ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run1_AugV5_k4_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: 0.9132
• Qwk: 0.5272
• Mse: 0.9132
• Rmse: 0.9556

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	0.0870	2	4.0249	0.0011	4.0249	2.0062
No log	0.1739	4	2.1314	0.0539	2.1314	1.4599
No log	0.2609	6	1.2858	0.0959	1.2858	1.1339
No log	0.3478	8	0.9297	-0.0028	0.9297	0.9642
No log	0.4348	10	0.8561	-0.0270	0.8561	0.9253
No log	0.5217	12	0.7435	0.1809	0.7435	0.8623
No log	0.6087	14	0.7228	0.1366	0.7228	0.8502
No log	0.6957	16	0.8575	0.0973	0.8575	0.9260
No log	0.7826	18	0.9567	0.0912	0.9567	0.9781
No log	0.8696	20	0.9817	0.0764	0.9817	0.9908
No log	0.9565	22	0.9408	0.1383	0.9408	0.9699
No log	1.0435	24	0.7640	0.1826	0.7640	0.8741
No log	1.1304	26	0.6670	0.3124	0.6670	0.8167
No log	1.2174	28	0.6887	0.1876	0.6887	0.8299
No log	1.3043	30	0.6264	0.2868	0.6264	0.7914
No log	1.3913	32	0.5884	0.2666	0.5884	0.7670
No log	1.4783	34	0.5910	0.2747	0.5910	0.7688
No log	1.5652	36	0.5900	0.3053	0.5900	0.7681
No log	1.6522	38	0.6390	0.2765	0.6390	0.7994
No log	1.7391	40	0.6867	0.2347	0.6867	0.8286
No log	1.8261	42	0.6562	0.4107	0.6562	0.8101
No log	1.9130	44	0.6949	0.3701	0.6949	0.8336
No log	2.0	46	0.7216	0.3908	0.7216	0.8494
No log	2.0870	48	0.8551	0.3218	0.8551	0.9247
No log	2.1739	50	0.8023	0.4106	0.8023	0.8957
No log	2.2609	52	0.7943	0.4206	0.7943	0.8912
No log	2.3478	54	0.7176	0.5178	0.7176	0.8471
No log	2.4348	56	0.6866	0.5182	0.6866	0.8286
No log	2.5217	58	0.6785	0.5241	0.6785	0.8237
No log	2.6087	60	0.7559	0.4402	0.7559	0.8694
No log	2.6957	62	0.9195	0.2328	0.9195	0.9589
No log	2.7826	64	1.1198	0.1318	1.1198	1.0582
No log	2.8696	66	1.1498	0.1336	1.1498	1.0723
No log	2.9565	68	1.0497	0.2246	1.0497	1.0245
No log	3.0435	70	0.7013	0.5517	0.7013	0.8374
No log	3.1304	72	0.8719	0.4910	0.8719	0.9337
No log	3.2174	74	0.8425	0.4949	0.8425	0.9179
No log	3.3043	76	0.6883	0.4896	0.6883	0.8297
No log	3.3913	78	1.1803	0.2971	1.1803	1.0864
No log	3.4783	80	1.3735	0.2119	1.3735	1.1719
No log	3.5652	82	1.3576	0.2418	1.3576	1.1652
No log	3.6522	84	1.1391	0.2875	1.1391	1.0673
No log	3.7391	86	0.8036	0.4837	0.8036	0.8964
No log	3.8261	88	0.6899	0.4789	0.6899	0.8306
No log	3.9130	90	0.7187	0.4785	0.7187	0.8478
No log	4.0	92	0.8270	0.4886	0.8270	0.9094
No log	4.0870	94	0.8409	0.4831	0.8409	0.9170
No log	4.1739	96	0.8429	0.4284	0.8429	0.9181
No log	4.2609	98	0.7776	0.5719	0.7776	0.8818
No log	4.3478	100	0.7662	0.5522	0.7662	0.8753
No log	4.4348	102	0.8050	0.5214	0.8050	0.8972
No log	4.5217	104	0.9660	0.4510	0.9660	0.9829
No log	4.6087	106	1.2127	0.3974	1.2127	1.1012
No log	4.6957	108	1.1769	0.4032	1.1769	1.0849
No log	4.7826	110	1.0627	0.4389	1.0627	1.0309
No log	4.8696	112	0.8874	0.5005	0.8874	0.9420
