244	0.6022	1.1244	1.0604
No log	7.3	146	1.1834	0.5736	1.1834	1.0878
No log	7.4	148	1.2505	0.5485	1.2505	1.1182
No log	7.5	150	1.3199	0.5117	1.3199	1.1489
No log	7.6	152	1.2863	0.5117	1.2863	1.1341
No log	7.7	154	1.1891	0.5779	1.1891	1.0904
No log	7.8	156	1.1504	0.5954	1.1504	1.0726
No log	7.9	158	1.1592	0.5954	1.1592	1.0767
No log	8.0	160	1.1437	0.5954	1.1437	1.0694
No log	8.1	162	1.1512	0.5916	1.1512	1.0729
No log	8.2	164	1.1928	0.5874	1.1928	1.0922
No log	8.3	166	1.2299	0.5445	1.2299	1.1090
No log	8.4	168	1.2543	0.5396	1.2543	1.1200
No log	8.5	170	1.2343	0.5469	1.2343	1.1110
No log	8.6	172	1.1940	0.5831	1.1940	1.0927
No log	8.7	174	1.1825	0.5842	1.1825	1.0874
No log	8.8	176	1.2019	0.5831	1.2019	1.0963
No log	8.9	178	1.2164	0.5685	1.2164	1.1029
No log	9.0	180	1.2268	0.5554	1.2268	1.1076
No log	9.1	182	1.2182	0.5475	1.2182	1.1037
No log	9.2	184	1.1909	0.5483	1.1909	1.0913
No log	9.3	186	1.1481	0.6061	1.1481	1.0715
No log	9.4	188	1.1143	0.6103	1.1143	1.0556
No log	9.5	190	1.0979	0.6116	1.0979	1.0478
No log	9.6	192	1.0961	0.6116	1.0961	1.0469
No log	9.7	194	1.1056	0.6116	1.1056	1.0515
No log	9.8	196	1.1134	0.6103	1.1134	1.0552
No log	9.9	198	1.1141	0.6103	1.1141	1.0555
No log	10.0	200	1.1132	0.6103	1.1132	1.0551

Framework versions

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