ArabicNewSplits6_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: 0.8322
• Qwk: 0.5114
• Mse: 0.8322
• Rmse: 0.9123

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	4.0775	-0.0169	4.0775	2.0193
No log	0.1739	4	2.2392	-0.0087	2.2392	1.4964
No log	0.2609	6	1.7270	-0.0847	1.7270	1.3141
No log	0.3478	8	1.1935	0.0220	1.1935	1.0925
No log	0.4348	10	0.8457	0.0386	0.8457	0.9196
No log	0.5217	12	0.8166	0.0248	0.8166	0.9037
No log	0.6087	14	0.7296	0.1107	0.7296	0.8541
No log	0.6957	16	0.6852	0.1824	0.6852	0.8278
No log	0.7826	18	0.7200	0.1448	0.7200	0.8485
No log	0.8696	20	0.8400	0.0936	0.8400	0.9165
No log	0.9565	22	0.8663	0.0638	0.8663	0.9307
No log	1.0435	24	0.8341	0.1076	0.8341	0.9133
No log	1.1304	26	0.7658	0.2102	0.7658	0.8751
No log	1.2174	28	0.7560	0.1869	0.7560	0.8695
No log	1.3043	30	0.7793	0.1869	0.7793	0.8828
No log	1.3913	32	0.8438	0.2218	0.8438	0.9186
No log	1.4783	34	0.7536	0.2898	0.7536	0.8681
No log	1.5652	36	0.8207	0.2523	0.8207	0.9059
No log	1.6522	38	1.1425	0.1476	1.1425	1.0689
No log	1.7391	40	1.6187	0.1818	1.6187	1.2723
No log	1.8261	42	1.3389	0.2147	1.3389	1.1571
No log	1.9130	44	1.0653	0.2310	1.0653	1.0321
No log	2.0	46	1.0315	0.2494	1.0315	1.0156
No log	2.0870	48	1.1797	0.2661	1.1797	1.0862
No log	2.1739	50	1.1123	0.2892	1.1123	1.0547
No log	2.2609	52	1.0676	0.2934	1.0676	1.0332
No log	2.3478	54	0.9172	0.3807	0.9172	0.9577
No log	2.4348	56	0.7312	0.4804	0.7312	0.8551
No log	2.5217	58	0.6984	0.4916	0.6984	0.8357
No log	2.6087	60	0.6410	0.5413	0.6410	0.8006
No log	2.6957	62	0.6030	0.4545	0.6030	0.7766
No log	2.7826	64	0.6160	0.4533	0.6160	0.7848
No log	2.8696	66	0.7119	0.4272	0.7119	0.8438
No log	2.9565	68	0.7127	0.4272	0.7127	0.8442
No log	3.0435	70	0.6121	0.4319	0.6121	0.7823
No log	3.1304	72	0.5738	0.4538	0.5738	0.7575
No log	3.2174	74	0.5792	0.4909	0.5792	0.7611
No log	3.3043	76	0.6109	0.5128	0.6109	0.7816
No log	3.3913	78	0.6238	0.5456	0.6238	0.7898
No log	3.4783	80	0.6617	0.5672	0.6617	0.8134
No log	3.5652	82	0.6850	0.5476	0.6850	0.8276
No log	3.6522	84	0.7079	0.5663	0.7079	0.8414
No log	3.7391	86	0.7441	0.5662	0.7441	0.8626
No log	3.8261	88	0.7521	0.5294	0.7521	0.8672
No log	3.9130	90	0.7590	0.4710	0.7590	0.8712
No log	4.0	92	0.7365	0.5294	0.7365	0.8582
No log	4.0870	94	0.7242	0.5478	0.7242	0.8510
No log	4.1739	96	0.7499	0.5760	0.7499	0.8659
No log	4.2609	98	0.7452	0.5763	0.7452	0.8633
No log	4.3478	100	0.7145	0.5356	0.7145	0.8453
No log	4.4348	102	0.7472	0.4294	0.7472	0.8644
No log	4.5217	104	0.7922	0.3968	0.7922	0.8900
No log	4.6087	106	0.7673	0.3958	0.7673	0.8760
No log	4.6957	108	0.7369	0.4831	0.7369	0.8584
No log	4.7826	110	0.7603	0.5748	0.7603	0.8719
No log	4.8696	112	0.7912	0.5415	0.7912	0.8895
No log	4.9565	114	0.7940	0.5385	0.7940	0.8911
