ArabicNewSplits6_FineTuningAraBERT_run3_AugV5_k5_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: 0.6828
• Qwk: 0.7126
• Mse: 0.6828
• Rmse: 0.8263

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	5.2393	-0.0054	5.2393	2.2889
No log	0.1481	4	3.1701	0.0661	3.1701	1.7805
No log	0.2222	6	1.9306	0.0816	1.9306	1.3895
No log	0.2963	8	1.4029	0.1558	1.4029	1.1844
No log	0.3704	10	1.2405	0.2972	1.2405	1.1138
No log	0.4444	12	1.1223	0.2819	1.1223	1.0594
No log	0.5185	14	1.2607	0.1959	1.2607	1.1228
No log	0.5926	16	1.3881	0.1290	1.3881	1.1782
No log	0.6667	18	1.6133	-0.1170	1.6133	1.2701
No log	0.7407	20	1.4986	-0.0347	1.4986	1.2242
No log	0.8148	22	1.3233	0.1152	1.3233	1.1503
No log	0.8889	24	1.4333	0.0860	1.4333	1.1972
No log	0.9630	26	1.7192	0.1266	1.7192	1.3112
No log	1.0370	28	1.9788	0.1883	1.9788	1.4067
No log	1.1111	30	2.2475	0.1433	2.2475	1.4992
No log	1.1852	32	1.8124	0.1986	1.8124	1.3463
No log	1.2593	34	1.5039	0.2726	1.5039	1.2263
No log	1.3333	36	1.3136	0.2799	1.3136	1.1461
No log	1.4074	38	1.0465	0.3800	1.0465	1.0230
No log	1.4815	40	0.8490	0.4639	0.8490	0.9214
No log	1.5556	42	0.8098	0.4776	0.8098	0.8999
No log	1.6296	44	0.9726	0.4810	0.9726	0.9862
No log	1.7037	46	1.3476	0.3587	1.3476	1.1608
No log	1.7778	48	1.6874	0.2912	1.6874	1.2990
No log	1.8519	50	1.4137	0.3392	1.4137	1.1890
No log	1.9259	52	0.8839	0.5474	0.8839	0.9402
No log	2.0	54	0.7163	0.5931	0.7163	0.8463
No log	2.0741	56	0.6879	0.6332	0.6879	0.8294
No log	2.1481	58	0.7028	0.6082	0.7028	0.8384
No log	2.2222	60	0.7993	0.5938	0.7993	0.8940
No log	2.2963	62	0.9671	0.5373	0.9671	0.9834
No log	2.3704	64	0.9543	0.5467	0.9543	0.9769
No log	2.4444	66	0.8122	0.5986	0.8122	0.9012
No log	2.5185	68	0.7688	0.6090	0.7688	0.8768
No log	2.5926	70	0.8449	0.6029	0.8449	0.9192
No log	2.6667	72	1.1516	0.5182	1.1516	1.0731
No log	2.7407	74	1.2526	0.4959	1.2526	1.1192
No log	2.8148	76	0.9888	0.5330	0.9888	0.9944
No log	2.8889	78	0.7292	0.6355	0.7292	0.8539
No log	2.9630	80	0.6500	0.6680	0.6500	0.8063
No log	3.0370	82	0.6360	0.6602	0.6360	0.7975
No log	3.1111	84	0.6309	0.6835	0.6309	0.7943
No log	3.1852	86	0.6443	0.6767	0.6443	0.8027
No log	3.2593	88	0.6577	0.6651	0.6577	0.8110
No log	3.3333	90	0.6863	0.6649	0.6863	0.8284
No log	3.4074	92	0.7775	0.6516	0.7775	0.8818
No log	3.4815	94	0.8367	0.6310	0.8367	0.9147
No log	3.5556	96	0.8545	0.6183	0.8545	0.9244
No log	3.6296	98	0.7295	0.6759	0.7295	0.8541
No log	3.7037	100	0.6761	0.7051	0.6761	0.8222
No log	3.7778	102	0.7058	0.7133	0.7058	0.8401
No log	3.8519	104	0.6884	0.7127	0.6884	0.8297
No log	3.9259	106	0.6728	0.7097	0.6728	0.8202
No log	4.0	108	0.6905	0.7069	0.6905	0.8309
No log	4.0741	110	0.7503	0.6888	0.7503	0.8662
No log	4.1481	112	0.7294	0.6749	0.7294	0.8540
No log	4.2222	114	0.6632	0.724	0.6632	0.8144
No log	4.2963	116	0.6518	0.7041	0.6518	0.8073
No log	4.3704	118	0.6549	0.6996	0.6549	0.8092
No log	4.4444	120	0.6606	0.7087	0.6606	0.8128
No log	4.5185	122	0.6730	0.7199	0.6730	0.8204
No log	4.5926	124	0.6540	0.7069	0.6540	0.8087
No log	4.6667	126	0.6602	0.7258	0.6602	0.8125
No log	4.7407	128	0.6799	0.7214	0.6799	0.8246
No log	4.8148	130	0.6752	0.7168	0.6752	0.8217
No log	4.8889	132	0.6425	0.7077	0.6425	0.8015
No log	4.9630	134	0.6418	0.7042	0.6418	0.8011
