ArabicNewSplits6_FineTuningAraBERT_run3_AugV5_k4_task1_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: 0.8008
• Qwk: 0.6438
• Mse: 0.8008
• Rmse: 0.8949

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	5.1133	-0.0308	5.1133	2.2613
No log	0.1739	4	2.8809	0.1078	2.8809	1.6973
No log	0.2609	6	1.9798	0.1025	1.9798	1.4070
No log	0.3478	8	1.4621	0.0393	1.4621	1.2092
No log	0.4348	10	1.2853	0.2065	1.2853	1.1337
No log	0.5217	12	1.2212	0.2794	1.2212	1.1051
No log	0.6087	14	1.3222	0.1396	1.3222	1.1499
No log	0.6957	16	1.3056	0.2224	1.3056	1.1426
No log	0.7826	18	1.3662	0.1350	1.3662	1.1688
No log	0.8696	20	1.2845	0.2161	1.2845	1.1334
No log	0.9565	22	1.0808	0.3725	1.0808	1.0396
No log	1.0435	24	1.0851	0.3951	1.0851	1.0417
No log	1.1304	26	1.0873	0.3504	1.0873	1.0428
No log	1.2174	28	1.2269	0.2473	1.2269	1.1077
No log	1.3043	30	1.3524	0.2696	1.3524	1.1629
No log	1.3913	32	1.0779	0.3377	1.0779	1.0382
No log	1.4783	34	0.9387	0.3839	0.9387	0.9689
No log	1.5652	36	0.9492	0.4014	0.9492	0.9743
No log	1.6522	38	1.1762	0.3133	1.1762	1.0845
No log	1.7391	40	1.2624	0.2915	1.2624	1.1236
No log	1.8261	42	1.1779	0.2871	1.1779	1.0853
No log	1.9130	44	0.9814	0.3323	0.9814	0.9906
No log	2.0	46	0.8725	0.4461	0.8725	0.9341
No log	2.0870	48	0.8423	0.4299	0.8423	0.9177
No log	2.1739	50	0.8636	0.4382	0.8636	0.9293
No log	2.2609	52	0.8301	0.4657	0.8301	0.9111
No log	2.3478	54	0.7889	0.4825	0.7889	0.8882
No log	2.4348	56	0.7861	0.5302	0.7861	0.8866
No log	2.5217	58	0.7812	0.5710	0.7812	0.8838
No log	2.6087	60	0.7821	0.5694	0.7821	0.8844
No log	2.6957	62	0.7751	0.5694	0.7751	0.8804
No log	2.7826	64	0.7493	0.5888	0.7493	0.8656
No log	2.8696	66	0.7433	0.6319	0.7433	0.8621
No log	2.9565	68	0.7360	0.6454	0.7360	0.8579
No log	3.0435	70	0.6900	0.5943	0.6900	0.8307
No log	3.1304	72	0.7335	0.6010	0.7335	0.8565
No log	3.2174	74	0.8132	0.5913	0.8132	0.9018
No log	3.3043	76	0.7274	0.6141	0.7274	0.8529
No log	3.3913	78	0.6587	0.6466	0.6587	0.8116
No log	3.4783	80	0.7062	0.6862	0.7062	0.8403
No log	3.5652	82	0.7648	0.6929	0.7648	0.8746
No log	3.6522	84	0.6882	0.6939	0.6882	0.8296
No log	3.7391	86	0.6367	0.6475	0.6367	0.7979
No log	3.8261	88	0.6463	0.6348	0.6463	0.8039
No log	3.9130	90	0.6619	0.6696	0.6619	0.8135
No log	4.0	92	0.7920	0.6444	0.7920	0.8899
No log	4.0870	94	0.8334	0.6406	0.8334	0.9129
No log	4.1739	96	0.7461	0.6544	0.7461	0.8638
No log	4.2609	98	0.7365	0.6419	0.7365	0.8582
No log	4.3478	100	0.7467	0.6376	0.7467	0.8641
No log	4.4348	102	0.7680	0.6511	0.7680	0.8763
No log	4.5217	104	0.8282	0.6306	0.8282	0.9100
No log	4.6087	106	0.9107	0.6021	0.9107	0.9543
No log	4.6957	108	0.9619	0.5589	0.9619	0.9808
No log	4.7826	110	0.9715	0.5920	0.9715	0.9857
No log	4.8696	112	0.8733	0.6345	0.8733	0.9345
No log	4.9565	114	0.7412	0.7082	0.7412	0.8609
