ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k3_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: 0.9801
• Qwk: 0.4612
• Mse: 0.9801
• Rmse: 0.9900

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	3.7432	-0.0206	3.7432	1.9347
No log	0.2222	4	2.8371	0.0701	2.8371	1.6844
No log	0.3333	6	1.2043	0.0951	1.2043	1.0974
No log	0.4444	8	0.8529	0.0656	0.8529	0.9235
No log	0.5556	10	0.9310	-0.0536	0.9310	0.9649
No log	0.6667	12	0.7545	0.2149	0.7545	0.8686
No log	0.7778	14	0.8474	0.0918	0.8474	0.9205
No log	0.8889	16	0.9325	0.1299	0.9325	0.9656
No log	1.0	18	0.9678	0.0233	0.9678	0.9838
No log	1.1111	20	0.8209	0.1208	0.8209	0.9060
No log	1.2222	22	0.6855	0.2602	0.6855	0.8279
No log	1.3333	24	0.6957	0.1786	0.6957	0.8341
No log	1.4444	26	0.6687	0.2419	0.6687	0.8177
No log	1.5556	28	0.6456	0.2552	0.6456	0.8035
No log	1.6667	30	0.6630	0.2484	0.6630	0.8142
No log	1.7778	32	0.7048	0.2340	0.7048	0.8395
No log	1.8889	34	0.7451	0.2849	0.7451	0.8632
No log	2.0	36	0.6166	0.3211	0.6166	0.7852
No log	2.1111	38	0.5982	0.2991	0.5982	0.7734
No log	2.2222	40	0.6008	0.3932	0.6008	0.7751
No log	2.3333	42	0.5767	0.3942	0.5767	0.7594
No log	2.4444	44	0.7630	0.3410	0.7630	0.8735
No log	2.5556	46	1.0048	0.2459	1.0048	1.0024
No log	2.6667	48	0.9587	0.3287	0.9587	0.9792
No log	2.7778	50	0.7370	0.4348	0.7370	0.8585
No log	2.8889	52	0.6050	0.5221	0.6050	0.7778
No log	3.0	54	0.7129	0.3726	0.7129	0.8443
No log	3.1111	56	0.6697	0.4558	0.6697	0.8184
No log	3.2222	58	0.6302	0.5415	0.6302	0.7938
No log	3.3333	60	0.9630	0.3410	0.9630	0.9813
No log	3.4444	62	1.1079	0.3120	1.1079	1.0526
No log	3.5556	64	0.8880	0.3850	0.8880	0.9423
No log	3.6667	66	0.6451	0.5450	0.6451	0.8032
No log	3.7778	68	0.6517	0.5121	0.6517	0.8073
No log	3.8889	70	0.6482	0.5156	0.6482	0.8051
No log	4.0	72	0.6720	0.5181	0.6720	0.8198
No log	4.1111	74	0.7699	0.4575	0.7699	0.8774
No log	4.2222	76	0.9008	0.4727	0.9008	0.9491
No log	4.3333	78	0.8930	0.4781	0.8930	0.9450
No log	4.4444	80	0.7944	0.5418	0.7944	0.8913
No log	4.5556	82	0.8473	0.4808	0.8473	0.9205
No log	4.6667	84	0.9104	0.4812	0.9104	0.9541
No log	4.7778	86	0.9421	0.4570	0.9421	0.9706
No log	4.8889	88	1.0657	0.4392	1.0657	1.0323
No log	5.0	90	1.1164	0.3972	1.1164	1.0566
No log	5.1111	92	1.0433	0.4640	1.0433	1.0214
No log	5.2222	94	0.9935	0.4685	0.9935	0.9967
No log	5.3333	96	1.0262	0.4611	1.0262	1.0130
No log	5.4444	98	0.9912	0.4661	0.9912	0.9956
No log	5.5556	100	0.9257	0.4592	0.9257	0.9621
No log	5.6667	102	0.9616	0.4830	0.9616	0.9806
No log	5.7778	104	0.9658	0.4758	0.9658	0.9827
No log	5.8889	106	0.8979	0.4564	0.8979	0.9476
No log	6.0	108	0.8630	0.4705	0.8630	0.9290
No log	6.1111	110	0.8634	0.4560	0.8634	0.9292
No log	6.2222	112	0.8960	0.4587	0.8960	0.9466
No log	6.3333	114	0.9274	0.4584	0.9274	0.9630
No log	6.4444	116	0.9834	0.4644	0.9834	0.9916
No log	6.5556	118	0.9932	0.4697	0.9932	0.9966
No log	6.6667	120	0.9506	0.4843	0.9506	0.9750
No log	6.7778	122	0.9405	0.4713	0.9405	0.9698
No log	6.8889	124	0.9581	0.4834	0.9581	0.9788
No log	7.0	126	0.9690	0.4984	0.9690	0.9844
No log	7.1111	128	0.9656	0.4719	0.9656	0.9827
No log	7.2222	130	1.0177	0.4552	1.0177	1.0088
No log	7.3333	132	1.0755	0.4656	1.0755	1.0370
No log	7.4444	134	1.0506	0.4723	1.0506	1.0250
No log	7.5556	136	0.9834	0.4882	0.9834	0.9917
No log	7.6667	138	0.9520	0.4511	0.9520	0.9757
No log	7.7778	140	0.9599	0.4913	0.9599	0.9797
No log	7.8889	142	0.9545	0.4669	0.9545	0.9770
No log	8.0	144	0.9432	0.4535	0.9432	0.9712
No log	8.1111	146	0.9657	0.4803	0.9657	0.9827
No log	8.2222	148	0.9936	0.4701	0.9936	0.9968
No log	8.3333	150	0.9906	0.4701	0.9906	0.9953
No log	8.4444	152	1.0018	0.4701	1.0018	1.0009
No log	8.5556	154	1.0239	0.4730	1.0239	1.0119
No log	8.6667	156	1.0455	0.4730	1.0455	1.0225
No log	8.7778	158	1.0302	0.4780	1.0302	1.0150
No log	8.8889	160	1.0008	0.4750	1.0008	1.0004
No log	9.0	162	0.9857	0.5145	0.9857	0.9928
No log	9.1111	164	0.9745	0.4691	0.9745	0.9872
No log	9.2222	166	0.9702	0.4668	0.9702	0.9850
No log	9.3333	168	0.9696	0.4686	0.9696	0.9847
No log	9.4444	170	0.9733	0.4642	0.9732	0.9865
No log	9.5556	172	0.9740	0.4730	0.9740	0.9869
No log	9.6667	174	0.9747	0.4505	0.9747	0.9873
No log	9.7778	176	0.9771	0.4562	0.9771	0.9885
No log	9.8889	178	0.9790	0.4562	0.9790	0.9895
No log	10.0	180	0.9801	0.4612	0.9801	0.9900

Framework versions

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