ArabicNewSplits6_FineTuningAraBERT_run3_AugV5_k7_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: 0.9215
• Qwk: 0.6137
• Mse: 0.9215
• Rmse: 0.9599

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	0.0741	2	2.2860	0.0408	2.2860	1.5120
No log	0.1481	4	1.5825	0.1391	1.5825	1.2580
No log	0.2222	6	1.7223	-0.0403	1.7223	1.3124
No log	0.2963	8	1.8033	0.0334	1.8033	1.3429
No log	0.3704	10	1.9061	0.1454	1.9061	1.3806
No log	0.4444	12	1.9023	0.1593	1.9023	1.3792
No log	0.5185	14	1.8483	0.1643	1.8483	1.3595
No log	0.5926	16	1.6176	0.2288	1.6176	1.2718
No log	0.6667	18	1.4752	0.2009	1.4752	1.2146
No log	0.7407	20	1.3655	0.1283	1.3655	1.1685
No log	0.8148	22	1.3141	0.1399	1.3141	1.1463
No log	0.8889	24	1.2844	0.1494	1.2844	1.1333
No log	0.9630	26	1.3418	0.2735	1.3418	1.1584
No log	1.0370	28	1.5301	0.3369	1.5301	1.2370
No log	1.1111	30	1.5682	0.3498	1.5682	1.2523
No log	1.1852	32	1.2881	0.3810	1.2881	1.1349
No log	1.2593	34	1.1053	0.3554	1.1053	1.0513
No log	1.3333	36	1.1534	0.3502	1.1534	1.0739
No log	1.4074	38	1.1436	0.3712	1.1436	1.0694
No log	1.4815	40	1.0547	0.4412	1.0547	1.0270
No log	1.5556	42	1.0202	0.4345	1.0202	1.0101
No log	1.6296	44	1.2093	0.4344	1.2093	1.0997
No log	1.7037	46	1.2790	0.4198	1.2790	1.1309
No log	1.7778	48	1.2849	0.4054	1.2849	1.1335
No log	1.8519	50	1.1941	0.4599	1.1941	1.0928
No log	1.9259	52	1.1332	0.4814	1.1332	1.0645
No log	2.0	54	1.0491	0.4844	1.0491	1.0243
No log	2.0741	56	0.9911	0.4545	0.9911	0.9955
No log	2.1481	58	0.9876	0.4394	0.9876	0.9938
No log	2.2222	60	1.0161	0.3798	1.0161	1.0080
No log	2.2963	62	1.0720	0.3588	1.0720	1.0354
No log	2.3704	64	1.1445	0.3470	1.1445	1.0698
No log	2.4444	66	1.2917	0.4324	1.2917	1.1365
No log	2.5185	68	1.4677	0.4471	1.4677	1.2115
No log	2.5926	70	1.4994	0.4429	1.4994	1.2245
No log	2.6667	72	1.2732	0.4454	1.2732	1.1284
No log	2.7407	74	1.0643	0.4883	1.0643	1.0316
No log	2.8148	76	0.9537	0.5190	0.9537	0.9766
No log	2.8889	78	0.9164	0.5703	0.9164	0.9573
No log	2.9630	80	0.8993	0.5602	0.8993	0.9483
No log	3.0370	82	0.9113	0.5764	0.9113	0.9546
No log	3.1111	84	0.9075	0.5699	0.9075	0.9526
No log	3.1852	86	1.0076	0.5777	1.0076	1.0038
No log	3.2593	88	0.9679	0.5765	0.9679	0.9838
No log	3.3333	90	0.8248	0.6324	0.8248	0.9082
No log	3.4074	92	0.7955	0.6534	0.7955	0.8919
No log	3.4815	94	0.8266	0.6036	0.8266	0.9092
No log	3.5556	96	1.1253	0.5884	1.1253	1.0608
No log	3.6296	98	1.3398	0.5055	1.3398	1.1575
No log	3.7037	100	1.1891	0.5495	1.1891	1.0905
No log	3.7778	102	0.9191	0.5706	0.9191	0.9587
No log	3.8519	104	0.8279	0.6170	0.8279	0.9099
No log	3.9259	106	0.8274	0.6409	0.8274	0.9096
No log	4.0	108	0.9206	0.5599	0.9206	0.9595
No log	4.0741	110	1.1232	0.5757	1.1232	1.0598
No log	4.1481	112	1.1841	0.5800	1.1841	1.0881
No log	4.2222	114	1.1975	0.5926	1.1975	1.0943
No log	4.2963	116	0.9478	0.6184	0.9478	0.9735
No log	4.3704	118	0.7845	0.6870	0.7845	0.8857
No log	4.4444	120	0.7819	0.6639	0.7819	0.8842
No log	4.5185	122	0.8656	0.6341	0.8656	0.9304
No log	4.5926	124	0.9186	0.6268	0.9186	0.9584
No log	4.6667	126	0.9460	0.6015	0.9460	0.9726
No log	4.7407	128	0.9988	0.5926	0.9988	0.9994
No log	4.8148	130	0.8977	0.6223	0.8977	0.9475
No log	4.8889	132	0.8939	0.6223	0.8939	0.9455
No log	4.9630	134	1.0103	0.6062	1.0103	1.0052
