ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k5_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: 0.8805
• Qwk: 0.6107
• Mse: 0.8805
• Rmse: 0.9383

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	5.2386	-0.0179	5.2386	2.2888
No log	0.1481	4	3.1957	0.0848	3.1957	1.7877
No log	0.2222	6	1.8763	0.1064	1.8763	1.3698
No log	0.2963	8	1.5535	0.1159	1.5535	1.2464
No log	0.3704	10	1.9235	0.0203	1.9235	1.3869
No log	0.4444	12	1.4179	0.1392	1.4179	1.1907
No log	0.5185	14	1.2124	0.2269	1.2124	1.1011
No log	0.5926	16	1.2029	0.2191	1.2029	1.0968
No log	0.6667	18	1.1632	0.1927	1.1632	1.0785
No log	0.7407	20	1.1838	0.1930	1.1838	1.0880
No log	0.8148	22	1.1612	0.2560	1.1612	1.0776
No log	0.8889	24	1.2971	0.1202	1.2971	1.1389
No log	0.9630	26	1.4697	0.0270	1.4697	1.2123
No log	1.0370	28	1.6017	0.0524	1.6017	1.2656
No log	1.1111	30	1.4466	0.0430	1.4466	1.2028
No log	1.1852	32	1.2579	0.2154	1.2579	1.1216
No log	1.2593	34	1.1438	0.3729	1.1438	1.0695
No log	1.3333	36	1.0741	0.3424	1.0741	1.0364
No log	1.4074	38	1.0560	0.3067	1.0560	1.0276
No log	1.4815	40	1.1296	0.3819	1.1296	1.0628
No log	1.5556	42	1.4681	0.1620	1.4681	1.2117
No log	1.6296	44	1.7523	0.2431	1.7523	1.3237
No log	1.7037	46	1.3624	0.2435	1.3624	1.1672
No log	1.7778	48	1.0359	0.4882	1.0359	1.0178
No log	1.8519	50	0.9856	0.5400	0.9856	0.9928
No log	1.9259	52	0.9865	0.5342	0.9865	0.9932
No log	2.0	54	0.9669	0.5206	0.9669	0.9833
No log	2.0741	56	1.0151	0.4674	1.0151	1.0075
No log	2.1481	58	1.0703	0.4229	1.0703	1.0345
No log	2.2222	60	1.0025	0.4201	1.0025	1.0012
No log	2.2963	62	0.9796	0.4344	0.9796	0.9897
No log	2.3704	64	0.9449	0.4489	0.9449	0.9721
No log	2.4444	66	0.9476	0.4453	0.9476	0.9734
No log	2.5185	68	1.0399	0.4930	1.0399	1.0198
No log	2.5926	70	1.0654	0.5076	1.0654	1.0322
No log	2.6667	72	1.0146	0.5437	1.0146	1.0073
No log	2.7407	74	0.9349	0.5692	0.9349	0.9669
No log	2.8148	76	1.0363	0.5346	1.0363	1.0180
No log	2.8889	78	1.2632	0.5179	1.2632	1.1239
No log	2.9630	80	1.0889	0.5348	1.0889	1.0435
No log	3.0370	82	0.9279	0.5992	0.9279	0.9633
No log	3.1111	84	0.9285	0.5915	0.9285	0.9636
No log	3.1852	86	0.9120	0.5843	0.9120	0.9550
No log	3.2593	88	0.9055	0.5835	0.9055	0.9516
No log	3.3333	90	0.8969	0.6012	0.8969	0.9471
No log	3.4074	92	0.9200	0.6163	0.9200	0.9592
No log	3.4815	94	0.9427	0.6019	0.9427	0.9709
No log	3.5556	96	0.9046	0.6071	0.9046	0.9511
No log	3.6296	98	0.9655	0.5061	0.9655	0.9826
No log	3.7037	100	0.9876	0.5328	0.9876	0.9938
No log	3.7778	102	0.9321	0.5128	0.9321	0.9654
No log	3.8519	104	0.8977	0.5663	0.8977	0.9475
No log	3.9259	106	0.9211	0.5168	0.9211	0.9597
No log	4.0	108	0.9669	0.5448	0.9669	0.9833
No log	4.0741	110	0.9151	0.5163	0.9151	0.9566
No log	4.1481	112	0.8849	0.5935	0.8849	0.9407
No log	4.2222	114	0.8740	0.6027	0.8740	0.9349
No log	4.2963	116	0.9236	0.5525	0.9236	0.9610
No log	4.3704	118	0.8996	0.5353	0.8996	0.9484
No log	4.4444	120	0.8703	0.6284	0.8703	0.9329
No log	4.5185	122	0.9377	0.6072	0.9377	0.9683
No log	4.5926	124	0.9268	0.6433	0.9268	0.9627
No log	4.6667	126	0.9617	0.6073	0.9617	0.9807
No log	4.7407	128	1.0193	0.5871	1.0193	1.0096
No log	4.8148	130	1.0086	0.5780	1.0086	1.0043
No log	4.8889	132	1.1152	0.5330	1.1152	1.0560
No log	4.9630	134	1.2230	0.5007	1.2230	1.1059