No log	4.9565	114	0.7420	0.5588	0.7420	0.8614
No log	5.0435	116	0.8203	0.5105	0.8203	0.9057
No log	5.1304	118	0.9399	0.4757	0.9399	0.9695
No log	5.2174	120	0.7851	0.4826	0.7851	0.8860
No log	5.3043	122	0.8060	0.5519	0.8060	0.8978
No log	5.3913	124	1.1158	0.4464	1.1158	1.0563
No log	5.4783	126	1.2151	0.3744	1.2151	1.1023
No log	5.5652	128	1.1677	0.4228	1.1677	1.0806
No log	5.6522	130	1.0434	0.4545	1.0434	1.0215
No log	5.7391	132	1.0052	0.4550	1.0052	1.0026
No log	5.8261	134	0.9755	0.4745	0.9755	0.9877
No log	5.9130	136	1.0359	0.4615	1.0359	1.0178
No log	6.0	138	1.0144	0.4741	1.0144	1.0072
No log	6.0870	140	0.9715	0.4885	0.9715	0.9857
No log	6.1739	142	1.0092	0.4813	1.0092	1.0046
No log	6.2609	144	1.0349	0.4918	1.0349	1.0173
No log	6.3478	146	1.0368	0.4901	1.0368	1.0182
No log	6.4348	148	0.9913	0.5187	0.9913	0.9956
No log	6.5217	150	1.0017	0.5175	1.0017	1.0009
No log	6.6087	152	0.9489	0.5352	0.9489	0.9741
No log	6.6957	154	0.8977	0.5590	0.8977	0.9475
No log	6.7826	156	0.9429	0.5306	0.9429	0.9710
No log	6.8696	158	0.9741	0.5277	0.9741	0.9869
No log	6.9565	160	0.9779	0.5168	0.9779	0.9889
No log	7.0435	162	0.9379	0.5363	0.9379	0.9684
No log	7.1304	164	0.9244	0.5167	0.9244	0.9614
No log	7.2174	166	0.8670	0.5470	0.8670	0.9312
No log	7.3043	168	0.8228	0.5786	0.8228	0.9071
No log	7.3913	170	0.8078	0.5711	0.8078	0.8988
No log	7.4783	172	0.7909	0.5615	0.7909	0.8894
No log	7.5652	174	0.7811	0.5805	0.7811	0.8838
No log	7.6522	176	0.7984	0.5643	0.7984	0.8935
No log	7.7391	178	0.8047	0.5644	0.8047	0.8971
No log	7.8261	180	0.8156	0.5583	0.8156	0.9031
No log	7.9130	182	0.8121	0.5644	0.8121	0.9012
No log	8.0	184	0.8324	0.5443	0.8324	0.9124
No log	8.0870	186	0.8801	0.5258	0.8801	0.9381
No log	8.1739	188	0.9082	0.5245	0.9082	0.9530
No log	8.2609	190	0.8938	0.5358	0.8938	0.9454
No log	8.3478	192	0.9161	0.5272	0.9161	0.9571
No log	8.4348	194	0.9348	0.5202	0.9348	0.9668
No log	8.5217	196	0.9243	0.5162	0.9243	0.9614
No log	8.6087	198	0.8866	0.5250	0.8866	0.9416
No log	8.6957	200	0.8596	0.5508	0.8596	0.9271
No log	8.7826	202	0.8438	0.5571	0.8438	0.9186
No log	8.8696	204	0.8488	0.5509	0.8488	0.9213
No log	8.9565	206	0.8628	0.5437	0.8628	0.9289
No log	9.0435	208	0.8731	0.5376	0.8731	0.9344
No log	9.1304	210	0.8829	0.5376	0.8829	0.9397
No log	9.2174	212	0.8802	0.5274	0.8802	0.9382
No log	9.3043	214	0.8795	0.5274	0.8795	0.9378
No log	9.3913	216	0.8871	0.5258	0.8871	0.9419
No log	9.4783	218	0.8894	0.5258	0.8894	0.9431
No log	9.5652	220	0.8995	0.5404	0.8995	0.9484
No log	9.6522	222	0.9081	0.5232	0.9081	0.9529
No log	9.7391	224	0.9100	0.5232	0.9100	0.9540
No log	9.8261	226	0.9109	0.5272	0.9109	0.9544
No log	9.9130	228	0.9118	0.5272	0.9118	0.9549
No log	10.0	230	0.9132	0.5272	0.9132	0.9556

Framework versions

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