No log	5.0370	136	0.9988	0.6133	0.9988	0.9994
No log	5.1111	138	0.8849	0.6224	0.8849	0.9407
No log	5.1852	140	0.8209	0.6675	0.8209	0.9060
No log	5.2593	142	0.7660	0.6758	0.7660	0.8752
No log	5.3333	144	0.7601	0.6586	0.7601	0.8718
No log	5.4074	146	0.7831	0.6645	0.7831	0.8849
No log	5.4815	148	0.8804	0.6023	0.8804	0.9383
No log	5.5556	150	0.8934	0.6059	0.8934	0.9452
No log	5.6296	152	0.9497	0.6111	0.9497	0.9745
No log	5.7037	154	1.0443	0.6010	1.0443	1.0219
No log	5.7778	156	1.0121	0.5992	1.0121	1.0060
No log	5.8519	158	0.9767	0.5985	0.9767	0.9883
No log	5.9259	160	0.9815	0.5893	0.9815	0.9907
No log	6.0	162	1.0092	0.5677	1.0092	1.0046
No log	6.0741	164	1.0736	0.5485	1.0736	1.0362
No log	6.1481	166	1.1106	0.5348	1.1106	1.0539
No log	6.2222	168	1.1308	0.5348	1.1308	1.0634
No log	6.2963	170	1.0863	0.5360	1.0863	1.0423
No log	6.3704	172	0.9321	0.6331	0.9321	0.9655
No log	6.4444	174	0.8271	0.6650	0.8271	0.9095
No log	6.5185	176	0.7872	0.6599	0.7872	0.8873
No log	6.5926	178	0.8101	0.6622	0.8101	0.9001
No log	6.6667	180	0.8929	0.6348	0.8929	0.9450
No log	6.7407	182	0.9249	0.6500	0.9249	0.9617
No log	6.8148	184	0.8889	0.6293	0.8889	0.9428
No log	6.8889	186	0.8528	0.6296	0.8528	0.9235
No log	6.9630	188	0.7937	0.6747	0.7937	0.8909
No log	7.0370	190	0.7450	0.6876	0.7450	0.8631
No log	7.1111	192	0.7435	0.6876	0.7435	0.8623
No log	7.1852	194	0.7808	0.6710	0.7808	0.8836
No log	7.2593	196	0.8944	0.6226	0.8944	0.9457
No log	7.3333	198	1.0666	0.6133	1.0666	1.0328
No log	7.4074	200	1.1136	0.6063	1.1136	1.0553
No log	7.4815	202	1.0428	0.6294	1.0428	1.0212
No log	7.5556	204	0.9220	0.6233	0.9220	0.9602
No log	7.6296	206	0.8846	0.5992	0.8846	0.9405
No log	7.7037	208	0.9143	0.6089	0.9143	0.9562
No log	7.7778	210	0.9573	0.6294	0.9573	0.9784
No log	7.8519	212	0.9834	0.6294	0.9834	0.9917
No log	7.9259	214	0.9689	0.6294	0.9689	0.9843
No log	8.0	216	0.9155	0.6232	0.9155	0.9568
No log	8.0741	218	0.8725	0.6567	0.8725	0.9341
No log	8.1481	220	0.8583	0.6497	0.8583	0.9265
No log	8.2222	222	0.8402	0.6419	0.8402	0.9166
No log	8.2963	224	0.8684	0.6532	0.8684	0.9319
No log	8.3704	226	0.9242	0.6232	0.9242	0.9613
No log	8.4444	228	0.9782	0.6233	0.9782	0.9890
No log	8.5185	230	1.0170	0.6294	1.0170	1.0085
No log	8.5926	232	1.0073	0.6233	1.0073	1.0036
No log	8.6667	234	0.9784	0.6303	0.9784	0.9891
No log	8.7407	236	0.9652	0.6317	0.9652	0.9824
No log	8.8148	238	0.9551	0.6317	0.9551	0.9773
No log	8.8889	240	0.9469	0.6317	0.9469	0.9731
No log	8.9630	242	0.9491	0.6246	0.9491	0.9742
No log	9.0370	244	0.9211	0.6304	0.9211	0.9597
No log	9.1111	246	0.8823	0.6392	0.8823	0.9393
No log	9.1852	248	0.8460	0.6580	0.8460	0.9198
No log	9.2593	250	0.8251	0.6677	0.8251	0.9084
No log	9.3333	252	0.8187	0.6533	0.8187	0.9048
No log	9.4074	254	0.8262	0.6677	0.8262	0.9090
No log	9.4815	256	0.8429	0.6588	0.8429	0.9181
No log	9.5556	258	0.8607	0.6284	0.8607	0.9277
No log	9.6296	260	0.8802	0.6319	0.8802	0.9382
No log	9.7037	262	0.8957	0.6151	0.8957	0.9464
No log	9.7778	264	0.9099	0.6151	0.9099	0.9539
No log	9.8519	266	0.9161	0.6137	0.9161	0.9571
No log	9.9259	268	0.9207	0.6137	0.9207	0.9595
No log	10.0	270	0.9215	0.6137	0.9215	0.9599

Framework versions

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