No log	5.0435	116	0.8002	0.4548	0.8002	0.8945
No log	5.1304	118	0.8612	0.4202	0.8612	0.9280
No log	5.2174	120	0.8737	0.4235	0.8737	0.9347
No log	5.3043	122	0.7996	0.4272	0.7996	0.8942
No log	5.3913	124	0.7523	0.4845	0.7523	0.8673
No log	5.4783	126	0.7666	0.5333	0.7666	0.8755
No log	5.5652	128	0.7873	0.5696	0.7873	0.8873
No log	5.6522	130	0.8185	0.5624	0.8185	0.9047
No log	5.7391	132	0.8080	0.5178	0.8080	0.8989
No log	5.8261	134	0.8222	0.5245	0.8222	0.9068
No log	5.9130	136	0.8485	0.5117	0.8485	0.9212
No log	6.0	138	0.8673	0.5254	0.8673	0.9313
No log	6.0870	140	0.8830	0.5337	0.8830	0.9397
No log	6.1739	142	0.8907	0.5460	0.8907	0.9438
No log	6.2609	144	0.8890	0.5566	0.8890	0.9429
No log	6.3478	146	0.8791	0.5460	0.8791	0.9376
No log	6.4348	148	0.8696	0.5376	0.8696	0.9325
No log	6.5217	150	0.8663	0.5376	0.8663	0.9308
No log	6.6087	152	0.8609	0.5249	0.8609	0.9278
No log	6.6957	154	0.8697	0.5421	0.8697	0.9326
No log	6.7826	156	0.8790	0.5369	0.8790	0.9376
No log	6.8696	158	0.8813	0.5447	0.8813	0.9388
No log	6.9565	160	0.8858	0.5487	0.8858	0.9412
No log	7.0435	162	0.8795	0.5353	0.8795	0.9378
No log	7.1304	164	0.8819	0.54	0.8819	0.9391
No log	7.2174	166	0.8807	0.5438	0.8807	0.9385
No log	7.3043	168	0.8616	0.5345	0.8616	0.9282
No log	7.3913	170	0.8595	0.5312	0.8595	0.9271
No log	7.4783	172	0.8493	0.5248	0.8493	0.9216
No log	7.5652	174	0.8554	0.5461	0.8554	0.9249
No log	7.6522	176	0.8568	0.5461	0.8568	0.9256
No log	7.7391	178	0.8545	0.5461	0.8545	0.9244
No log	7.8261	180	0.8563	0.5473	0.8563	0.9254
No log	7.9130	182	0.8567	0.5381	0.8567	0.9256
No log	8.0	184	0.8627	0.5370	0.8627	0.9288
No log	8.0870	186	0.8736	0.5370	0.8736	0.9346
No log	8.1739	188	0.8867	0.5474	0.8867	0.9417
No log	8.2609	190	0.8879	0.5435	0.8879	0.9423
No log	8.3478	192	0.8872	0.5364	0.8872	0.9419
No log	8.4348	194	0.8833	0.5103	0.8833	0.9399
No log	8.5217	196	0.8798	0.5020	0.8798	0.9380
No log	8.6087	198	0.8721	0.5103	0.8721	0.9339
No log	8.6957	200	0.8658	0.5364	0.8658	0.9305
No log	8.7826	202	0.8604	0.5483	0.8604	0.9276
No log	8.8696	204	0.8553	0.5322	0.8553	0.9248
No log	8.9565	206	0.8598	0.5252	0.8598	0.9272
No log	9.0435	208	0.8543	0.5256	0.8543	0.9243
No log	9.1304	210	0.8451	0.5553	0.8451	0.9193
No log	9.2174	212	0.8369	0.5319	0.8369	0.9148
No log	9.3043	214	0.8294	0.5304	0.8294	0.9107
No log	9.3913	216	0.8260	0.5346	0.8260	0.9089
No log	9.4783	218	0.8264	0.5219	0.8264	0.9091
No log	9.5652	220	0.8293	0.5123	0.8293	0.9107
No log	9.6522	222	0.8306	0.5123	0.8306	0.9114
No log	9.7391	224	0.8310	0.5130	0.8310	0.9116
No log	9.8261	226	0.8314	0.5130	0.8314	0.9118
No log	9.9130	228	0.8319	0.5114	0.8319	0.9121
No log	10.0	230	0.8322	0.5114	0.8322	0.9123

Framework versions

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