ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run1_AugV5_k5_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: 1.1763
• Qwk: 0.4605
• Mse: 1.1763
• Rmse: 1.0846

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	0.0667	2	4.0837	0.0077	4.0837	2.0208
No log	0.1333	4	2.2489	0.0356	2.2489	1.4996
No log	0.2	6	1.1627	0.0678	1.1627	1.0783
No log	0.2667	8	0.8043	0.0785	0.8043	0.8968
No log	0.3333	10	0.6679	0.2333	0.6679	0.8173
No log	0.4	12	0.7458	0.1960	0.7458	0.8636
No log	0.4667	14	0.7820	0.1728	0.7820	0.8843
No log	0.5333	16	0.6955	0.2333	0.6955	0.8340
No log	0.6	18	0.6861	0.1941	0.6861	0.8283
No log	0.6667	20	0.6652	0.2202	0.6652	0.8156
No log	0.7333	22	0.6507	0.2108	0.6507	0.8067
No log	0.8	24	0.6455	0.2019	0.6455	0.8034
No log	0.8667	26	0.6492	0.2061	0.6492	0.8057
No log	0.9333	28	0.6086	0.2288	0.6086	0.7801
No log	1.0	30	0.5884	0.2288	0.5884	0.7671
No log	1.0667	32	0.5735	0.3248	0.5735	0.7573
No log	1.1333	34	0.6545	0.4050	0.6545	0.8090
No log	1.2	36	0.8081	0.3265	0.8081	0.8989
No log	1.2667	38	0.9552	0.2028	0.9552	0.9773
No log	1.3333	40	0.8868	0.3001	0.8868	0.9417
No log	1.4	42	0.9251	0.2856	0.9251	0.9618
No log	1.4667	44	0.7740	0.4139	0.7740	0.8798
No log	1.5333	46	0.6223	0.4687	0.6223	0.7889
No log	1.6	48	0.5792	0.5407	0.5792	0.7610
No log	1.6667	50	0.5742	0.5628	0.5742	0.7578
No log	1.7333	52	0.5901	0.5039	0.5901	0.7682
No log	1.8	54	0.6455	0.4490	0.6455	0.8034
No log	1.8667	56	0.7868	0.5436	0.7868	0.8870
No log	1.9333	58	1.0129	0.3638	1.0129	1.0064
No log	2.0	60	1.1180	0.3107	1.1180	1.0573
No log	2.0667	62	0.8470	0.3944	0.8470	0.9203
No log	2.1333	64	0.6667	0.5091	0.6667	0.8165
No log	2.2	66	0.6752	0.4636	0.6752	0.8217
No log	2.2667	68	0.6622	0.4752	0.6622	0.8137
No log	2.3333	70	0.6745	0.5218	0.6745	0.8213
No log	2.4	72	0.6679	0.4951	0.6679	0.8173
No log	2.4667	74	0.6935	0.4976	0.6935	0.8328
No log	2.5333	76	0.7531	0.4791	0.7531	0.8678
No log	2.6	78	0.8022	0.4560	0.8022	0.8957
No log	2.6667	80	0.7732	0.4969	0.7732	0.8793
No log	2.7333	82	0.7692	0.5567	0.7692	0.8771
No log	2.8	84	0.8250	0.5067	0.8250	0.9083
No log	2.8667	86	0.9976	0.3987	0.9976	0.9988
No log	2.9333	88	1.1769	0.3105	1.1769	1.0849
No log	3.0	90	1.3130	0.2489	1.3130	1.1459
No log	3.0667	92	1.2292	0.2785	1.2292	1.1087
No log	3.1333	94	0.9605	0.4482	0.9605	0.9801
No log	3.2	96	0.8021	0.5065	0.8021	0.8956
No log	3.2667	98	0.7348	0.5642	0.7348	0.8572
No log	3.3333	100	0.8498	0.4755	0.8498	0.9219
No log	3.4	102	0.8807	0.4895	0.8807	0.9384
No log	3.4667	104	0.8389	0.4853	0.8389	0.9159
No log	3.5333	106	0.7544	0.5582	0.7544	0.8685
No log	3.6	108	0.7556	0.5236	0.7556	0.8692
No log	3.6667	110	0.9965	0.3398	0.9965	0.9983
No log	3.7333	112	1.1329	0.2478	1.1329	1.0644
No log	3.8	114	1.0871	0.2835	1.0871	1.0426
No log	3.8667	116	0.9319	0.4225	0.9319	0.9654
No log	3.9333	118	0.8743	0.4253	0.8743	0.9350
No log	4.0	120	0.9284	0.4450	0.9284	0.9635
No log	4.0667	122	0.9472	0.4644	0.9472	0.9732
No log	4.1333	124	0.9195	0.4891	0.9195	0.9589
No log	4.2	126	0.9073	0.5184	0.9073	0.9525
No log	4.2667	128	0.8937	0.5564	0.8937	0.9453
No log	4.3333	130	0.9036	0.5116	0.9036	0.9506
No log	4.4	132	0.9683	0.4926	0.9683	0.9840
No log	4.4667	134	0.9648	0.5005	0.9648	0.9822
No log	4.5333	136	0.9015	0.5163	0.9015	0.9495
No log	4.6	138	0.9257	0.5040	0.9257	0.9621
No log	4.6667	140	0.9250	0.5219	0.9250	0.9617
No log	4.7333	142	0.9532	0.5053	0.9532	0.9763
No log	4.8	144	1.0423	0.5196	1.0423	1.0209
No log	4.8667	146	1.0461	0.5074	1.0461	1.0228
No log	4.9333	148	1.0043	0.5354	1.0043	1.0021
