ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k7_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: 0.8887
• Qwk: 0.7017
• Mse: 0.8887
• Rmse: 0.9427

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	0.0690	2	2.1303	-0.0173	2.1303	1.4596
No log	0.1379	4	1.4918	0.1825	1.4918	1.2214
No log	0.2069	6	1.3855	0.1398	1.3855	1.1771
No log	0.2759	8	1.5882	0.3301	1.5882	1.2602
No log	0.3448	10	1.7914	0.3138	1.7914	1.3384
No log	0.4138	12	1.3932	0.2311	1.3932	1.1803
No log	0.4828	14	1.3721	0.1627	1.3721	1.1714
No log	0.5517	16	1.3769	0.1789	1.3769	1.1734
No log	0.6207	18	1.3156	0.1789	1.3156	1.1470
No log	0.6897	20	1.3555	0.2861	1.3555	1.1642
No log	0.7586	22	1.5428	0.3577	1.5428	1.2421
No log	0.8276	24	1.5253	0.3589	1.5253	1.2350
No log	0.8966	26	1.3806	0.3423	1.3806	1.1750
No log	0.9655	28	1.1601	0.3364	1.1601	1.0771
No log	1.0345	30	1.1003	0.3464	1.1003	1.0489
No log	1.1034	32	1.0946	0.4213	1.0946	1.0462
No log	1.1724	34	1.0450	0.4070	1.0450	1.0223
No log	1.2414	36	0.9890	0.4939	0.9890	0.9945
No log	1.3103	38	1.0719	0.4424	1.0719	1.0353
No log	1.3793	40	1.2458	0.4473	1.2458	1.1161
No log	1.4483	42	1.2652	0.4663	1.2652	1.1248
No log	1.5172	44	1.0447	0.4733	1.0447	1.0221
No log	1.5862	46	0.9836	0.4480	0.9836	0.9918
No log	1.6552	48	0.9504	0.4843	0.9504	0.9749
No log	1.7241	50	1.0028	0.4762	1.0028	1.0014
No log	1.7931	52	1.5456	0.4876	1.5456	1.2432
No log	1.8621	54	1.7937	0.4578	1.7937	1.3393
No log	1.9310	56	1.5793	0.5169	1.5793	1.2567
No log	2.0	58	1.1152	0.5199	1.1152	1.0560
No log	2.0690	60	0.8665	0.5188	0.8665	0.9308
No log	2.1379	62	0.8828	0.5461	0.8828	0.9396
No log	2.2069	64	0.8657	0.5042	0.8657	0.9304
No log	2.2759	66	0.9289	0.4881	0.9289	0.9638
No log	2.3448	68	1.2933	0.5042	1.2933	1.1373
No log	2.4138	70	1.5826	0.5050	1.5826	1.2580
No log	2.4828	72	1.4713	0.5208	1.4713	1.2130
No log	2.5517	74	1.1716	0.5433	1.1716	1.0824
No log	2.6207	76	0.9048	0.5283	0.9048	0.9512
No log	2.6897	78	0.8498	0.5953	0.8498	0.9219
No log	2.7586	80	0.8335	0.6215	0.8335	0.9130
No log	2.8276	82	0.8520	0.5909	0.8520	0.9231
No log	2.8966	84	0.9579	0.5037	0.9579	0.9787
No log	2.9655	86	1.0773	0.5538	1.0773	1.0379
No log	3.0345	88	1.2156	0.5788	1.2156	1.1026
No log	3.1034	90	1.0895	0.5930	1.0895	1.0438
No log	3.1724	92	1.0276	0.6262	1.0276	1.0137
No log	3.2414	94	0.8756	0.6652	0.8756	0.9357
No log	3.3103	96	0.7265	0.7062	0.7265	0.8523
No log	3.3793	98	0.7163	0.7062	0.7163	0.8464
No log	3.4483	100	0.7012	0.7127	0.7012	0.8374
No log	3.5172	102	0.7444	0.6883	0.7444	0.8628
No log	3.5862	104	0.7635	0.6910	0.7635	0.8738
No log	3.6552	106	0.7131	0.6865	0.7131	0.8445
No log	3.7241	108	0.6762	0.6942	0.6762	0.8223
No log	3.7931	110	0.6721	0.7181	0.6721	0.8198
No log	3.8621	112	0.7054	0.7172	0.7054	0.8399
No log	3.9310	114	0.7904	0.7062	0.7904	0.8890
No log	4.0	116	0.7750	0.6966	0.7750	0.8803
No log	4.0690	118	0.7587	0.6980	0.7587	0.8710
No log	4.1379	120	0.8562	0.7054	0.8562	0.9253
No log	4.2069	122	1.0409	0.6630	1.0409	1.0202
No log	4.2759	124	1.3425	0.6002	1.3425	1.1587
No log	4.3448	126	1.4710	0.5837	1.4710	1.2129
No log	4.4138	128	1.2616	0.6059	1.2616	1.1232
No log	4.4828	130	1.0515	0.6337	1.0515	1.0254
No log	4.5517	132	1.0242	0.6429	1.0242	1.0120
No log	4.6207	134	1.0004	0.6435	1.0004	1.0002
No log	4.6897	136	0.8205	0.7275	0.8205	0.9058
No log	4.7586	138	0.7318	0.7347	0.7318	0.8554
No log	4.8276	140	0.7669	0.7302	0.7669	0.8757
No log	4.8966	142	0.8831	0.7003	0.8831	0.9397
No log	4.9655	144	0.8695	0.6923	0.8695	0.9325