No log	5.0370	136	1.1402	0.5080	1.1402	1.0678
No log	5.1111	138	1.0193	0.4948	1.0193	1.0096
No log	5.1852	140	0.9366	0.5886	0.9366	0.9678
No log	5.2593	142	0.9234	0.5845	0.9234	0.9609
No log	5.3333	144	0.9256	0.5798	0.9256	0.9621
No log	5.4074	146	0.9189	0.5948	0.9189	0.9586
No log	5.4815	148	0.9258	0.6141	0.9258	0.9622
No log	5.5556	150	0.9887	0.5425	0.9887	0.9943
No log	5.6296	152	1.0306	0.5622	1.0306	1.0152
No log	5.7037	154	0.9564	0.5864	0.9564	0.9780
No log	5.7778	156	0.9100	0.6259	0.9100	0.9539
No log	5.8519	158	0.9871	0.5765	0.9871	0.9935
No log	5.9259	160	1.0120	0.5765	1.0120	1.0060
No log	6.0	162	0.9523	0.5841	0.9523	0.9759
No log	6.0741	164	0.8539	0.5806	0.8539	0.9241
No log	6.1481	166	0.9040	0.5935	0.9040	0.9508
No log	6.2222	168	1.0538	0.5823	1.0538	1.0265
No log	6.2963	170	1.1305	0.5169	1.1305	1.0632
No log	6.3704	172	1.0843	0.5630	1.0843	1.0413
No log	6.4444	174	0.9117	0.5932	0.9117	0.9548
No log	6.5185	176	0.8481	0.5368	0.8481	0.9209
No log	6.5926	178	0.9148	0.5771	0.9148	0.9565
No log	6.6667	180	0.8908	0.5246	0.8908	0.9438
No log	6.7407	182	0.8733	0.6168	0.8733	0.9345
No log	6.8148	184	0.9789	0.6274	0.9789	0.9894
No log	6.8889	186	1.1018	0.5719	1.1018	1.0497
No log	6.9630	188	1.1050	0.5594	1.1050	1.0512
No log	7.0370	190	1.0214	0.5835	1.0214	1.0107
No log	7.1111	192	0.9115	0.6367	0.9115	0.9547
No log	7.1852	194	0.8791	0.5602	0.8791	0.9376
No log	7.2593	196	0.8870	0.5585	0.8870	0.9418
No log	7.3333	198	0.8834	0.5889	0.8834	0.9399
No log	7.4074	200	0.9203	0.6085	0.9203	0.9593
No log	7.4815	202	1.0073	0.5709	1.0073	1.0036
No log	7.5556	204	1.0711	0.5551	1.0711	1.0349
No log	7.6296	206	1.0289	0.5699	1.0289	1.0144
No log	7.7037	208	0.9877	0.5708	0.9877	0.9938
No log	7.7778	210	0.9537	0.5743	0.9537	0.9766
No log	7.8519	212	0.8970	0.6130	0.8970	0.9471
No log	7.9259	214	0.8684	0.5916	0.8684	0.9319
No log	8.0	216	0.8627	0.5891	0.8627	0.9288
No log	8.0741	218	0.8661	0.5990	0.8661	0.9306
No log	8.1481	220	0.9005	0.6209	0.9005	0.9489
No log	8.2222	222	0.9524	0.5537	0.9524	0.9759
No log	8.2963	224	0.9541	0.5537	0.9541	0.9768
No log	8.3704	226	0.9273	0.6072	0.9273	0.9629
No log	8.4444	228	0.9248	0.6072	0.9248	0.9617
No log	8.5185	230	0.8998	0.6203	0.8998	0.9486
No log	8.5926	232	0.8722	0.6148	0.8722	0.9339
No log	8.6667	234	0.8635	0.5915	0.8635	0.9292
No log	8.7407	236	0.8605	0.5925	0.8605	0.9276
No log	8.8148	238	0.8581	0.5925	0.8581	0.9264
No log	8.8889	240	0.8566	0.5987	0.8566	0.9255
No log	8.9630	242	0.8653	0.6084	0.8653	0.9302
No log	9.0370	244	0.8800	0.6167	0.8800	0.9381
No log	9.1111	246	0.8872	0.6246	0.8872	0.9419
No log	9.1852	248	0.8859	0.6292	0.8859	0.9412
No log	9.2593	250	0.8821	0.6255	0.8821	0.9392
No log	9.3333	252	0.8769	0.6062	0.8769	0.9364
No log	9.4074	254	0.8721	0.6107	0.8721	0.9339
No log	9.4815	256	0.8690	0.6084	0.8690	0.9322
No log	9.5556	258	0.8698	0.6084	0.8698	0.9326
No log	9.6296	260	0.8721	0.6038	0.8721	0.9339
No log	9.7037	262	0.8741	0.6107	0.8741	0.9349
No log	9.7778	264	0.8763	0.6107	0.8763	0.9361
No log	9.8519	266	0.8793	0.6107	0.8793	0.9377
No log	9.9259	268	0.8805	0.6107	0.8805	0.9384
No log	10.0	270	0.8805	0.6107	0.8805	0.9383

Framework versions

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