No log	5.0	150	1.0316	0.5156	1.0316	1.0157
No log	5.0667	152	1.0496	0.4843	1.0496	1.0245
No log	5.1333	154	1.1000	0.4457	1.1000	1.0488
No log	5.2	156	1.1650	0.4853	1.1650	1.0793
No log	5.2667	158	1.1683	0.4765	1.1683	1.0809
No log	5.3333	160	1.1158	0.4461	1.1158	1.0563
No log	5.4	162	1.0671	0.4630	1.0671	1.0330
No log	5.4667	164	1.0181	0.4823	1.0181	1.0090
No log	5.5333	166	0.9909	0.4816	0.9909	0.9954
No log	5.6	168	1.0419	0.4914	1.0419	1.0208
No log	5.6667	170	1.0980	0.4991	1.0980	1.0478
No log	5.7333	172	1.1124	0.4985	1.1124	1.0547
No log	5.8	174	1.1182	0.4991	1.1182	1.0575
No log	5.8667	176	1.0847	0.4853	1.0847	1.0415
No log	5.9333	178	1.0123	0.4555	1.0123	1.0061
No log	6.0	180	0.9837	0.4890	0.9837	0.9918
No log	6.0667	182	0.9879	0.4920	0.9879	0.9939
No log	6.1333	184	1.0064	0.4912	1.0064	1.0032
No log	6.2	186	1.0813	0.4809	1.0813	1.0399
No log	6.2667	188	1.1888	0.4578	1.1888	1.0903
No log	6.3333	190	1.2692	0.4496	1.2692	1.1266
No log	6.4	192	1.3198	0.4519	1.3198	1.1488
No log	6.4667	194	1.2890	0.4377	1.2890	1.1353
No log	6.5333	196	1.2013	0.4777	1.2013	1.0960
No log	6.6	198	1.0814	0.4966	1.0814	1.0399
No log	6.6667	200	1.0082	0.4988	1.0082	1.0041
No log	6.7333	202	1.0045	0.5075	1.0045	1.0023
No log	6.8	204	1.0341	0.4711	1.0341	1.0169
No log	6.8667	206	1.0886	0.4624	1.0886	1.0434
No log	6.9333	208	1.1772	0.4581	1.1772	1.0850
No log	7.0	210	1.2867	0.4170	1.2867	1.1343
No log	7.0667	212	1.2967	0.4106	1.2967	1.1387
No log	7.1333	214	1.2448	0.4083	1.2448	1.1157
No log	7.2	216	1.1677	0.4190	1.1677	1.0806
No log	7.2667	218	1.0514	0.5135	1.0514	1.0254
No log	7.3333	220	0.9848	0.4711	0.9848	0.9924
No log	7.4	222	0.9586	0.4735	0.9586	0.9791
No log	7.4667	224	0.9735	0.4840	0.9735	0.9867
No log	7.5333	226	1.0181	0.4614	1.0181	1.0090
No log	7.6	228	1.0887	0.4598	1.0887	1.0434
No log	7.6667	230	1.1527	0.4419	1.1527	1.0737
No log	7.7333	232	1.1728	0.4384	1.1728	1.0830
No log	7.8	234	1.1489	0.4477	1.1489	1.0718
No log	7.8667	236	1.1355	0.4477	1.1355	1.0656
No log	7.9333	238	1.0906	0.4567	1.0906	1.0443
No log	8.0	240	1.0833	0.4618	1.0833	1.0408
No log	8.0667	242	1.1170	0.4619	1.1170	1.0569
No log	8.1333	244	1.1528	0.4421	1.1528	1.0737
No log	8.2	246	1.1828	0.4470	1.1828	1.0876
No log	8.2667	248	1.1870	0.4470	1.1870	1.0895
No log	8.3333	250	1.1661	0.4421	1.1661	1.0799
No log	8.4	252	1.1331	0.4609	1.1331	1.0645
No log	8.4667	254	1.0930	0.4759	1.0930	1.0455
No log	8.5333	256	1.0512	0.4661	1.0512	1.0253
No log	8.6	258	1.0314	0.4719	1.0314	1.0156
No log	8.6667	260	1.0361	0.4661	1.0361	1.0179
No log	8.7333	262	1.0576	0.4711	1.0576	1.0284
No log	8.8	264	1.0938	0.4604	1.0938	1.0459
No log	8.8667	266	1.1465	0.4701	1.1465	1.0708
No log	8.9333	268	1.2092	0.4174	1.2092	1.0996
No log	9.0	270	1.2876	0.4027	1.2876	1.1347
No log	9.0667	272	1.3435	0.3843	1.3435	1.1591
No log	9.1333	274	1.3676	0.3846	1.3676	1.1695
No log	9.2	276	1.3782	0.3846	1.3782	1.1740
No log	9.2667	278	1.3685	0.3896	1.3685	1.1698
No log	9.3333	280	1.3431	0.3904	1.3431	1.1589
No log	9.4	282	1.3119	0.4139	1.3119	1.1454
No log	9.4667	284	1.2822	0.4137	1.2822	1.1323
No log	9.5333	286	1.2569	0.4239	1.2569	1.1211
No log	9.6	288	1.2303	0.4420	1.2303	1.1092
No log	9.6667	290	1.2107	0.4734	1.2107	1.1003
No log	9.7333	292	1.1945	0.4737	1.1945	1.0930
No log	9.8	294	1.1840	0.4559	1.1840	1.0881
No log	9.8667	296	1.1790	0.4605	1.1790	1.0858
No log	9.9333	298	1.1768	0.4605	1.1768	1.0848
No log	10.0	300	1.1763	0.4605	1.1763	1.0846

Framework versions

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