ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k4_task3_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: 0.7984
• Qwk: 0.1304
• Mse: 0.7984
• Rmse: 0.8935

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	3.0653	0.0	3.0653	1.7508
No log	0.2222	4	1.4407	-0.0070	1.4407	1.2003
No log	0.3333	6	1.5395	0.0790	1.5395	1.2408
No log	0.4444	8	1.4855	0.0588	1.4855	1.2188
No log	0.5556	10	0.6009	0.0388	0.6009	0.7752
No log	0.6667	12	0.5559	0.0569	0.5559	0.7456
No log	0.7778	14	0.5833	0.0569	0.5833	0.7637
No log	0.8889	16	0.6115	0.0388	0.6115	0.7820
No log	1.0	18	0.6212	0.0303	0.6212	0.7881
No log	1.1111	20	0.7190	0.0734	0.7190	0.8479
No log	1.2222	22	0.6768	-0.1733	0.6768	0.8227
No log	1.3333	24	0.7859	0.0833	0.7859	0.8865
No log	1.4444	26	0.7311	-0.0818	0.7311	0.8551
No log	1.5556	28	0.7527	0.0388	0.7527	0.8676
No log	1.6667	30	0.7855	0.0388	0.7855	0.8863
No log	1.7778	32	0.6987	-0.0496	0.6987	0.8359
No log	1.8889	34	0.9633	0.0460	0.9633	0.9815
No log	2.0	36	0.7905	0.0588	0.7905	0.8891
No log	2.1111	38	0.6217	0.0388	0.6217	0.7885
No log	2.2222	40	0.7010	0.125	0.7010	0.8373
No log	2.3333	42	0.7053	0.1278	0.7053	0.8398
No log	2.4444	44	0.7417	0.1529	0.7417	0.8612
No log	2.5556	46	0.9363	0.0512	0.9363	0.9676
No log	2.6667	48	0.8773	0.0270	0.8773	0.9366
No log	2.7778	50	0.8536	0.1529	0.8536	0.9239
No log	2.8889	52	0.9767	0.0459	0.9767	0.9883
No log	3.0	54	1.0124	0.0130	1.0124	1.0062
No log	3.1111	56	0.9471	0.0222	0.9471	0.9732
No log	3.2222	58	0.8736	0.1111	0.8736	0.9347
No log	3.3333	60	0.8818	0.1131	0.8818	0.9390
No log	3.4444	62	1.0339	0.0040	1.0339	1.0168
No log	3.5556	64	0.9371	0.1169	0.9371	0.9681
No log	3.6667	66	0.8881	0.1481	0.8881	0.9424
No log	3.7778	68	0.8906	0.1712	0.8906	0.9437
No log	3.8889	70	1.0107	0.0617	1.0107	1.0053
No log	4.0	72	1.1954	0.0490	1.1954	1.0933
No log	4.1111	74	1.1462	0.0701	1.1462	1.0706
No log	4.2222	76	0.9442	-0.0046	0.9442	0.9717
No log	4.3333	78	0.9216	0.0599	0.9216	0.9600
No log	4.4444	80	1.0353	0.0044	1.0353	1.0175
No log	4.5556	82	1.0066	-0.0046	1.0066	1.0033
No log	4.6667	84	0.8853	0.0826	0.8853	0.9409
No log	4.7778	86	0.8948	0.0769	0.8948	0.9459
No log	4.8889	88	0.9006	0.1111	0.9006	0.9490
No log	5.0	90	1.0414	0.1111	1.0414	1.0205
No log	5.1111	92	0.9515	0.1000	0.9515	0.9754
No log	5.2222	94	0.8133	0.1923	0.8133	0.9018
No log	5.3333	96	0.8270	0.1759	0.8270	0.9094
No log	5.4444	98	0.7950	0.2079	0.7950	0.8916
No log	5.5556	100	0.8383	0.1131	0.8383	0.9156
No log	5.6667	102	0.8325	0.0493	0.8325	0.9124
No log	5.7778	104	0.8179	0.0182	0.8179	0.9044
No log	5.8889	106	0.7610	0.1443	0.7610	0.8723
No log	6.0	108	0.7570	0.2079	0.7570	0.8700
No log	6.1111	110	0.7561	0.25	0.7561	0.8695
No log	6.2222	112	0.7796	0.2780	0.7796	0.8830
No log	6.3333	114	0.8409	-0.0233	0.8409	0.9170
No log	6.4444	116	1.0323	0.0916	1.0323	1.0160
No log	6.5556	118	1.0122	0.0569	1.0122	1.0061
No log	6.6667	120	0.8634	-0.0209	0.8634	0.9292
No log	6.7778	122	0.8333	0.2157	0.8333	0.9128
No log	6.8889	124	0.8399	0.2077	0.8399	0.9164
No log	7.0	126	0.8616	-0.0209	0.8616	0.9282
No log	7.1111	128	0.9840	0.0539	0.9840	0.9920
No log	7.2222	130	1.0970	0.0598	1.0970	1.0474
No log	7.3333	132	1.0288	0.0569	1.0288	1.0143
No log	7.4444	134	0.8854	-0.0169	0.8854	0.9410
No log	7.5556	136	0.8334	0.2239	0.8334	0.9129
No log	7.6667	138	0.8522	0.2157	0.8522	0.9232
No log	7.7778	140	0.8229	0.2871	0.8229	0.9071
No log	7.8889	142	0.8069	0.1238	0.8069	0.8983
No log	8.0	144	0.8405	0.0476	0.8405	0.9168
No log	8.1111	146	0.9286	0.0393	0.9286	0.9636
No log	8.2222	148	0.9608	0.0393	0.9608	0.9802
No log	8.3333	150	0.9041	0.0308	0.9041	0.9508
No log	8.4444	152	0.8537	0.0423	0.8537	0.9239
No log	8.5556	154	0.8187	0.1357	0.8187	0.9048
No log	8.6667	156	0.7930	0.2239	0.7930	0.8905
No log	8.7778	158	0.7937	0.2475	0.7937	0.8909
No log	8.8889	160	0.7900	0.2161	0.7900	0.8888
No log	9.0	162	0.7848	0.2239	0.7848	0.8859
No log	9.1111	164	0.7960	0.1429	0.7960	0.8922
No log	9.2222	166	0.8044	0.1045	0.8044	0.8969
No log	9.3333	168	0.8004	0.1429	0.8004	0.8947
No log	9.4444	170	0.7969	0.1304	0.7969	0.8927
No log	9.5556	172	0.7941	0.1304	0.7941	0.8911
No log	9.6667	174	0.7969	0.1304	0.7969	0.8927
No log	9.7778	176	0.8003	0.1304	0.8003	0.8946
No log	9.8889	178	0.7990	0.1304	0.7990	0.8939
No log	10.0	180	0.7984	0.1304	0.7984	0.8935

Framework versions

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