ArabicNewSplits7_B_usingALLEssays_FineTuningAraBERT_run1_AugV5_k1_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: 2.3119
• Qwk: 0.1471
• Mse: 2.3119
• Rmse: 1.5205

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.6667	2	6.0240	0.0	6.0240	2.4544
No log	1.3333	4	3.2891	0.1562	3.2891	1.8136
No log	2.0	6	2.4128	-0.0154	2.4128	1.5533
No log	2.6667	8	1.8941	0.1441	1.8941	1.3762
No log	3.3333	10	2.0622	0.0458	2.0622	1.4361
No log	4.0	12	2.2035	0.2238	2.2035	1.4844
No log	4.6667	14	2.2088	0.1818	2.2088	1.4862
No log	5.3333	16	2.1716	0.1353	2.1716	1.4736
No log	6.0	18	2.2350	0.0645	2.2350	1.4950
No log	6.6667	20	2.3679	0.0432	2.3679	1.5388
No log	7.3333	22	2.4105	-0.1951	2.4105	1.5526
No log	8.0	24	2.4939	-0.1575	2.4939	1.5792
No log	8.6667	26	2.6082	0.0280	2.6082	1.6150
No log	9.3333	28	2.6092	0.0142	2.6092	1.6153
No log	10.0	30	2.3989	-0.1034	2.3989	1.5488
No log	10.6667	32	2.3386	-0.1034	2.3386	1.5292
No log	11.3333	34	2.3996	0.0444	2.3996	1.5491
No log	12.0	36	2.3776	0.0567	2.3776	1.5419
No log	12.6667	38	2.3822	0.0699	2.3822	1.5434
No log	13.3333	40	2.3940	0.0699	2.3940	1.5472
No log	14.0	42	2.3498	0.0305	2.3498	1.5329
No log	14.6667	44	2.5496	0.0544	2.5496	1.5967
No log	15.3333	46	2.8348	0.0268	2.8348	1.6837
No log	16.0	48	2.7311	0.0268	2.7311	1.6526
No log	16.6667	50	2.4069	0.1111	2.4069	1.5514
No log	17.3333	52	2.2647	0.1014	2.2647	1.5049
No log	18.0	54	2.3137	0.1111	2.3137	1.5211
No log	18.6667	56	2.2567	0.0588	2.2567	1.5022
No log	19.3333	58	2.2500	0.0735	2.2500	1.5000
No log	20.0	60	2.2747	0.0730	2.2747	1.5082
No log	20.6667	62	2.2748	0.0746	2.2748	1.5083
No log	21.3333	64	2.3927	0.0966	2.3927	1.5468
No log	22.0	66	2.3174	0.1127	2.3174	1.5223
No log	22.6667	68	2.2283	0.1714	2.2283	1.4928
No log	23.3333	70	2.1077	0.2206	2.1077	1.4518
No log	24.0	72	2.0926	0.1765	2.0926	1.4466
No log	24.6667	74	2.1371	0.1898	2.1371	1.4619
No log	25.3333	76	2.1505	0.1618	2.1505	1.4664
No log	26.0	78	2.3387	0.0685	2.3387	1.5293
No log	26.6667	80	2.5138	0.0405	2.5138	1.5855
No log	27.3333	82	2.3569	0.0972	2.3569	1.5352
No log	28.0	84	2.2393	0.1343	2.2393	1.4964
No log	28.6667	86	2.3124	0.1739	2.3124	1.5206
No log	29.3333	88	2.2837	0.1277	2.2837	1.5112
No log	30.0	90	2.1057	0.1475	2.1057	1.4511
No log	30.6667	92	2.0055	0.0354	2.0055	1.4161
No log	31.3333	94	1.9977	0.0702	1.9977	1.4134
No log	32.0	96	2.0984	0.1240	2.0984	1.4486
No log	32.6667	98	2.4068	0.0690	2.4068	1.5514
No log	33.3333	100	2.4733	0.0548	2.4733	1.5727
No log	34.0	102	2.2820	0.1752	2.2820	1.5106
No log	34.6667	104	2.1455	0.1832	2.1455	1.4648
No log	35.3333	106	2.0006	0.1760	2.0006	1.4144
No log	36.0	108	1.9944	0.1760	1.9944	1.4122
No log	36.6667	110	2.1016	0.1846	2.1016	1.4497
No log	37.3333	112	2.2652	0.1752	2.2652	1.5050
No log	38.0	114	2.3218	0.1449	2.3218	1.5237
No log	38.6667	116	2.2177	0.1765	2.2177	1.4892
No log	39.3333	118	2.0767	0.2047	2.0767	1.4411
No log	40.0	120	2.0315	0.1613	2.0315	1.4253
No log	40.6667	122	2.0778	0.1905	2.0778	1.4414
No log	41.3333	124	2.1034	0.1719	2.1034	1.4503
No log	42.0	126	2.0902	0.2016	2.0902	1.4457
No log	42.6667	128	2.0952	0.2016	2.0952	1.4475
No log	43.3333	130	2.1807	0.2105	2.1807	1.4767
No log	44.0	132	2.3171	0.1418	2.3171	1.5222
No log	44.6667	134	2.3237	0.1439	2.3237	1.5244
No log	45.3333	136	2.1662	0.1832	2.1662	1.4718
No log	46.0	138	2.0096	0.1967	2.0096	1.4176
No log	46.6667	140	1.9624	0.1391	1.9624	1.4009
No log	47.3333	142	1.9770	0.2114	1.9770	1.4061
No log	48.0	144	2.0764	0.2016	2.0764	1.4410
No log	48.6667	146	2.1621	0.1940	2.1621	1.4704
No log	49.3333	148	2.2137	0.1752	2.2137	1.4879
