ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k4_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: 0.8095
• Qwk: 0.4986
• Mse: 0.8095
• Rmse: 0.8997

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	0.0870	2	4.1925	-0.0203	4.1925	2.0476
No log	0.1739	4	2.2103	0.0619	2.2103	1.4867
No log	0.2609	6	1.2865	-0.0207	1.2865	1.1342
No log	0.3478	8	1.3894	-0.0658	1.3894	1.1787
No log	0.4348	10	1.2917	-0.0655	1.2917	1.1366
No log	0.5217	12	0.8176	0.1257	0.8176	0.9042
No log	0.6087	14	0.7075	0.2162	0.7075	0.8411
No log	0.6957	16	0.7071	0.1881	0.7071	0.8409
No log	0.7826	18	0.8759	0.1081	0.8759	0.9359
No log	0.8696	20	0.9172	0.0721	0.9172	0.9577
No log	0.9565	22	0.7592	0.0911	0.7592	0.8713
No log	1.0435	24	0.7373	0.1779	0.7373	0.8587
No log	1.1304	26	0.6890	0.1967	0.6890	0.8300
No log	1.2174	28	0.7025	0.2052	0.7025	0.8382
No log	1.3043	30	0.6873	0.2374	0.6873	0.8291
No log	1.3913	32	0.6544	0.3114	0.6544	0.8090
No log	1.4783	34	0.6285	0.3333	0.6285	0.7927
No log	1.5652	36	0.6440	0.3271	0.6440	0.8025
No log	1.6522	38	0.6472	0.3358	0.6472	0.8045
No log	1.7391	40	0.6365	0.4123	0.6365	0.7978
No log	1.8261	42	0.6135	0.3947	0.6135	0.7833
No log	1.9130	44	0.5982	0.3937	0.5982	0.7734
No log	2.0	46	0.5856	0.3958	0.5856	0.7652
No log	2.0870	48	0.5988	0.3720	0.5988	0.7738
No log	2.1739	50	0.6198	0.3937	0.6198	0.7872
No log	2.2609	52	0.6903	0.2997	0.6903	0.8308
No log	2.3478	54	0.7931	0.2062	0.7931	0.8906
No log	2.4348	56	0.7857	0.2716	0.7857	0.8864
No log	2.5217	58	0.7280	0.3157	0.7280	0.8532
No log	2.6087	60	0.6320	0.4214	0.6320	0.7950
No log	2.6957	62	0.6978	0.4005	0.6978	0.8353
No log	2.7826	64	0.7574	0.3661	0.7574	0.8703
No log	2.8696	66	0.7639	0.3690	0.7639	0.8740
No log	2.9565	68	0.6458	0.4316	0.6458	0.8036
No log	3.0435	70	0.6270	0.4589	0.6270	0.7918
No log	3.1304	72	0.5871	0.4958	0.5871	0.7662
No log	3.2174	74	0.6232	0.4033	0.6232	0.7894
No log	3.3043	76	0.5829	0.4005	0.5829	0.7635
No log	3.3913	78	0.5520	0.3449	0.5520	0.7430
No log	3.4783	80	0.5477	0.3479	0.5477	0.7401
No log	3.5652	82	0.5532	0.4611	0.5532	0.7438
No log	3.6522	84	0.5573	0.5237	0.5573	0.7466
No log	3.7391	86	0.5541	0.5799	0.5541	0.7444
No log	3.8261	88	0.5468	0.5289	0.5468	0.7395
No log	3.9130	90	0.5749	0.4703	0.5749	0.7582
No log	4.0	92	0.5882	0.4653	0.5882	0.7670
No log	4.0870	94	0.5707	0.5465	0.5707	0.7555
No log	4.1739	96	0.5531	0.6051	0.5531	0.7437
No log	4.2609	98	0.5624	0.5787	0.5624	0.7499
No log	4.3478	100	0.5793	0.5890	0.5793	0.7611
No log	4.4348	102	0.6071	0.4832	0.6071	0.7791
No log	4.5217	104	0.6357	0.4780	0.6357	0.7973
No log	4.6087	106	0.6480	0.4773	0.6480	0.8050
No log	4.6957	108	0.6564	0.4975	0.6564	0.8102
No log	4.7826	110	0.6606	0.5138	0.6606	0.8128
No log	4.8696	112	0.6559	0.5149	0.6559	0.8099
No log	4.9565	114	0.6403	0.5171	0.6403	0.8002
