ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k4_task1_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: 0.6692
• Qwk: 0.7037
• Mse: 0.6692
• Rmse: 0.8181

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	0.0741	2	5.1476	-0.0323	5.1476	2.2688
No log	0.1481	4	3.3495	0.0811	3.3495	1.8302
No log	0.2222	6	2.3915	0.1871	2.3915	1.5464
No log	0.2963	8	1.7911	0.1117	1.7911	1.3383
No log	0.3704	10	1.3212	0.1888	1.3212	1.1494
No log	0.4444	12	1.1129	0.3848	1.1129	1.0549
No log	0.5185	14	1.0434	0.3556	1.0434	1.0215
No log	0.5926	16	1.1057	0.4306	1.1057	1.0515
No log	0.6667	18	1.5275	0.1973	1.5275	1.2359
No log	0.7407	20	1.3552	0.2925	1.3552	1.1641
No log	0.8148	22	1.1243	0.4117	1.1243	1.0603
No log	0.8889	24	1.0306	0.4715	1.0306	1.0152
No log	0.9630	26	1.0248	0.4980	1.0248	1.0123
No log	1.0370	28	1.7011	0.3797	1.7011	1.3043
No log	1.1111	30	2.3666	0.2015	2.3666	1.5384
No log	1.1852	32	2.2094	0.3017	2.2094	1.4864
No log	1.2593	34	1.4323	0.4333	1.4323	1.1968
No log	1.3333	36	0.8500	0.5784	0.8500	0.9220
No log	1.4074	38	0.7797	0.5906	0.7797	0.8830
No log	1.4815	40	0.8224	0.6108	0.8224	0.9069
No log	1.5556	42	1.1603	0.5163	1.1603	1.0772
No log	1.6296	44	1.4998	0.4345	1.4998	1.2247
No log	1.7037	46	1.6857	0.3751	1.6857	1.2984
No log	1.7778	48	1.4620	0.4184	1.4620	1.2091
No log	1.8519	50	1.1167	0.4719	1.1167	1.0567
No log	1.9259	52	1.0695	0.4762	1.0695	1.0342
No log	2.0	54	1.2207	0.4307	1.2207	1.1049
No log	2.0741	56	1.6435	0.3374	1.6435	1.2820
No log	2.1481	58	1.7131	0.3272	1.7131	1.3088
No log	2.2222	60	1.3591	0.4446	1.3591	1.1658
No log	2.2963	62	0.8428	0.6217	0.8428	0.9180
No log	2.3704	64	0.6403	0.7274	0.6403	0.8002
No log	2.4444	66	0.6363	0.7188	0.6363	0.7977
No log	2.5185	68	0.8223	0.5773	0.8223	0.9068
No log	2.5926	70	0.7662	0.6348	0.7662	0.8753
No log	2.6667	72	0.6871	0.7074	0.6871	0.8289
No log	2.7407	74	0.9026	0.6506	0.9026	0.9500
No log	2.8148	76	1.3488	0.5189	1.3488	1.1614
No log	2.8889	78	1.5016	0.4938	1.5016	1.2254
No log	2.9630	80	1.3345	0.5269	1.3345	1.1552
No log	3.0370	82	0.9885	0.6205	0.9885	0.9942
No log	3.1111	84	0.6980	0.7034	0.6980	0.8355
No log	3.1852	86	0.6383	0.7396	0.6383	0.7989
No log	3.2593	88	0.7504	0.6745	0.7504	0.8663
No log	3.3333	90	0.8135	0.6480	0.8135	0.9020
No log	3.4074	92	0.7267	0.6863	0.7267	0.8524
No log	3.4815	94	0.6448	0.7394	0.6448	0.8030
No log	3.5556	96	0.7376	0.6936	0.7376	0.8588
No log	3.6296	98	0.9283	0.6343	0.9283	0.9635
No log	3.7037	100	0.9509	0.6371	0.9509	0.9751
No log	3.7778	102	0.7818	0.6960	0.7818	0.8842
No log	3.8519	104	0.6800	0.6976	0.6800	0.8246
No log	3.9259	106	0.6317	0.7455	0.6317	0.7948
No log	4.0	108	0.6379	0.7413	0.6379	0.7987
No log	4.0741	110	0.6617	0.7363	0.6617	0.8134
No log	4.1481	112	0.6993	0.7295	0.6993	0.8362
No log	4.2222	114	0.7349	0.6989	0.7349	0.8572
No log	4.2963	116	0.7845	0.6729	0.7845	0.8857
No log	4.3704	118	0.9119	0.6449	0.9119	0.9550
No log	4.4444	120	0.9598	0.6530	0.9598	0.9797
No log	4.5185	122	0.8484	0.6604	0.8484	0.9211
No log	4.5926	124	0.7332	0.7162	0.7332	0.8563
No log	4.6667	126	0.7307	0.7263	0.7307	0.8548
No log	4.7407	128	0.8922	0.6713	0.8922	0.9445
No log	4.8148	130	0.9350	0.6463	0.9350	0.9669
No log	4.8889	132	0.8404	0.6702	0.8404	0.9167