No log	5.0435	116	0.7110	0.7035	0.7110	0.8432
No log	5.1304	118	0.7165	0.6686	0.7165	0.8465
No log	5.2174	120	0.7028	0.7022	0.7028	0.8384
No log	5.3043	122	0.7668	0.6724	0.7668	0.8757
No log	5.3913	124	0.8537	0.6139	0.8537	0.9240
No log	5.4783	126	0.8654	0.6067	0.8654	0.9303
No log	5.5652	128	0.8084	0.6402	0.8084	0.8991
No log	5.6522	130	0.7248	0.6703	0.7248	0.8514
No log	5.7391	132	0.6965	0.6519	0.6965	0.8346
No log	5.8261	134	0.6997	0.6309	0.6997	0.8365
No log	5.9130	136	0.7306	0.6867	0.7306	0.8547
No log	6.0	138	0.7941	0.6645	0.7941	0.8911
No log	6.0870	140	0.8540	0.6053	0.8540	0.9241
No log	6.1739	142	0.8208	0.6440	0.8208	0.9060
No log	6.2609	144	0.7677	0.6887	0.7677	0.8762
No log	6.3478	146	0.7382	0.6905	0.7382	0.8592
No log	6.4348	148	0.7265	0.6844	0.7265	0.8523
No log	6.5217	150	0.7427	0.6945	0.7427	0.8618
No log	6.6087	152	0.8021	0.6654	0.8021	0.8956
No log	6.6957	154	0.9066	0.6031	0.9066	0.9522
No log	6.7826	156	0.9541	0.5726	0.9541	0.9768
No log	6.8696	158	0.9308	0.6087	0.9308	0.9648
No log	6.9565	160	0.8625	0.6662	0.8625	0.9287
No log	7.0435	162	0.8590	0.6501	0.8590	0.9268
No log	7.1304	164	0.8425	0.6807	0.8425	0.9179
No log	7.2174	166	0.8487	0.6732	0.8487	0.9212
No log	7.3043	168	0.8644	0.6510	0.8644	0.9297
No log	7.3913	170	0.8919	0.6244	0.8919	0.9444
No log	7.4783	172	0.9267	0.5797	0.9267	0.9627
No log	7.5652	174	0.9268	0.5620	0.9268	0.9627
No log	7.6522	176	0.8973	0.5678	0.8973	0.9472
No log	7.7391	178	0.8704	0.5942	0.8704	0.9330
No log	7.8261	180	0.8300	0.6242	0.8300	0.9111
No log	7.9130	182	0.7892	0.6361	0.7892	0.8884
No log	8.0	184	0.7691	0.6618	0.7691	0.8770
No log	8.0870	186	0.7657	0.6637	0.7657	0.8750
No log	8.1739	188	0.7870	0.6289	0.7870	0.8871
No log	8.2609	190	0.8199	0.6289	0.8199	0.9055
No log	8.3478	192	0.8558	0.6058	0.8558	0.9251
No log	8.4348	194	0.8981	0.5537	0.8981	0.9477
No log	8.5217	196	0.9276	0.5668	0.9276	0.9631
No log	8.6087	198	0.9351	0.5583	0.9351	0.9670
No log	8.6957	200	0.9185	0.5765	0.9185	0.9584
No log	8.7826	202	0.8856	0.5776	0.8856	0.9410
No log	8.8696	204	0.8410	0.6148	0.8410	0.9171
No log	8.9565	206	0.8069	0.6609	0.8069	0.8983
No log	9.0435	208	0.7804	0.6703	0.7804	0.8834
No log	9.1304	210	0.7736	0.6776	0.7736	0.8796
No log	9.2174	212	0.7724	0.6842	0.7724	0.8789
No log	9.3043	214	0.7729	0.6776	0.7729	0.8792
No log	9.3913	216	0.7770	0.6776	0.7770	0.8814
No log	9.4783	218	0.7834	0.6586	0.7834	0.8851
No log	9.5652	220	0.7886	0.6540	0.7886	0.8880
No log	9.6522	222	0.7932	0.6540	0.7932	0.8906
No log	9.7391	224	0.7965	0.6531	0.7965	0.8925
No log	9.8261	226	0.7988	0.6438	0.7988	0.8938
No log	9.9130	228	0.8001	0.6438	0.8001	0.8945
No log	10.0	230	0.8008	0.6438	0.8008	0.8949

Framework versions

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