No log	5.0435	116	0.6528	0.5174	0.6528	0.8080
No log	5.1304	118	0.6482	0.5161	0.6482	0.8051
No log	5.2174	120	0.6271	0.5622	0.6271	0.7919
No log	5.3043	122	0.6408	0.5288	0.6408	0.8005
No log	5.3913	124	0.6724	0.4642	0.6724	0.8200
No log	5.4783	126	0.7165	0.4672	0.7165	0.8465
No log	5.5652	128	0.7144	0.4575	0.7144	0.8452
No log	5.6522	130	0.6833	0.5091	0.6833	0.8266
No log	5.7391	132	0.6603	0.4823	0.6603	0.8126
No log	5.8261	134	0.6590	0.4879	0.6590	0.8118
No log	5.9130	136	0.6741	0.5296	0.6741	0.8210
No log	6.0	138	0.6980	0.5231	0.6980	0.8354
No log	6.0870	140	0.7108	0.5155	0.7108	0.8431
No log	6.1739	142	0.7155	0.4879	0.7155	0.8459
No log	6.2609	144	0.7189	0.4935	0.7189	0.8479
No log	6.3478	146	0.7026	0.5225	0.7026	0.8382
No log	6.4348	148	0.6837	0.5183	0.6837	0.8269
No log	6.5217	150	0.6635	0.5609	0.6635	0.8145
No log	6.6087	152	0.6649	0.5689	0.6649	0.8154
No log	6.6957	154	0.6702	0.5428	0.6702	0.8187
No log	6.7826	156	0.6721	0.5234	0.6721	0.8198
No log	6.8696	158	0.6673	0.5504	0.6673	0.8169
No log	6.9565	160	0.6677	0.5609	0.6677	0.8171
No log	7.0435	162	0.6924	0.5348	0.6924	0.8321
No log	7.1304	164	0.7143	0.5322	0.7143	0.8452
No log	7.2174	166	0.7145	0.5298	0.7145	0.8453
No log	7.3043	168	0.7245	0.5343	0.7245	0.8512
No log	7.3913	170	0.7321	0.5019	0.7321	0.8556
No log	7.4783	172	0.7401	0.4997	0.7401	0.8603
No log	7.5652	174	0.7551	0.5071	0.7551	0.8689
No log	7.6522	176	0.7656	0.5071	0.7656	0.8750
No log	7.7391	178	0.7778	0.4894	0.7778	0.8820
No log	7.8261	180	0.7846	0.5041	0.7846	0.8858
No log	7.9130	182	0.7833	0.5041	0.7833	0.8851
No log	8.0	184	0.7753	0.5121	0.7753	0.8805
No log	8.0870	186	0.7511	0.5545	0.7511	0.8667
No log	8.1739	188	0.7251	0.5458	0.7251	0.8516
No log	8.2609	190	0.7098	0.5187	0.7098	0.8425
No log	8.3478	192	0.7026	0.5083	0.7026	0.8382
No log	8.4348	194	0.7012	0.5197	0.7012	0.8374
No log	8.5217	196	0.7096	0.5488	0.7096	0.8424
No log	8.6087	198	0.7240	0.5705	0.7240	0.8509
No log	8.6957	200	0.7346	0.5706	0.7346	0.8571
No log	8.7826	202	0.7477	0.5545	0.7477	0.8647
No log	8.8696	204	0.7658	0.5066	0.7658	0.8751
No log	8.9565	206	0.7854	0.4994	0.7854	0.8862
No log	9.0435	208	0.7937	0.4986	0.7937	0.8909
No log	9.1304	210	0.7933	0.4986	0.7933	0.8907
No log	9.2174	212	0.7895	0.4986	0.7895	0.8885
No log	9.3043	214	0.7870	0.4986	0.7870	0.8872
No log	9.3913	216	0.7895	0.4986	0.7895	0.8885
No log	9.4783	218	0.7957	0.4986	0.7957	0.8920
No log	9.5652	220	0.8002	0.4986	0.8002	0.8946
No log	9.6522	222	0.8022	0.4986	0.8022	0.8957
No log	9.7391	224	0.8049	0.4986	0.8049	0.8972
No log	9.8261	226	0.8068	0.4986	0.8068	0.8982
No log	9.9130	228	0.8086	0.4986	0.8086	0.8992
No log	10.0	230	0.8095	0.4986	0.8095	0.8997

Framework versions

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