No log	5.0370	136	0.6684	0.7057	0.6684	0.8175
No log	5.1111	138	0.6742	0.7113	0.6742	0.8211
No log	5.1852	140	0.6752	0.7189	0.6752	0.8217
No log	5.2593	142	0.6868	0.7003	0.6868	0.8287
No log	5.3333	144	0.6966	0.7232	0.6966	0.8346
No log	5.4074	146	0.7056	0.7215	0.7056	0.8400
No log	5.4815	148	0.6697	0.7058	0.6697	0.8184
No log	5.5556	150	0.6781	0.7126	0.6781	0.8235
No log	5.6296	152	0.6818	0.6956	0.6818	0.8257
No log	5.7037	154	0.6560	0.7291	0.6560	0.8099
No log	5.7778	156	0.6912	0.6984	0.6912	0.8314
No log	5.8519	158	0.7475	0.6447	0.7475	0.8646
No log	5.9259	160	0.8265	0.6059	0.8265	0.9091
No log	6.0	162	0.7673	0.6256	0.7673	0.8759
No log	6.0741	164	0.6706	0.6884	0.6706	0.8189
No log	6.1481	166	0.6521	0.7133	0.6521	0.8076
No log	6.2222	168	0.6729	0.7072	0.6729	0.8203
No log	6.2963	170	0.6670	0.7172	0.6670	0.8167
No log	6.3704	172	0.6478	0.7378	0.6478	0.8048
No log	6.4444	174	0.6661	0.7129	0.6661	0.8162
No log	6.5185	176	0.7105	0.6825	0.7105	0.8429
No log	6.5926	178	0.6952	0.6924	0.6952	0.8338
No log	6.6667	180	0.6611	0.7349	0.6611	0.8131
No log	6.7407	182	0.6728	0.7278	0.6728	0.8202
No log	6.8148	184	0.6781	0.7137	0.6781	0.8235
No log	6.8889	186	0.6966	0.6877	0.6966	0.8346
No log	6.9630	188	0.7104	0.6775	0.7104	0.8429
No log	7.0370	190	0.6972	0.6728	0.6972	0.8350
No log	7.1111	192	0.6918	0.6618	0.6918	0.8317
No log	7.1852	194	0.7076	0.6629	0.7076	0.8412
No log	7.2593	196	0.7612	0.6483	0.7612	0.8725
No log	7.3333	198	0.8607	0.5994	0.8607	0.9277
No log	7.4074	200	0.9824	0.5786	0.9824	0.9912
No log	7.4815	202	0.9858	0.5929	0.9858	0.9929
No log	7.5556	204	0.8722	0.6057	0.8722	0.9339
No log	7.6296	206	0.7396	0.6790	0.7396	0.8600
No log	7.7037	208	0.6916	0.7253	0.6916	0.8316
No log	7.7778	210	0.6967	0.7096	0.6967	0.8347
No log	7.8519	212	0.6966	0.7001	0.6966	0.8346
No log	7.9259	214	0.6958	0.7177	0.6958	0.8342
No log	8.0	216	0.6978	0.7065	0.6978	0.8353
No log	8.0741	218	0.7052	0.7114	0.7052	0.8398
No log	8.1481	220	0.7263	0.6860	0.7263	0.8522
No log	8.2222	222	0.7340	0.6798	0.7340	0.8567
No log	8.2963	224	0.7559	0.6670	0.7559	0.8694
No log	8.3704	226	0.7747	0.6472	0.7747	0.8802
No log	8.4444	228	0.7705	0.6565	0.7705	0.8778
No log	8.5185	230	0.7498	0.6574	0.7498	0.8659
No log	8.5926	232	0.7188	0.6550	0.7188	0.8478
No log	8.6667	234	0.6956	0.6907	0.6956	0.8341
No log	8.7407	236	0.6871	0.7113	0.6871	0.8289
No log	8.8148	238	0.6861	0.7086	0.6861	0.8283
No log	8.8889	240	0.6877	0.7192	0.6877	0.8293
No log	8.9630	242	0.6890	0.7113	0.6890	0.8300
No log	9.0370	244	0.6906	0.7060	0.6906	0.8310
No log	9.1111	246	0.6962	0.6803	0.6962	0.8344
No log	9.1852	248	0.6970	0.6849	0.6970	0.8349
No log	9.2593	250	0.6970	0.6837	0.6970	0.8349
No log	9.3333	252	0.6998	0.6837	0.6998	0.8366
No log	9.4074	254	0.6975	0.6875	0.6975	0.8352
No log	9.4815	256	0.6928	0.7077	0.6928	0.8323
No log	9.5556	258	0.6894	0.7115	0.6894	0.8303
No log	9.6296	260	0.6868	0.7064	0.6868	0.8287
No log	9.7037	262	0.6857	0.7064	0.6857	0.8281
No log	9.7778	264	0.6846	0.7064	0.6846	0.8274
No log	9.8519	266	0.6837	0.7064	0.6837	0.8268
No log	9.9259	268	0.6831	0.7064	0.6831	0.8265
No log	10.0	270	0.6828	0.7126	0.6828	0.8263

Framework versions

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