No log	5.0345	146	0.8376	0.6925	0.8376	0.9152
No log	5.1034	148	0.7899	0.7166	0.7899	0.8888
No log	5.1724	150	0.7689	0.7194	0.7689	0.8768
No log	5.2414	152	0.8575	0.7073	0.8575	0.9260
No log	5.3103	154	1.0162	0.6604	1.0162	1.0080
No log	5.3793	156	1.0443	0.6449	1.0443	1.0219
No log	5.4483	158	0.9083	0.6842	0.9083	0.9531
No log	5.5172	160	0.7860	0.6913	0.7860	0.8866
No log	5.5862	162	0.7994	0.7054	0.7994	0.8941
No log	5.6552	164	0.8663	0.6635	0.8663	0.9307
No log	5.7241	166	0.8657	0.6678	0.8657	0.9305
No log	5.7931	168	0.8991	0.6653	0.8991	0.9482
No log	5.8621	170	0.9230	0.6771	0.9230	0.9607
No log	5.9310	172	0.8920	0.6889	0.8920	0.9445
No log	6.0	174	0.7935	0.6893	0.7935	0.8908
No log	6.0690	176	0.7611	0.7120	0.7611	0.8724
No log	6.1379	178	0.8028	0.7026	0.8028	0.8960
No log	6.2069	180	0.8856	0.7006	0.8856	0.9410
No log	6.2759	182	1.0387	0.6810	1.0387	1.0192
No log	6.3448	184	1.0770	0.6615	1.0770	1.0378
No log	6.4138	186	0.9470	0.6773	0.9470	0.9731
No log	6.4828	188	0.8020	0.7253	0.8020	0.8955
No log	6.5517	190	0.7086	0.7138	0.7086	0.8418
No log	6.6207	192	0.7091	0.7091	0.7091	0.8421
No log	6.6897	194	0.7669	0.7194	0.7669	0.8757
No log	6.7586	196	0.9128	0.6507	0.9128	0.9554
No log	6.8276	198	1.1312	0.6298	1.1312	1.0636
No log	6.8966	200	1.2957	0.6001	1.2957	1.1383
No log	6.9655	202	1.3195	0.5990	1.3195	1.1487
No log	7.0345	204	1.2897	0.6001	1.2897	1.1357
No log	7.1034	206	1.2257	0.6072	1.2257	1.1071
No log	7.1724	208	1.1713	0.6161	1.1713	1.0823
No log	7.2414	210	1.1077	0.6200	1.1077	1.0525
No log	7.3103	212	1.0855	0.6439	1.0855	1.0418
No log	7.3793	214	1.0134	0.6514	1.0134	1.0067
No log	7.4483	216	0.9232	0.6527	0.9232	0.9608
No log	7.5172	218	0.9282	0.6493	0.9282	0.9634
No log	7.5862	220	0.9554	0.6511	0.9554	0.9774
No log	7.6552	222	0.9890	0.6511	0.9890	0.9945
No log	7.7241	224	0.9998	0.6496	0.9998	0.9999
No log	7.7931	226	0.9761	0.6511	0.9761	0.9880
No log	7.8621	228	0.9501	0.6629	0.9501	0.9747
No log	7.9310	230	0.8918	0.6720	0.8918	0.9443
No log	8.0	232	0.8581	0.6592	0.8581	0.9264
No log	8.0690	234	0.8400	0.6712	0.8400	0.9165
No log	8.1379	236	0.8504	0.6720	0.8504	0.9222
No log	8.2069	238	0.8834	0.6878	0.8834	0.9399
No log	8.2759	240	0.8813	0.6861	0.8813	0.9388
No log	8.3448	242	0.8747	0.6745	0.8747	0.9353
No log	8.4138	244	0.9106	0.6785	0.9106	0.9543
No log	8.4828	246	0.9180	0.6785	0.9180	0.9581
No log	8.5517	248	0.8902	0.6902	0.8902	0.9435
No log	8.6207	250	0.8713	0.6902	0.8713	0.9334
No log	8.6897	252	0.8512	0.6991	0.8512	0.9226
No log	8.7586	254	0.8409	0.6933	0.8409	0.9170
No log	8.8276	256	0.8153	0.7058	0.8153	0.9030
No log	8.8966	258	0.8118	0.7183	0.8118	0.9010
No log	8.9655	260	0.8381	0.7108	0.8381	0.9155
No log	9.0345	262	0.8725	0.6995	0.8725	0.9341
No log	9.1034	264	0.8997	0.6820	0.8997	0.9485
No log	9.1724	266	0.9296	0.6720	0.9296	0.9642
No log	9.2414	268	0.9519	0.6543	0.9519	0.9756
No log	9.3103	270	0.9813	0.6538	0.9813	0.9906
No log	9.3793	272	0.9842	0.6538	0.9842	0.9921
No log	9.4483	274	0.9859	0.6538	0.9859	0.9929
No log	9.5172	276	0.9726	0.6577	0.9726	0.9862
No log	9.5862	278	0.9539	0.6577	0.9539	0.9767
No log	9.6552	280	0.9383	0.6743	0.9383	0.9687
No log	9.7241	282	0.9236	0.6622	0.9236	0.9610
No log	9.7931	284	0.9078	0.6662	0.9078	0.9528
No log	9.8621	286	0.8990	0.6976	0.8990	0.9482
No log	9.9310	288	0.8922	0.6976	0.8922	0.9446
No log	10.0	290	0.8887	0.7017	0.8887	0.9427

Framework versions

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