No log	50.0	150	2.2626	0.0993	2.2626	1.5042
No log	50.6667	152	2.3094	0.0704	2.3094	1.5197
No log	51.3333	154	2.2395	0.1765	2.2395	1.4965
No log	52.0	156	2.1522	0.1732	2.1522	1.4670
No log	52.6667	158	2.1732	0.0968	2.1732	1.4742
No log	53.3333	160	2.2124	0.0650	2.2124	1.4874
No log	54.0	162	2.2506	-0.0339	2.2506	1.5002
No log	54.6667	164	2.3309	0.0794	2.3309	1.5267
No log	55.3333	166	2.4704	0.1151	2.4704	1.5717
No log	56.0	168	2.6211	0.0	2.6211	1.6190
No log	56.6667	170	2.7003	0.0	2.7003	1.6432
No log	57.3333	172	2.6740	0.0	2.6740	1.6352
No log	58.0	174	2.5550	0.0274	2.5550	1.5984
No log	58.6667	176	2.3663	0.1151	2.3663	1.5383
No log	59.3333	178	2.2110	0.1120	2.2110	1.4869
No log	60.0	180	2.1568	0.1452	2.1568	1.4686
No log	60.6667	182	2.1705	0.1452	2.1705	1.4733
No log	61.3333	184	2.2203	0.1791	2.2203	1.4901
No log	62.0	186	2.2628	0.1618	2.2628	1.5043
No log	62.6667	188	2.3179	0.1449	2.3179	1.5225
No log	63.3333	190	2.4129	0.0414	2.4129	1.5534
No log	64.0	192	2.4081	0.0845	2.4081	1.5518
No log	64.6667	194	2.3578	0.1439	2.3578	1.5355
No log	65.3333	196	2.3050	0.1618	2.3050	1.5182
No log	66.0	198	2.3098	0.1618	2.3098	1.5198
No log	66.6667	200	2.2570	0.1481	2.2570	1.5023
No log	67.3333	202	2.2510	0.1221	2.2510	1.5003
No log	68.0	204	2.2940	0.1221	2.2940	1.5146
No log	68.6667	206	2.2896	0.1240	2.2896	1.5131
No log	69.3333	208	2.2253	0.0820	2.2253	1.4917
No log	70.0	210	2.2172	0.1000	2.2172	1.4890
No log	70.6667	212	2.2502	0.0661	2.2502	1.5001
No log	71.3333	214	2.2631	0.0820	2.2631	1.5044
No log	72.0	216	2.2734	0.0820	2.2734	1.5078
No log	72.6667	218	2.2703	0.0820	2.2703	1.5067
No log	73.3333	220	2.3299	0.0938	2.3299	1.5264
No log	74.0	222	2.4762	0.1007	2.4762	1.5736
No log	74.6667	224	2.6634	0.0135	2.6634	1.6320
No log	75.3333	226	2.7823	0.0	2.7823	1.6680
No log	76.0	228	2.8198	0.0	2.8198	1.6792
No log	76.6667	230	2.7928	0.0	2.7928	1.6712
No log	77.3333	232	2.7102	0.0	2.7102	1.6463
No log	78.0	234	2.6113	0.0414	2.6113	1.6160
No log	78.6667	236	2.5558	0.0556	2.5558	1.5987
No log	79.3333	238	2.5627	0.0556	2.5627	1.6008
No log	80.0	240	2.5658	0.0414	2.5658	1.6018
No log	80.6667	242	2.5362	0.0414	2.5362	1.5925
No log	81.3333	244	2.5124	0.0414	2.5124	1.5850
No log	82.0	246	2.4598	0.0556	2.4598	1.5684
No log	82.6667	248	2.4483	0.0556	2.4483	1.5647
No log	83.3333	250	2.4259	0.0839	2.4259	1.5575
No log	84.0	252	2.3973	0.1127	2.3973	1.5483
No log	84.6667	254	2.3584	0.1583	2.3584	1.5357
No log	85.3333	256	2.3546	0.1594	2.3546	1.5345
No log	86.0	258	2.3396	0.1594	2.3396	1.5296
No log	86.6667	260	2.3392	0.1594	2.3392	1.5294
No log	87.3333	262	2.3547	0.1594	2.3547	1.5345
No log	88.0	264	2.3423	0.1594	2.3423	1.5305
No log	88.6667	266	2.3417	0.1594	2.3417	1.5302
No log	89.3333	268	2.3456	0.1594	2.3456	1.5316
No log	90.0	270	2.3647	0.1594	2.3647	1.5378
No log	90.6667	272	2.3858	0.0839	2.3858	1.5446
No log	91.3333	274	2.3961	0.0839	2.3961	1.5479
No log	92.0	276	2.4040	0.0839	2.4040	1.5505
No log	92.6667	278	2.4068	0.0839	2.4068	1.5514
No log	93.3333	280	2.3856	0.1127	2.3856	1.5445
No log	94.0	282	2.3621	0.1594	2.3621	1.5369
No log	94.6667	284	2.3556	0.1594	2.3556	1.5348
No log	95.3333	286	2.3386	0.1594	2.3386	1.5293
No log	96.0	288	2.3157	0.1471	2.3157	1.5217
No log	96.6667	290	2.3064	0.1471	2.3064	1.5187
No log	97.3333	292	2.3072	0.1471	2.3072	1.5189
No log	98.0	294	2.3107	0.1471	2.3107	1.5201
No log	98.6667	296	2.3120	0.1471	2.3120	1.5205
No log	99.3333	298	2.3118	0.1471	2.3118	1.5204
No log	100.0	300	2.3119	0.1471	2.3119	1.5205

Framework versions

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