ArabicNewSplits4_FineTuningAraBERT_run2_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: 0.8418
• Qwk: 0.6783
• Mse: 0.8418
• Rmse: 0.9175

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	0.1111	2	2.2522	0.0020	2.2522	1.5007
No log	0.2222	4	1.5425	0.1190	1.5425	1.2420
No log	0.3333	6	1.8093	-0.0277	1.8093	1.3451
No log	0.4444	8	1.8289	0.1553	1.8289	1.3524
No log	0.5556	10	1.8804	0.2692	1.8804	1.3713
No log	0.6667	12	1.6127	0.2089	1.6127	1.2699
No log	0.7778	14	1.6145	0.0619	1.6145	1.2706
No log	0.8889	16	1.5487	0.0825	1.5487	1.2445
No log	1.0	18	1.5198	0.0882	1.5198	1.2328
No log	1.1111	20	1.5209	0.1551	1.5209	1.2332
No log	1.2222	22	1.5216	0.2495	1.5216	1.2335
No log	1.3333	24	1.6570	0.3703	1.6570	1.2872
No log	1.4444	26	1.6994	0.3802	1.6994	1.3036
No log	1.5556	28	1.5689	0.3787	1.5689	1.2525
No log	1.6667	30	1.4095	0.3248	1.4095	1.1872
No log	1.7778	32	1.3254	0.3085	1.3254	1.1513
No log	1.8889	34	1.2966	0.3094	1.2966	1.1387
No log	2.0	36	1.2223	0.2818	1.2223	1.1056
No log	2.1111	38	1.1438	0.2275	1.1438	1.0695
No log	2.2222	40	1.1353	0.3036	1.1353	1.0655
No log	2.3333	42	1.1775	0.4174	1.1775	1.0851
No log	2.4444	44	1.2494	0.4263	1.2494	1.1178
No log	2.5556	46	1.3891	0.4430	1.3891	1.1786
No log	2.6667	48	1.3484	0.4749	1.3484	1.1612
No log	2.7778	50	1.1974	0.4579	1.1974	1.0943
No log	2.8889	52	1.1471	0.4727	1.1471	1.0710
No log	3.0	54	1.0471	0.4982	1.0471	1.0233
No log	3.1111	56	0.9736	0.5433	0.9736	0.9867
No log	3.2222	58	0.8732	0.5814	0.8732	0.9345
No log	3.3333	60	0.8737	0.6026	0.8737	0.9347
No log	3.4444	62	0.9289	0.5655	0.9289	0.9638
No log	3.5556	64	1.0540	0.4881	1.0540	1.0267
No log	3.6667	66	1.1819	0.5036	1.1819	1.0872
No log	3.7778	68	1.1641	0.5043	1.1641	1.0789
No log	3.8889	70	1.0078	0.4867	1.0078	1.0039
No log	4.0	72	0.8985	0.5525	0.8985	0.9479
No log	4.1111	74	0.8514	0.5872	0.8514	0.9227
No log	4.2222	76	0.8552	0.5538	0.8552	0.9248
No log	4.3333	78	0.9141	0.5579	0.9141	0.9561
No log	4.4444	80	0.9720	0.5809	0.9720	0.9859
No log	4.5556	82	1.0164	0.5435	1.0164	1.0081
No log	4.6667	84	1.1823	0.5327	1.1823	1.0873
No log	4.7778	86	1.1873	0.5319	1.1873	1.0896
No log	4.8889	88	1.0123	0.5489	1.0123	1.0061
No log	5.0	90	0.9157	0.6011	0.9157	0.9569
No log	5.1111	92	0.8757	0.5970	0.8757	0.9358
No log	5.2222	94	0.9226	0.5806	0.9226	0.9605
No log	5.3333	96	1.1069	0.5428	1.1069	1.0521
No log	5.4444	98	1.4655	0.5360	1.4655	1.2106
No log	5.5556	100	1.5864	0.5296	1.5864	1.2595
No log	5.6667	102	1.4459	0.5496	1.4459	1.2024
No log	5.7778	104	1.1355	0.5437	1.1355	1.0656
No log	5.8889	106	0.8516	0.5887	0.8516	0.9228
No log	6.0	108	0.7551	0.6506	0.7551	0.8690
No log	6.1111	110	0.7563	0.6086	0.7563	0.8696
No log	6.2222	112	0.7640	0.6154	0.7640	0.8741
No log	6.3333	114	0.8086	0.6106	0.8086	0.8992
No log	6.4444	116	0.9637	0.5666	0.9637	0.9817
No log	6.5556	118	1.2269	0.5536	1.2269	1.1076
No log	6.6667	120	1.3834	0.5496	1.3834	1.1762
No log	6.7778	122	1.3671	0.5595	1.3671	1.1692
No log	6.8889	124	1.2228	0.5643	1.2228	1.1058
No log	7.0	126	1.0705	0.5678	1.0705	1.0346
No log	7.1111	128	0.9632	0.6116	0.9632	0.9814
No log	7.2222	130	0.9107	0.6303	0.9107	0.9543
No log	7.3333	132	0.9181	0.6209	0.9181	0.9582
No log	7.4444	134	0.9224	0.6210	0.9224	0.9604
No log	7.5556	136	0.9378	0.6315	0.9378	0.9684
No log	7.6667	138	0.9648	0.6315	0.9648	0.9823
No log	7.7778	140	0.9595	0.6315	0.9595	0.9796
No log	7.8889	142	0.9329	0.6315	0.9329	0.9659
No log	8.0	144	0.9436	0.6260	0.9436	0.9714
No log	8.1111	146	0.9188	0.6316	0.9188	0.9586
No log	8.2222	148	0.8788	0.6369	0.8788	0.9375
No log	8.3333	150	0.8222	0.6804	0.8222	0.9068
No log	8.4444	152	0.7958	0.7150	0.7958	0.8921
No log	8.5556	154	0.7984	0.6987	0.7984	0.8935
No log	8.6667	156	0.8141	0.6768	0.8141	0.9023
No log	8.7778	158	0.8217	0.6689	0.8217	0.9065
No log	8.8889	160	0.8366	0.6776	0.8366	0.9147
No log	9.0	162	0.8449	0.6783	0.8449	0.9192
No log	9.1111	164	0.8526	0.6783	0.8526	0.9234
No log	9.2222	166	0.8409	0.6697	0.8409	0.9170
No log	9.3333	168	0.8351	0.6697	0.8351	0.9138
No log	9.4444	170	0.8312	0.6783	0.8312	0.9117
No log	9.5556	172	0.8295	0.6783	0.8295	0.9108
No log	9.6667	174	0.8392	0.6783	0.8392	0.9161
No log	9.7778	176	0.8446	0.6783	0.8446	0.9190
No log	9.8889	178	0.8439	0.6783	0.8439	0.9186
No log	10.0	180	0.8418	0.6783	0.8418	0.9175

Framework versions

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