No log	4.9630	134	0.7061	0.7207	0.7061	0.8403
No log	5.0370	136	0.6717	0.7498	0.6717	0.8196
No log	5.1111	138	0.7027	0.7265	0.7027	0.8383
No log	5.1852	140	0.7253	0.7060	0.7253	0.8517
No log	5.2593	142	0.6890	0.7187	0.6890	0.8301
No log	5.3333	144	0.6464	0.7587	0.6464	0.8040
No log	5.4074	146	0.6568	0.7350	0.6568	0.8104
No log	5.4815	148	0.6983	0.7080	0.6983	0.8357
No log	5.5556	150	0.7155	0.7067	0.7155	0.8459
No log	5.6296	152	0.6927	0.7320	0.6927	0.8323
No log	5.7037	154	0.6909	0.7177	0.6909	0.8312
No log	5.7778	156	0.7407	0.712	0.7407	0.8606
No log	5.8519	158	0.7468	0.7158	0.7468	0.8642
No log	5.9259	160	0.7005	0.7152	0.7005	0.8370
No log	6.0	162	0.6784	0.7369	0.6784	0.8236
No log	6.0741	164	0.6886	0.7305	0.6886	0.8298
No log	6.1481	166	0.6797	0.7305	0.6797	0.8244
No log	6.2222	168	0.6617	0.7192	0.6617	0.8134
No log	6.2963	170	0.6578	0.7154	0.6578	0.8111
No log	6.3704	172	0.6466	0.7126	0.6466	0.8041
No log	6.4444	174	0.6462	0.7185	0.6462	0.8038
No log	6.5185	176	0.6391	0.7165	0.6391	0.7994
No log	6.5926	178	0.6386	0.7344	0.6386	0.7991
No log	6.6667	180	0.6424	0.7370	0.6424	0.8015
No log	6.7407	182	0.6622	0.7165	0.6622	0.8138
No log	6.8148	184	0.7218	0.6916	0.7218	0.8496
No log	6.8889	186	0.7473	0.6827	0.7473	0.8645
No log	6.9630	188	0.7407	0.6898	0.7407	0.8606
No log	7.0370	190	0.7082	0.7264	0.7082	0.8416
No log	7.1111	192	0.7028	0.7274	0.7028	0.8384
No log	7.1852	194	0.7183	0.7058	0.7183	0.8475
No log	7.2593	196	0.7434	0.7110	0.7434	0.8622
No log	7.3333	198	0.7584	0.7129	0.7584	0.8708
No log	7.4074	200	0.7612	0.7129	0.7612	0.8725
No log	7.4815	202	0.7635	0.7110	0.7635	0.8738
No log	7.5556	204	0.7543	0.7147	0.7543	0.8685
No log	7.6296	206	0.7470	0.7092	0.7470	0.8643
No log	7.7037	208	0.7379	0.7036	0.7379	0.8590
No log	7.7778	210	0.7321	0.7002	0.7321	0.8557
No log	7.8519	212	0.7295	0.6888	0.7295	0.8541
No log	7.9259	214	0.7251	0.6883	0.7251	0.8515
No log	8.0	216	0.7280	0.7020	0.7280	0.8532
No log	8.0741	218	0.7276	0.7264	0.7276	0.8530
No log	8.1481	220	0.7176	0.7020	0.7176	0.8471
No log	8.2222	222	0.7071	0.6894	0.7071	0.8409
No log	8.2963	224	0.6949	0.6906	0.6949	0.8336
No log	8.3704	226	0.6899	0.6917	0.6899	0.8306
No log	8.4444	228	0.6857	0.6948	0.6857	0.8281
No log	8.5185	230	0.6829	0.6886	0.6829	0.8264
No log	8.5926	232	0.6790	0.6842	0.6790	0.8240
No log	8.6667	234	0.6762	0.6917	0.6762	0.8223
No log	8.7407	236	0.6771	0.6854	0.6771	0.8229
No log	8.8148	238	0.6812	0.6916	0.6812	0.8253
No log	8.8889	240	0.6853	0.6965	0.6853	0.8278
No log	8.9630	242	0.6863	0.7050	0.6863	0.8284
No log	9.0370	244	0.6852	0.7037	0.6852	0.8278
No log	9.1111	246	0.6853	0.7079	0.6853	0.8278
No log	9.1852	248	0.6846	0.7079	0.6846	0.8274
No log	9.2593	250	0.6817	0.7079	0.6817	0.8257
No log	9.3333	252	0.6798	0.7020	0.6798	0.8245
No log	9.4074	254	0.6775	0.7039	0.6775	0.8231
No log	9.4815	256	0.6751	0.7039	0.6751	0.8217
No log	9.5556	258	0.6746	0.7039	0.6746	0.8213
No log	9.6296	260	0.6735	0.7039	0.6735	0.8207
No log	9.7037	262	0.6721	0.7039	0.6721	0.8198
No log	9.7778	264	0.6709	0.7039	0.6709	0.8191
No log	9.8519	266	0.6700	0.7079	0.6700	0.8186
No log	9.9259	268	0.6695	0.7037	0.6695	0.8182
No log	10.0	270	0.6692	0.7037	0.6692	0.8181

Framework versions

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